Efficient control and adjustment method for power grid energy storage battery

Abstract

The invention discloses an efficient control and adjustment method for power grid energy storage batteries, and relates to the technical field of electric power energy storage, and the method comprises the steps: obtaining a plurality of energy storage batteries in a preset power grid energy storage module; performing battery control consistent classification to generate a plurality of groups of battery control units; carrying out the optimization of the charging and discharging speed and the charging and discharging depth for multiple groups of battery control units, and generating a plurality of bidirectional optimal control parameter sets; when the control center sends a charging instruction, the charging control parameter with the minimum battery attenuation index is extracted to perform charging control on the multiple groups of battery control units; and when the control center sends a discharge instruction, carrying out load balance distribution according to the plurality of bidirectional optimal control parameter sets, and carrying out discharge control on the plurality of groups of battery control units according to a balance distribution result. According to the invention, the technical problem of non-uniform load distribution during charging and discharging of the battery in the prior art is solved, and the technical effects of balanced load distribution for charging and discharging of the battery and improvement of the overall performance of the energy storage system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to a high-efficiency control and regulation method for a power grid energy storage battery. Background Art

[0002] With the widespread application of renewable energy in power systems, grid energy storage batteries, as important equipment for balancing power supply and demand, face multiple technical challenges, which directly affect the efficiency of the system and the service life of the battery. In the existing grid energy storage battery management technology, load distribution is often unbalanced. Some battery packs are subjected to high loads for a long time, resulting in rapid aging and capacity decay, while other battery packs are not fully utilized, resulting in uneven load distribution when charging and discharging the battery. Summary of the invention

[0003] The present application provides a highly efficient control and regulation method for a power grid energy storage battery, which is used to solve the technical problem of uneven load distribution during battery charging and discharging in the prior art.

[0004] In view of the above problems, the present application provides a method for efficiently controlling and regulating a power grid energy storage battery.

[0005] The present application provides a method for efficiently controlling and regulating a power grid energy storage battery, the method comprising: Acquire multiple energy storage batteries in a preset power grid energy storage module; collect historical charge and discharge records of the multiple energy storage batteries to perform battery control consistency classification, and generate multiple groups of battery control units; optimize the charge and discharge speed and charge and discharge depth for the multiple groups of battery control units, and generate multiple bidirectional optimal control parameter sets with battery attenuation indicators less than preset indicators; when the control center of the preset power grid energy storage module issues a charging instruction, extract the charging control parameters with the smallest battery attenuation indicators according to the multiple bidirectional optimal control parameter sets to perform charging control on the multiple groups of battery control units; when the control center of the preset power grid energy storage module issues a discharging instruction, receive the discharge load demand information, perform load balancing distribution according to the multiple bidirectional optimal control parameter sets, and perform discharge control on the multiple groups of battery control units according to the balanced distribution results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application obtains multiple energy storage batteries in a preset power grid energy storage module; collects historical charge and discharge records of multiple energy storage batteries for consistent battery control classification, and generates multiple groups of battery control units; optimizes the charge and discharge speed and charge and discharge depth for multiple groups of battery control units, and generates multiple bidirectional optimal control parameter sets with battery attenuation indicators less than preset indicators; when the control center of the preset power grid energy storage module issues a charging instruction, the charging control parameters with the smallest battery attenuation indicators are extracted according to multiple bidirectional optimal control parameter sets to perform charging control on multiple groups of battery control units; when the control center of the preset power grid energy storage module issues a discharge instruction, it receives discharge load demand information, performs load balancing distribution according to multiple bidirectional optimal control parameter sets, and performs discharge control on multiple groups of battery control units according to the balanced distribution results. The present invention solves the technical problem of uneven load distribution when charging and discharging batteries in the prior art, collects historical charge and discharge records of energy storage batteries, generates multiple groups of battery control units by consistent classification, optimizes the charge and discharge rate and depth for these units, generates a bidirectional optimal control parameter set, and when a charge or discharge instruction is issued, extracts the optimal control parameters, performs load balancing distribution, and achieves balanced load distribution for battery charging and discharging, and improves the technical effect of improving the overall performance of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of a method for efficiently controlling and regulating a power grid energy storage battery provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of generating multiple groups of battery control units in a method for efficiently controlling and regulating a power grid energy storage battery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application provides an efficient control and regulation method for power grid energy storage batteries, which is used to solve the technical problem of uneven load distribution when charging and discharging batteries in the prior art. By collecting historical charging and discharging records of energy storage batteries, multiple groups of battery control units are generated using consistent classification, and the charging and discharging rates and depths of these units are optimized to generate a bidirectional optimal control parameter set. When a charging or discharging instruction is issued, the optimal control parameters are extracted to perform load balancing, thereby achieving balanced load distribution for battery charging and discharging and improving the technical effect of the overall performance of the energy storage system.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0012] like Figure 1 As shown, the present application provides a method for efficiently controlling and regulating a grid energy storage battery, the method comprising: Step S100: Acquire multiple energy storage batteries in a preset power grid energy storage module.

