Battery matching method, device and energy storage power plant

Abstract

The application is suitable for the field of energy storage technology, and provides a battery matching method and device and an energy storage power plant. The method comprises the following steps: determining a matching direction of a retired battery according to state information of the retired battery and an application scenario matching model; the application scenario matching model is constructed according to a power grid regulation scenario; and in the case that the matching direction of the retired battery meets application requirements, determining an assembly strategy of the retired battery according to the matching direction of the retired battery and energy storage parameters of the energy storage power plant. The retired battery can be applied to the power grid regulation scenario suitable for the retired battery, and the energy storage core meeting various power grid regulation scenarios can be constructed based on the retired battery.

Description

Technical Field

[0001] This application belongs to the field of energy storage technology, and in particular relates to a battery selection method, device and energy storage power plant. Background Technology

[0002] With the continuous advancement of new energy technologies, new energy vehicles are increasingly being used in all aspects of life. This results in a large number of retired batteries being generated.

[0003] To improve energy utilization and protect the environment, retired batteries can be used in the power system to build energy storage systems. Currently, most energy storage applications are only for functional compensation of the power grid, such as frequency regulation and peak shaving.

[0004] How to utilize retired batteries to build energy storage power plants that can meet the needs of grid regulation is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a battery selection method, device, and energy storage power plant, which can apply retired batteries to the grid regulation scenarios applicable to the retired batteries, and can build an energy storage core that meets various grid regulation scenarios based on retired batteries.

[0006] This application provides a battery selection method for energy storage plants, the method comprising:

[0007] Based on the status information of the retired batteries and the application scenario selection model, the selection direction of the retired batteries is determined; the application scenario selection model is constructed based on the power grid regulation scenario.

[0008] If the selection direction of the retired battery meets the application requirements, the assembly strategy of the retired battery is determined according to the selection direction of the retired battery and the energy storage parameters of the energy storage plant; the assembly strategy is used to indicate the installation information of the retired battery in the energy storage core of the energy storage plant.

[0009] In this application, the suitable grid regulation scenario for a retired battery can be determined based on its battery status. Then, based on the energy storage parameters of the energy storage core of the energy storage plant and the selection direction of the retired battery, the installation strategy of the retired battery can be determined, and the battery can be assembled based on the installation strategy. This allows the retired battery to be used more rationally, that is, it can be applied to the grid regulation scenario to which the retired battery is applicable, thus realizing the need to build an energy storage core that meets various grid regulation scenarios based on the retired battery.

[0010] In one possible implementation of the first aspect, the application scenario selection model includes demand matching calculation models corresponding to different power grid regulation scenarios; the matching model based on the state information and application type of retired batteries includes:

[0011] Obtain the status information of the retired battery;

[0012] The status information of the retired battery is input into the demand matching calculation model corresponding to each power grid regulation scenario to calculate the matching degree between the retired battery and each power grid regulation scenario;

[0013] The application scenario selection model determines the selection direction of the retired battery based on the matching degree between the retired battery and each of the power grid regulation scenarios.

[0014] In this application, a matching calculation model is constructed based on different power grid regulation scenarios, which can more accurately determine the matching degree between retired batteries and various power grid regulation scenarios, thereby improving the accuracy of the selection direction of retired batteries.

[0015] In one possible implementation of the first aspect, the energy storage core of the energy storage plant includes multiple energy storage zones, each corresponding to a different grid function. When the selection direction of the retired batteries meets the application requirements, the assembly strategy for the retired batteries is determined based on the selection direction of the retired batteries and the energy storage parameters of the energy storage plant, including:

[0016] The corresponding energy storage zones are determined based on the selection and matching direction of the retired batteries;

[0017] Obtain the energy storage parameters of the energy storage zone;

[0018] The target battery is determined based on the energy storage parameters of the energy storage zone;

[0019] The assembly strategy for the retired battery is determined based on the battery information of the target battery.

[0020] In this application, the energy storage core may include different energy storage zones. When performing grid regulation functions, the corresponding energy storage zone can be called to perform regulation, which can achieve efficient and reliable regulation functions. Furthermore, the assembly strategy of retired batteries can be determined based on the energy storage parameters of the energy storage zone corresponding to the selected direction, making the assembly strategy of retired batteries more in line with actual application needs.

[0021] In one possible implementation of the first aspect, the assembly module includes:

[0022] The step of determining the target battery based on the energy storage parameters of the energy storage zone includes:

[0023] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0024] The retired battery with the lowest performance score is determined based on the battery parameters of each installed retired battery, and the retired battery with the lowest performance score is determined as the target battery.