[0013] In the embodiment of the present application, the configuration data is first read from the management system of the energy storage module. The preset power grid energy storage module refers to a group of energy storage devices that have been installed and configured in the power grid, usually used for the storage and dispatch of electricity. By establishing a connection with the communication interface of the energy storage module, verify whether the module is online, and obtain a list of batteries in the energy storage module. In this process, the status information of each battery, such as the charging status, health status, and current battery usage, is obtained through the battery management system.

[0014] Through the above process, information of multiple energy storage batteries in the preset power grid energy storage module is obtained.

[0015] Step S200: collecting historical charge and discharge records of the plurality of energy storage batteries for battery control consistency classification, and generating a plurality of groups of battery control units.

[0016] In the embodiment of the present application, a preliminary classification is first performed according to the models of multiple energy storage batteries. This is a preliminary screening based on the battery specification parameters, such as capacity, rated voltage, rated current, etc., to ensure that the classified batteries are consistent in physical properties and basic working conditions.

[0017] After the model classification is completed, the historical charge and discharge records of these batteries are further collected, including the number of charge and discharge times, charge and discharge rate, charge and discharge depth, SOC, voltage, current, temperature and other data. By analyzing these historical data, the health status of the battery is evaluated, such as the battery capacity decay and power decay. Then, these batteries are classified based on the consistency of health status to ensure that the same group of batteries has consistent operating performance and loss under the same voltage and current conditions. Through this secondary classification, multiple groups of battery control units are generated.

[0018] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the historical charge and discharge records of the plurality of energy storage batteries are collected for battery control consistency classification to generate a plurality of groups of battery control units, and further includes: A multi-layer decision classifier is established, wherein the multi-layer decision classifier includes a first layer classifier and a second layer classifier, and the input layer of the second layer classifier is connected to the output layer of the first layer classifier and the output layer of the battery status analysis network; the plurality of energy storage batteries are classified according to a first classification constraint by the first layer classifier to generate a first classification result; the first classification result is input into the second layer classifier, and the first classification result is reclassified according to a second classification constraint in combination with the battery status analysis network and the historical charge and discharge records of the plurality of energy storage batteries to generate the plurality of battery control units.

[0019] Furthermore, the method provided in the application embodiment also includes: The first classification constraint is a constraint condition for consistent battery specifications, and the second classification constraint is a constraint condition for consistent battery safety states.

[0020] In the embodiment of the present application, a multi-layer decision classifier is first established, which consists of a first-layer classifier and a second-layer classifier to classify energy storage batteries at different stages. In order to ensure the consistency of the battery, the second-layer classifier is not only connected to the output layer of the first-layer classifier, but also to the output layer of the battery status analysis network to ensure that the health status of the battery is considered in the classification process.

[0021] In the first layer of classifiers, the decision tree algorithm is used for classification. The decision tree algorithm classifies the energy storage batteries according to specifications, such as capacity, rated voltage, rated current, etc., by gradually dividing the data set. The battery specification parameters are extracted from the battery management system (BMS), and the batteries are classified into different groups based on these physical properties. The constraint condition for the first layer of classification is that the battery specifications are consistent, that is, batteries with the same specifications are assigned to the same category, generating the first classification result, that is, a group of batteries with the same specifications.

[0022] The first classification result is then input into the second-layer classifier. At the same time, a battery state analysis network is introduced, which processes the historical charge and discharge data collected from the battery management system through a long short-term memory network, including the number of charge / discharge times, SOC (state of charge), voltage, current, temperature, etc. The battery health status is analyzed by the battery state analysis network to obtain the battery health status prediction data, such as capacity decay, power decay, etc. After that, the second-layer classifier uses the K-means clustering algorithm to perform secondary classification on the battery. Based on the obtained battery health status prediction data, the batteries with similar health status are clustered together to ensure that the batteries in the same group have similar decay characteristics under the same voltage and current conditions. The second classification constraint is that the battery safety status is consistent, that is, after K-means clustering, the safety status of the same group of batteries, such as health status and decay performance, is similar.

[0023] Through the above two levels of classification, multiple battery control units are generated. The batteries in these units are not only the same in physical specifications, but also in health status.

[0024] Furthermore, in the method provided in the embodiment of the application, the first classification result is input into the second layer classifier, and the first classification result is reclassified according to the second classification constraint in combination with the battery state analysis network and the historical charge and discharge records of the plurality of energy storage batteries to generate the plurality of battery control units, and further includes: The historical charge and discharge records include at least charge and discharge temperature, voltage, current, number of charge and discharge cycles and SOC value; the battery safety status under the same charge and discharge parameters is predicted according to the historical charge and discharge records by the battery status analysis network, and a plurality of predicted battery decay states corresponding to the plurality of energy storage batteries are generated; the first classification result and the plurality of predicted battery decay states are input into the second layer classifier, and the first classification result is reclassified according to the second classification constraint to generate the plurality of battery control units.

[0025] In an embodiment of the present application, the historical charge and discharge records of each energy storage battery are first obtained through the battery management system, and the records include at least the charge and discharge temperature, voltage, current, number of charge and discharge cycles and SOC value. Next, the battery state analysis network is used to process the historical charge and discharge records. Specifically, the battery state analysis network processes the above historical charge and discharge data through a long short-term memory network. Due to its ability to process time series data, the LSTM network can learn and extract patterns from the historical operating data of the battery, thereby predicting the future state of the battery. Through the training of the LSTM model, the safety state of the battery is predicted under the same charge and discharge parameters, including capacity decay and power decay. Finally, the predicted battery decay state corresponding to multiple energy storage batteries is generated.

[0026] After obtaining the battery health status prediction result, the first classification result and the predicted battery attenuation state are input into the second layer classifier together. Then the K-means clustering algorithm is used in the second layer classifier to further classify the battery. The K-means clustering algorithm groups batteries with similar attenuation characteristics based on the predicted battery attenuation state, that is, the health state. By repeatedly calculating the distance between the attenuation index of each battery and the cluster center, the K-means algorithm clusters batteries with similar health states together. The second classification constraint in this step is that the battery safety state is consistent, that is, the batteries in the same group show similar attenuation trends under the same voltage and current conditions. Through these two layers of classification, multiple battery control units are finally generated.

[0027] Furthermore, in the method provided in the embodiment of the application, the battery safety status prediction under the same charging and discharging parameters is performed according to the historical charging and discharging records by the battery status analysis network, and further includes: The battery structure information of the multiple energy storage batteries is obtained, and digital twin simulation is performed in combination with the historical charge and discharge records, multiple battery twins are established and integrated and connected, and the battery status analysis network is generated; the multiple battery twins are respectively simulated according to multiple groups of charge and discharge parameters, and the multiple predicted battery attenuation states are read, wherein any predicted battery attenuation state includes at least a predicted battery capacity attenuation index and a predicted battery power attenuation index.

[0028] In an embodiment of the present application, the structural information of multiple energy storage batteries is first obtained through a battery management system. These structural information include the physical properties of the battery, such as battery material type, capacity, internal resistance, size, electrode configuration, etc. After obtaining the structural information of the battery, these structural data are combined with the historical charge and discharge records of the battery, and simulation modeling is performed using digital twin technology. During the digital twin simulation process, a corresponding battery twin is generated for each battery, that is, a virtual model of the battery. These battery twins are based on structural information and charge and discharge records, and can synchronously simulate the operation of actual batteries in a virtual environment. Afterwards, the generated multiple battery twins are integrated and connected through a network to form a battery status analysis network.