[0025] In one possible implementation of the first aspect, determining the target battery based on the energy storage parameters of the energy storage zone includes:

[0026] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0027] Calculate the similarity between the retired battery and each of the installed retired batteries;

[0028] The installed retired battery with the highest similarity to the retired battery is identified as the target battery.

[0029] In one possible implementation of the first aspect, determining the target battery based on the energy storage parameters of the energy storage zone includes:

[0030] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0031] Based on the battery parameters of each installed retired battery, the installed retired battery with the shortest remaining cycle life is determined, and the retired battery with the shortest remaining cycle life is determined as the target battery.

[0032] In one possible implementation of the first aspect, the battery selection method further includes:

[0033] Based on the energy storage parameters of the retired battery and the energy storage zone, the simulated operation of the energy storage core is simulated after the retired battery is installed in the energy storage zone.

[0034] If the simulated operating conditions of the energy storage core do not meet the operational requirements, the installation strategy for the retired battery will be redefined.

[0035] In this application, by simulating the operation of the energy storage core after replacing the retired batteries, the accuracy of the retired battery installation location can be effectively verified. If the operation requirements are not met, the installation strategy of the retired batteries can be redefined to further improve the effectiveness of the energy storage core operation.

[0036] In one possible implementation of the first aspect, the battery information of the target battery includes the installation location of the target battery and the remaining cycle life of the target battery, and determining the assembly strategy of the retired battery based on the battery information of the target battery includes:

[0037] Obtain the historical operating data of the energy storage power plant;

[0038] The power input-output model of the energy storage power plant is determined based on its historical operating data.

[0039] The operating parameters of the target battery are estimated based on the power input-output model of the energy storage plant.

[0040] Based on the remaining cycle life and usage parameters of the target battery, predict the disassembly time of the target battery;

[0041] The installation location of the target battery is defined as the target installation location, and the removal time of the target battery is defined as the target installation time.

[0042] Secondly, a battery selection device is provided, comprising:

[0043] The matching direction determination module is used to determine the matching direction of the retired battery based on the status information of the retired battery and the matching model of the application scenario; the matching model of the application scenario is constructed based on the power grid regulation scenario.

[0044] The assembly strategy determination module is used to determine the assembly strategy of the retired battery based on the selection direction of the retired battery and the energy storage parameters of the energy storage plant, provided that the selection direction of the retired battery meets the application requirements; the assembly strategy is used to indicate the installation information of the retired battery in the energy storage core of the energy storage plant.

[0045] Thirdly, an energy storage power plant is provided, which includes a battery matching device as described in the second aspect, or includes electronic equipment performing a battery matching method as described in the first aspect and various implementations thereof.

[0046] Fourthly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any of the first aspects above.

[0047] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0048] Sixthly, embodiments of this application provide a chip system including a processor coupled to a memory. The processor executes a computer program stored in the memory to implement the methods described in any of the first aspects above. The chip system may be a single chip or a chip module composed of multiple chips.

[0049] In a seventh aspect, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the methods described in any of the first aspects.

[0050] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram illustrating the implementation process of a battery selection method provided in an embodiment of this application;

[0053] Figure 2 This is an application schematic diagram of an application scenario selection model provided in an embodiment of this application;

[0054] Figure 3 This is an application diagram illustrating another application scenario selection model provided in this application embodiment;

[0055] Figure 4 This is a schematic diagram of the zoning of the energy storage core in an energy storage power plant provided in the embodiments of this application;

[0056] Figure 5 This is a schematic diagram illustrating the implementation process of another battery selection method provided in this application embodiment;

[0057] Figure 6 This is a schematic diagram of the structure of a battery selection device provided in an embodiment of this application;

[0058] Figure 7 This is a schematic diagram of the structure of an energy storage power plant provided in an embodiment of this application;

[0059] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0061] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0062] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0063] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0064] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0065] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0066] As new energy sources are increasingly used in all aspects of life, a large number of retired batteries have been generated. These retired batteries are those whose performance has deteriorated and can no longer meet the power supply requirements of new energy devices. Recycling and reusing these retired batteries can improve energy efficiency and reduce environmental pollution.

[0067] Retired batteries are mostly used in power systems to provide energy storage for power plants. Energy storage refers to the process of storing energy through a medium or device and releasing it when needed. In other words, energy storage systems can be built using retired batteries to achieve power grid regulation functions such as power supply security, frequency regulation, grid smoothing, peak shaving and valley filling.