[0029] Next, through the integrated battery state analysis network, each battery twin is simulated under multiple sets of charge and discharge parameters. Specifically, different charge and discharge parameters such as charging rate, discharge depth, and charging current are set to simulate the battery in multiple dimensions. During the simulation process, the operating status of each battery twin is monitored in real time, especially the changes in its capacity and power. In these simulation runs, the predicted battery decay state of each battery is generated and read. Among them, the predicted battery capacity decay index refers to the possible downward trend of the battery's energy storage capacity after multiple charge and discharge cycles; the predicted battery power decay index reflects the trend that the battery's output power gradually decreases with the increase of usage time.

[0030] Step S300: optimizing the charging and discharging speed and the charging and discharging depth for the multiple groups of battery control units, and generating multiple bidirectional optimal control parameter sets whose battery attenuation indicators are less than preset indicators.

[0031] In the embodiment of the present application, the predicted battery attenuation state of each group of batteries is first extracted, including the capacity attenuation index and the power attenuation index. Then, these predicted attenuation states are compared with the preset attenuation thresholds to ensure that the capacity and power attenuation of the battery are lower than the thresholds during the charging and discharging process. The parameter combinations of the charging and discharging speed and the charging and discharging depth that meet the requirements are analyzed and screened out. Finally, the charging and discharging parameter combinations that meet the requirements are combined to generate multiple bidirectional optimal control parameter sets.

[0032] Furthermore, in the method provided in the embodiment of the application, optimizing the charging and discharging speed and charging and discharging depth for the multiple groups of battery control units, generating multiple bidirectional optimal control parameter sets whose battery attenuation index is less than the preset index, also includes: According to the multiple predicted battery attenuation states, a charging and discharging parameter combination in which the predicted battery capacity attenuation index and the predicted battery power attenuation index are both less than the preset index is obtained for the multiple battery control units respectively; and the multiple bidirectional optimal control parameter sets are generated by the charging and discharging parameter combination.

[0033] In the embodiment of the present application, the predicted battery decay state of each group of battery control units is first extracted from the battery state analysis network. Since the batteries in each group have consistent specifications and states, only the predicted state of one battery needs to be extracted from them. These predicted states include predicted battery capacity decay indicators and predicted battery power decay indicators.

[0034] Next, the predicted battery capacity attenuation index and predicted battery power attenuation index obtained by multiple battery control units are compared with the preset indexes, and the charge and discharge parameter combinations that make the predicted battery capacity attenuation and power attenuation less than the preset threshold are screened out through calculation and comparison. These parameter combinations include different charging rates, discharging rates, and charge and discharge depths.

[0035] Finally, the charging and discharging parameter combinations that meet the requirements are used as multiple bidirectional optimal control parameter sets corresponding to multiple groups of battery control units.

[0036] Step S400: When the control center of the preset grid energy storage module issues a charging instruction, the charging control parameters with the smallest battery attenuation index are extracted according to the multiple bidirectional optimal control parameter sets to control charging of the multiple battery control units.

[0037] In an embodiment of the present application, when the control center of the grid energy storage module issues a charging instruction, the charging process is started. After receiving the charging instruction, the corresponding charging control parameters are extracted from the previously generated bidirectional optimal control parameter set. The bidirectional optimal control parameter set contains the optimal parameter combination that can be used by each group of battery control units during charging and discharging, covering different charging rates and charging depths. Then, the charging control parameters that minimize the battery attenuation index are selected to control the charging of multiple groups of battery control units.

[0038] Step S500: When the control center of the preset grid energy storage module issues a discharge instruction, discharge load demand information is received, load balancing is performed according to the multiple bidirectional optimal control parameter sets, and discharge control is performed on the multiple battery control units according to the balancing distribution result.

[0039] In an embodiment of the present application, when the control center of the preset power grid energy storage module issues a discharge command, the discharge load demand information is first received, which includes the discharge current demand and the power demand, indicating the specific power output and current supply required by the current power grid. Thereafter, the discharge process is started according to the discharge load demand information, and the corresponding discharge control parameters are extracted from the previously generated bidirectional optimal control parameter set to ensure that the discharge process can minimize battery attenuation. Specifically, multiple groups of discharge control parameters corresponding to the minimum battery attenuation index are first extracted from multiple bidirectional optimal control parameter sets. Among them, the battery attenuation index is calculated by weighted fusion of the predicted battery capacity attenuation index and the predicted battery power attenuation index.

[0040] Next, multiple groups of battery control units are prioritized according to the battery attenuation index. In this process, the priority is inversely proportional to the battery attenuation index. The smaller the battery attenuation index, the higher the priority. Finally, discharge control is performed on multiple groups of battery control units according to the selected multiple groups of discharge control parameters and the results of priority configuration.

[0041] Furthermore, in the method provided in the embodiment of the application, load balancing distribution is performed according to the multiple bidirectional optimal control parameter sets, and discharge control is performed on the multiple groups of battery control units according to the balancing distribution results, and further includes: Extract multiple groups of discharge control parameters corresponding to the minimum battery attenuation index from the multiple bidirectional optimal control parameter sets; wherein the battery attenuation index is obtained by weighted fusion calculation of the predicted battery capacity attenuation index and the predicted battery power attenuation index; prioritize the multiple groups of battery control units according to the battery attenuation index, wherein the priority is inversely proportional to the battery attenuation index; and distribute and control the discharge load demand information according to the multiple groups of discharge control parameters and the priority configuration results.

[0042] In the embodiment of the present application, firstly, multiple groups of discharge control parameters corresponding to the minimum battery attenuation index are extracted from the multiple bidirectional optimal control parameter sets that have been obtained. The battery attenuation index is obtained by weighted fusion calculation of the predicted battery capacity attenuation index and the predicted battery power attenuation index, and the weights of the predicted battery capacity attenuation index and the predicted battery power attenuation index are preset by technical experts.

[0043] Next, multiple groups of battery control units are prioritized according to the battery attenuation index, where the priority is inversely proportional to the battery attenuation index, that is, battery groups with smaller attenuation indexes have higher priorities, and these batteries in better health will take on more discharge tasks first. For battery groups with larger capacity and power attenuation, their loads are reduced by reducing their discharge current or shortening their discharge time to avoid excessive use of these battery groups and delay further attenuation. Through priority configuration, the load distribution of different battery groups is balanced to ensure the efficiency of the overall battery system and the extension of its service life.

[0044] After completing the priority configuration, the discharge load distribution and control begins according to the results of multiple groups of discharge control parameters and priority configuration. According to the discharge load demand information of the power grid, including current demand and power demand, the load is reasonably distributed to the battery groups with higher priority, so that the battery groups with better health status bear more loads, while the battery groups with more serious attenuation bear lighter loads to avoid further loss. During the discharge process, the status of each battery group, including voltage, current, temperature and SOC, is monitored in real time, and dynamic adjustments are made according to the actual working status of the battery to ensure safety and efficiency during the discharge process.

[0045] Furthermore, the method provided in the application embodiment also includes: The discharge load demand information is fitted with discharge load distribution and control according to multiple groups of discharge control parameters and priority configuration results to generate a fitting result; whether the discharge load demand information is met is determined according to the fitting result; if not, based on the priority configuration result, in descending order of priority, the discharge control parameters corresponding to the attenuation index adjacent to the minimum battery attenuation index are extracted from the multiple bidirectional optimal control parameter sets, and the discharge load distribution and control are refitted until the discharge load demand information is met.

[0046] In an embodiment of the present application, discharge load demand information is first received, including current demand and power demand. For example, a power output of 100kW and a corresponding current value may be required. After receiving the demand, the control parameters that best match the demand are extracted from the previously generated bidirectional optimal control parameter set, which include discharge rate and discharge depth. Then, a preliminary load distribution is performed on each battery pack through a linear programming algorithm. According to the priority of the battery pack, battery packs in better health are allocated more loads, such as 60% of the total power demand, while battery packs in slightly worse health bear 40% of the load.

[0047] Then, based on the results of the preliminary load distribution, a fitting result is generated. The fitting result determines whether the current load distribution meets the current and power requirements of the power grid by comparing the thresholds. The actual allocated current and power are compared with the requirements of the power grid to determine whether they are within the required range. If the discharge load demand information is not met, the load is adjusted according to the priority sorting algorithm of the battery pack. Through priority sorting, battery packs with better health are assigned higher priorities. At this time, according to the power gap, the load is first increased from the battery packs with higher priorities. For example, the load of the battery pack with higher priority is increased from 60% to 70% to make up for the power shortage.