[0068] Since different power grid regulation scenarios require different response capabilities, such as the response capability of the energy storage grid required for secondary frequency regulation of the power grid and the response capability of the energy storage grid required for peak shaving of the power grid, how to build an energy storage core that meets the requirements of various power grid regulation scenarios based on retired batteries is a technical problem that urgently needs to be solved.

[0069] Based on this, embodiments of this application provide a battery selection method, apparatus, and energy storage plant. This method can determine the suitable grid regulation scenario for a retired battery based on its battery state. Then, based on the energy storage parameters of the energy storage core of the energy storage plant and the selection direction of the retired battery, an installation strategy for the retired battery is determined. The battery is then assembled based on this installation strategy, thereby enabling the retired battery to be used more rationally. Specifically, the retired battery can be applied to the grid regulation scenario to which it is applicable, thus fulfilling the need to construct an energy storage core that meets various grid regulation scenarios based on the retired battery.

[0070] It should be noted that the energy storage core mentioned in the embodiments of this application refers to the equipment used by the energy storage power plant to provide or store electrical energy. It is an energy storage device composed of retired batteries. That is, the energy storage power plant uses the retired batteries in the energy storage core to realize the grid regulation function.

[0071] The battery selection method provided in this application can be applied to electronic devices such as servers, laptops, and ultra-mobile personal computers (UMPCs) that can determine battery handling strategies. This application does not impose any limitations on the specific type of electronic device.

[0072] The following description, in conjunction with the accompanying drawings, illustrates the battery selection method, apparatus, and energy storage power plant provided in the embodiments of this application:

[0073] Please see Figure 1 , Figure 1 A schematic diagram illustrating the implementation flow of a battery selection method provided in an embodiment of this application is given. For example... Figure 1As shown, the above battery selection method may include the following steps:

[0074] S11: Determine the selection direction of retired batteries based on the status information of retired batteries and the selection model for application scenarios.

[0075] In practical applications, the above-mentioned application scenario selection model can be constructed according to the power grid regulation scenario.

[0076] Among them, power grid regulation scenarios include, but are not limited to: power grid secondary frequency regulation scenarios and power grid peak shaving scenarios, etc.

[0077] In practical applications, different grid regulation scenarios require energy storage power plants to have different response capabilities.

[0078] In the context of grid peak shaving, the need to adjust grid output through energy storage systems to maintain grid power balance based on changes in electricity load is highlighted. It's understood that grid load is uneven; during peak periods, increased load allows retired batteries in energy storage systems to provide power to the grid. Conversely, during off-peak periods, retired batteries in energy storage systems absorb excess energy, thus maintaining grid power balance.

[0079] Specifically, peak shaving usually refers to adjusting power output within a 4-8 hour range, requiring energy storage systems to have large storage capacity and long response time.

[0080] Secondary frequency regulation in power grids refers to the need to utilize energy storage systems to achieve stable control of power grid frequency. Specifically, it refers to situations where changes in the load of the power system cause a shift in the power grid frequency, and traditional frequency regulation methods cannot respond immediately. Instead, energy storage devices in the energy storage system can quickly adjust power output to adjust the supply and demand balance in real time, thereby maintaining a stable power grid frequency.

[0081] Specifically, frequency regulation typically involves adjusting power output within a 1-hour range, requiring energy storage systems to have a fast response capability. Compared to grid peak shaving scenarios, grid secondary frequency regulation scenarios have lower requirements for storage capacity.

[0082] In some embodiments, the above-mentioned power grid regulation scenario types may specifically include power grid secondary frequency regulation scenarios, peak shaving demand, and valley filling demand.

[0083] In practical applications, since different grid regulation scenarios have different requirements for energy storage devices, this application embodiment constructs an application scenario matching model that can classify the matching direction of retired batteries based on the response capability required by the energy storage devices in the energy storage power plant for the grid regulation scenario.

[0084] Here, the selection direction of the retired batteries mentioned above specifically refers to the application scenarios that the retired batteries are suitable for. Specifically, the selection direction of the retired batteries may include, but is not limited to: retired batteries are suitable for grid frequency regulation scenarios, retired batteries are suitable for grid peak shaving scenarios, retired batteries do not meet the requirements of grid frequency regulation scenarios (specifically, grid secondary frequency regulation scenarios) and grid peak shaving scenarios, retired batteries meet both grid frequency regulation scenarios and grid peak shaving scenarios, retired batteries do not meet the requirements of energy storage reuse (can be recycled and dismantled for reuse), etc.