[0048] If the grid demand is still not met after the priority adjustment, the neighboring search algorithm is used to extract the discharge control parameters adjacent to the current control parameters from the bidirectional optimal control parameter set. Through this fine-tuning step, the discharge rate and depth are slightly adjusted to increase the power output. For example, the discharge rate of a healthy battery pack is slightly increased while ensuring that the depth of discharge does not cause excessive battery attenuation. If the demand is still not fully met after the above adjustments, the iterative optimization algorithm is started and multiple load adjustments are performed. In each iteration, the load distribution is gradually optimized based on the actual state of the battery pack. After each iteration, a new fitting result is generated and an evaluation is made as to whether the current load distribution meets the demand. If the fitting result shows that the load distribution still does not meet the power and current requirements of the grid, adjustments will continue until the demand is finally met. Through multiple iterations and load adjustments, the discharge load demand information of the grid can eventually be met.

[0049] Furthermore, the method provided in the application embodiment also includes: When the discharge load of any battery control unit in two consecutive discharge tasks is higher than the average load of multiple groups of battery control units, the priority configuration result is optimized according to a preset priority adjustment step.

[0050] In an embodiment of the present application, the discharge load of each battery control unit is calculated, including current and power output. The load of each battery group in two consecutive discharge tasks is compared with the average load of multiple battery control units. When the load of a battery control unit is continuously higher than the average load of other battery groups, the battery group is considered to be overloaded. The priority of the battery group is adjusted according to the preset priority adjustment step. The preset priority adjustment step is pre-set by technical experts. After detecting that the load of a battery group is too high and confirming that adjustment is required, the priority of the battery group is lowered according to the preset priority adjustment step, thereby reducing the load of the battery group in the next discharge task.

[0051] Through this adjustment, excessive use of high-load battery packs can be avoided, thereby slowing down their degradation rate, and priority adjustment can be used to ensure that other battery packs bear reasonable loads and maintain a healthy balance between battery packs.

[0052] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application obtains multiple energy storage batteries in a preset power grid energy storage module; collects historical charge and discharge records of multiple energy storage batteries for consistent battery control classification, and generates multiple groups of battery control units; optimizes the charge and discharge speed and charge and discharge depth for multiple groups of battery control units, and generates multiple bidirectional optimal control parameter sets with battery attenuation indicators less than preset indicators; when the control center of the preset power grid energy storage module issues a charging instruction, the charging control parameters with the smallest battery attenuation indicators are extracted according to multiple bidirectional optimal control parameter sets to perform charging control on multiple groups of battery control units; when the control center of the preset power grid energy storage module issues a discharge instruction, it receives discharge load demand information, performs load balancing distribution according to multiple bidirectional optimal control parameter sets, and performs discharge control on multiple groups of battery control units according to the balanced distribution results. The present invention solves the technical problem of uneven load distribution when charging and discharging batteries in the prior art, collects historical charge and discharge records of energy storage batteries, generates multiple groups of battery control units by consistent classification, optimizes the charge and discharge rate and depth for these units, generates a bidirectional optimal control parameter set, and when a charge or discharge instruction is issued, extracts the optimal control parameters, performs load balancing distribution, and achieves balanced load distribution for battery charging and discharging, and improves the technical effect of improving the overall performance of the energy storage system.

[0053] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0055] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A high-efficiency control and regulation method for a power grid energy storage battery, characterized in that: include: Acquire multiple energy storage batteries in a preset power grid energy storage module; Collecting historical charge and discharge records of the plurality of energy storage batteries for battery control consistency classification, and generating a plurality of groups of battery control units; Optimizing the charging and discharging speed and the charging and discharging depth for the multiple groups of battery control units, and generating multiple bidirectional optimal control parameter sets whose battery attenuation indicators are less than preset indicators; When the control center of the preset grid energy storage module issues a charging instruction, the charging control parameters with the smallest battery attenuation index are extracted according to the multiple bidirectional optimal control parameter sets to control the charging of the multiple groups of battery control units; When the control center of the preset power grid energy storage module issues a discharge instruction, it receives discharge load demand information, performs load balancing distribution according to the multiple bidirectional optimal control parameter sets, and performs discharge control on the multiple groups of battery control units according to the balancing distribution results.

2. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 1, characterized in that: Collect the historical charge and discharge records of the multiple energy storage batteries to perform battery control consistency classification, and generate multiple groups of battery control units, including: Establishing a multi-layer decision classifier, wherein the multi-layer decision classifier includes a first layer classifier and a second layer classifier, and the input layer of the second layer classifier is connected to the output layer of the first layer classifier and the output layer of the battery state analysis network; Classifying the plurality of energy storage batteries according to the first classification constraint through the first layer classifier to generate a first classification result; The first classification result is input into the second layer classifier, and the first classification result is reclassified according to the second classification constraint in combination with the battery status analysis network and the historical charge and discharge records of the plurality of energy storage batteries to generate the plurality of battery control units.

3. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 2, characterized in that: The first classification constraint is a constraint condition for consistent battery specifications, and the second classification constraint is a constraint condition for consistent battery safety states.

4. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 3, characterized in that: Inputting the first classification result into the second layer classifier, combining the battery state analysis network and the historical charge and discharge records of the plurality of energy storage batteries to classify the first classification result again according to the second classification constraint, and generating the plurality of battery control units, including: The historical charge and discharge records include at least charge and discharge temperature, voltage, current, charge and discharge cycle number and SOC value; The battery state analysis network predicts the battery safety state under the same charge and discharge parameters according to the historical charge and discharge records, and generates a plurality of predicted battery attenuation states corresponding to the plurality of energy storage batteries; The first classification result and the multiple predicted battery attenuation states are input into the second layer classifier, and the first classification result is reclassified according to the second classification constraint to generate the multiple groups of battery control units.

5. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 4, characterized in that: The battery state analysis network predicts the battery safety state under the same charge and discharge parameters according to the historical charge and discharge records, including: Obtaining battery structure information of the multiple energy storage batteries, and performing digital twin simulation in combination with the historical charge and discharge records, establishing multiple battery twins and integrating and connecting them, and generating the battery status analysis network; The multiple battery twins are simulated and run according to multiple groups of charge and discharge parameters respectively, and the multiple predicted battery decay states are read, wherein any predicted battery decay state at least includes a predicted battery capacity decay index and a predicted battery power decay index.

6. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 5, characterized in that: Optimizing the charging and discharging speed and charging and discharging depth for the multiple groups of battery control units, generating multiple bidirectional optimal control parameter sets whose battery attenuation index is less than a preset index, including: According to the multiple predicted battery attenuation states, respectively obtaining, for the multiple groups of battery control units, a charge and discharge parameter combination in which both the predicted battery capacity attenuation index and the predicted battery power attenuation index are less than a preset index; The plurality of bidirectional optimal control parameter sets are generated by combining the charge and discharge parameters.

7. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 1, characterized in that: Load balancing is performed according to the plurality of bidirectional optimal control parameter sets, and discharge control is performed on the plurality of battery control units according to the balancing distribution result, including: Extracting multiple groups of discharge control parameters corresponding to minimum battery attenuation indicators from the multiple bidirectional optimal control parameter sets; The battery attenuation index is obtained by weighted fusion calculation of the predicted battery capacity attenuation index and the predicted battery power attenuation index; Prioritizing the multiple groups of battery control units according to the battery attenuation index, wherein the priority is inversely proportional to the battery attenuation index; The discharge load demand information is used to distribute and control the discharge load according to the multiple groups of discharge control parameters and priority configuration results.

8. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 7, characterized in that: Also includes: Fitting the discharge load demand information with the discharge load distribution and control according to the multiple groups of discharge control parameters and priority configuration results to generate a fitting result; Determining whether the discharge load requirement information is met according to the fitting result; If not, based on the priority configuration result, in descending order of priority, the discharge control parameters corresponding to the attenuation indicators adjacent to the minimum battery attenuation indicator are extracted from the multiple bidirectional optimal control parameter sets, and the discharge load distribution and control are re-fitted until the discharge load demand information is met.

9. A high-efficiency control and regulation method for a power grid energy storage battery as claimed in claim 7, characterized in that: Also includes: When the discharge load of any battery control unit in two consecutive discharge tasks is higher than the average load of multiple groups of battery control units, the priority configuration result is optimized according to a preset priority adjustment step.

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