[0085] In some implementations, a matching degree calculation model is constructed based on different power grid regulation scenarios and corresponding to each power grid regulation scenario. That is, in this embodiment of the application scenario, the above-mentioned application scenario selection model includes a demand matching degree calculation model corresponding to different power grid regulation scenarios. The state information of the retired battery is input into the demand matching degree calculation model corresponding to each power grid regulation scenario, and the matching degree between the retired battery and each power grid regulation scenario is calculated. After obtaining the matching degree between the retired battery and each power grid regulation scenario, the application scenario selection model can determine the selection direction of the retired battery based on the matching degree between the retired battery and each power grid regulation scenario.

[0086] Specifically, after determining the matching degree between the retired battery and each grid regulation scenario, the above application scenario selection model can determine the maximum value of the matching degree between the retired battery and each grid regulation scenario (called the maximum matching degree), and then determine the grid regulation scenario corresponding to the maximum matching degree as the selection direction of the retired battery.

[0087] In another implementation, after determining the matching degree between the retired battery and each grid regulation scenario, the above application scenario matching model can determine the maximum value (called the maximum matching degree) among the matching degrees between the retired battery and each grid regulation scenario. Then, it is determined whether the maximum matching degree exceeds the matching degree threshold. If the maximum matching degree is greater than or equal to the matching degree threshold, the grid regulation scenario corresponding to the maximum matching degree is determined as the matching direction of the retired battery.

[0088] In another implementation, after determining the matching degree between the retired battery and each grid regulation scenario, the above application scenario matching model can identify the grid regulation scenarios corresponding to matching degrees greater than the matching degree threshold. If the number of identified grid regulation scenarios is greater than 1, the grid regulation scenario corresponding to the maximum matching degree is determined as the matching direction for the retired battery. If the number of identified grid regulation scenarios is equal to 1, the grid regulation scenario is determined as the matching direction for the retired battery. If the number of identified grid regulation scenarios is less than 1 (i.e., equal to 0), the matching direction for the retired battery is determined to be inconsistent with the energy storage reuse requirements.

[0089] For example, such as Figure 2 As shown, the application scenario selection model is assumed to include a first matching degree calculation model and a second matching degree calculation model. The first matching degree calculation model calculates the matching degree between the retired battery and the grid secondary frequency regulation scenario, while the second matching degree calculation model calculates the matching degree between the retired battery and the grid peak shaving scenario. The state information of the retired battery is input into the first matching degree calculation model to obtain the first matching degree A, and the state information is input into the second matching degree calculation model to obtain the second matching degree B. Then, the selection direction of the retired battery is determined based on the first matching degree A and the second matching degree B.

[0090] In practical applications, the application scenario selection model can compare the first matching degree A and the second matching degree B to determine the larger value between the first matching degree A and the second matching degree B. Let's assume that the larger value is the second matching degree B. Then, the maximum matching degree can be determined as the second matching degree B. Therefore, the grid regulation scenario corresponding to the second matching degree B can be determined as the selection direction of the retired battery, that is, the grid peak shaving scenario can be determined as the selection direction of the retired battery.

[0091] In other embodiments, after determining the maximum matching degree as the second matching degree B, the application scenario selection model can compare the second matching degree B with the matching degree threshold S. If the second matching degree B is greater than or equal to the matching degree threshold S, then the grid regulation scenario corresponding to the second matching degree B can be determined as the selection direction for the retired battery, i.e., the grid peak shaving scenario can be determined as the selection direction for the retired battery. If the second matching degree B is less than the matching degree threshold S, then the selection direction for the retired battery can be determined as not meeting the requirements for energy storage reuse.

[0092] In other embodiments, after determining the first matching degree A and the second matching degree B, the application scenario selection model compares the first matching degree A and the second matching degree B with a matching degree threshold S, respectively, to determine the matching degree that is greater than or equal to the matching degree threshold S. Assuming that both the first matching degree A and the second matching degree B are greater than or equal to the matching degree threshold S, the magnitudes of the first matching degree A and the second matching degree B can be further compared, and the larger value between the first matching degree A and the second matching degree B can be determined as the maximum matching degree. Assuming that the first matching degree A is greater than the second matching degree B, the grid regulation demand corresponding to the first matching degree A can be determined as the selection direction for the retired battery, that is, the selection direction for the retired battery is determined to be the grid secondary frequency regulation scenario.

[0093] In some embodiments, assuming that the first matching degree A and the second matching degree B are equal, it means that the retired battery can be applied to both the grid peak shaving scenario and the grid secondary frequency regulation scenario. Therefore, the application direction of the retired battery can simultaneously meet the grid secondary frequency regulation scenario and the grid peak shaving scenario.

[0094] In some embodiments, since the power grid peak shaving scenario may include peak shaving scenario and valley filling scenario, and the response capability required for peak shaving scenario and valley filling scenario may be different, the above application scenario selection model may also be configured with a matching degree calculation model for calculating the matching degree between retired batteries and peak shaving scenario, and a matching degree calculation model for calculating the matching degree between retired batteries and valley filling scenario.

[0095] For example, such as Figure 3 As shown, the application scenario selection model is assumed to include a first matching degree calculation model, a third matching degree calculation model, and a fourth matching degree calculation model. The first matching degree calculation model is used to calculate the matching degree between retired batteries and the secondary frequency regulation scenario of the power grid, the third matching degree calculation model is used to calculate the matching degree between retired batteries and the peak shaving scenario, and the fourth matching degree calculation model is used to calculate the matching degree between retired batteries and the valley filling scenario.

[0096] After obtaining the status information of the retired battery, this information can be input into the first matching degree calculation model to calculate the first matching degree A. The status information can then be input into the third matching degree calculation model to calculate the third matching degree C. Finally, the status information can be input into the fourth matching degree calculation model to calculate the fourth matching degree D. The application scenario selection model can then determine the matching direction for the retired battery based on the first matching degree A, the third matching degree C, and the fourth matching degree D.

[0097] In practical applications, the application scenario selection model can compare the first matching degree A, the third matching degree C, and the fourth matching degree D to determine the maximum matching degree. Assuming that the maximum value among the first matching degree A, the third matching degree C, and the fourth matching degree D is the first matching degree A, then the maximum matching degree can be determined as the first matching degree A. Therefore, the grid regulation scenario corresponding to the first matching degree A can be determined as the selection direction for the retired battery, that is, the grid secondary frequency regulation scenario can be determined as the selection direction for the retired battery.

[0098] In other embodiments, after determining the maximum matching degree as the first matching degree A, the application scenario selection model can compare the first matching degree A with the matching degree threshold S. If the first matching degree A is greater than or equal to the matching degree threshold S, then the grid regulation scenario corresponding to the first matching degree A can be determined as the selection direction for the retired battery, i.e., the grid secondary frequency regulation scenario can be determined as the selection direction for the retired battery. If the first matching degree A is less than the matching degree threshold S, then the selection direction for the retired battery can be determined as not meeting the requirements for energy storage reuse.

[0099] In other embodiments, after determining the first matching degree A, the third matching degree C, and the fourth matching degree D, the application scenario selection model compares each of these degrees with a matching degree threshold S to determine the matching degree that is greater than or equal to the threshold S. Assuming that the first matching degree A, the third matching degree C, and the fourth matching degree D are all greater than or equal to the threshold S, the sizes of these three degrees can be further compared. The maximum value of these three degrees is determined as the maximum matching degree. If the first matching degree A is greater than the third matching degree C, and the third matching degree C is equal to the fourth matching degree D, then the grid regulation demand corresponding to the first matching degree A can be determined as the selection direction for the retired battery, i.e., the selection direction for the retired battery is determined to be the grid secondary frequency regulation scenario.

[0100] In some embodiments, assuming that the first matching degree A, the third matching degree C, and the fourth matching degree D are equal, it means that the retired battery can be applied in the secondary frequency regulation scenario of the power grid, the peak shaving scenario, and the valley filling scenario. Therefore, the application direction of the retired battery can simultaneously meet the requirements of the secondary frequency regulation scenario of the power grid, the peak shaving scenario, and the valley filling scenario.

[0101] It is understood that the two examples above are merely illustrative examples of the matching degree calculation model configured in the application scenario selection model, and not limitations. In actual applications, the application scenario selection model can also be configured with matching degree calculation models corresponding to other power grid regulation scenarios, such as power grid smoothing scenario, power grid primary frequency regulation scenario, etc.

[0102] In practical applications, the matching degree calculation model can be constructed based on N response capability dimensions. That is, the matching degree calculation model calculates the matching degree between retired batteries and grid regulation scenarios based on N response capability dimensions.

[0103] In some implementations, the aforementioned N response capability dimensions include, but are not limited to, the number of charge / discharge cycles, battery capacity, charge / discharge rate, internal resistance, and charge / discharge current.

[0104] In specific applications, the aforementioned number of charge / discharge cycles can include the number of times a device can be charged, the number of times a device can be discharged, and the charge / discharge rate can include the charging rate and the discharging rate.

[0105] It is understandable that energy storage plants need to have different response capabilities for different grid regulation scenarios. Therefore, different response capability dimensions can be selected when constructing the matching degree calculation model for each grid regulation scenario.

[0106] For example, when constructing a matching degree calculation model corresponding to the power grid peak shaving scenario, response capability dimensions such as battery capacity, number of charge and discharge cycles, and internal resistance can be selected; when constructing a matching degree calculation model corresponding to the power grid secondary frequency regulation scenario, response capability dimensions such as number of charge and discharge cycles, charge and discharge rate, and internal resistance can be selected.

[0107] In some embodiments, the response capability dimension is the same in the matching degree calculation model corresponding to different power grid regulation scenarios, but the matching calculation threshold corresponding to each response capability dimension may be different.

[0108] For example, suppose that the matching degree calculation model corresponding to the grid peak shaving scenario and the matching degree calculation model corresponding to the grid secondary frequency regulation scenario both include response capability dimensions such as battery capacity, number of charge-discharge cycles, and charge-discharge rate. However, the matching calculation threshold corresponding to battery capacity in the grid peak shaving scenario is different from that in the grid secondary frequency regulation scenario. For example, the matching calculation threshold corresponding to battery capacity in the grid peak shaving scenario is greater than that in the grid secondary frequency regulation scenario. Similarly, the matching calculation threshold for the number of charge-discharge cycles in the grid peak shaving scenario is different from that in the grid secondary frequency regulation scenario. For example, the matching calculation threshold for the number of charge-discharge cycles in the grid peak shaving scenario is 1, while the matching calculation threshold for the number of charge-discharge cycles in the grid secondary frequency regulation scenario is 2, and so on.

[0109] It is understood that the above examples are not limiting. The matching degree calculation models corresponding to the power grid peak shaving scenario and the power grid secondary frequency regulation scenario may also include other response capability dimensions, such as internal resistance. This application does not impose specific limitations on this.

[0110] In some embodiments, the response capability dimension selected in the matching degree calculation model corresponding to different power grid regulation scenarios may be the same, and the weight coefficient corresponding to each response capability dimension may be different.

[0111] For example, assuming that the matching degree calculation model corresponding to the grid peak shaving scenario and the matching degree calculation model corresponding to the grid secondary frequency regulation scenario both include response capability dimensions such as battery capacity, number of charge-discharge cycles, and charge-discharge rate, the weight coefficient of the battery capacity response capability dimension in the matching degree calculation model corresponding to the grid secondary frequency regulation scenario can be 0.3, the weight coefficient of the number of charge-discharge cycles can be 0.4, and the weight coefficient of the charge-discharge rate can be 0.4. In contrast, the weight coefficient of the battery capacity response capability dimension in the matching degree calculation model corresponding to the grid peak shaving scenario can be 0.5, the weight coefficient of the number of charge-discharge cycles can be 0.3, and the weight coefficient of the charge-discharge rate can be 0.2.

[0112] It should be noted that the weight coefficients corresponding to each response capability dimension of the above different matching calculation models can be set according to the actual situation, and this application does not impose specific restrictions on this.

[0113] It is understood that the response capability dimensions selected in the matching degree calculation models corresponding to different power grid regulation scenarios can be the same or different, the matching degree calculation thresholds corresponding to different response capability dimensions can be the same or different, and the weight coefficients corresponding to different response capability dimensions can also be the same or different. The response capability dimensions selected in the matching degree calculation models corresponding to different power grid regulation scenarios can be selected according to application requirements. The above example is not a limitation. For example, in other implementations, the matching degree calculation models corresponding to the power grid peak shaving scenario can also select response capability dimensions such as maximum number of charging cycles, charging rate, maximum number of discharging cycles, discharging rate, and capacity. This application does not impose specific limitations on this.

[0114] The matching degree calculation model has been explained above. Below, we will explain how to calculate the matching degree between retired batteries and grid regulation scenarios using the matching degree calculation model, taking into account the state information of retired batteries:

[0115] In practical applications, the status information of the retired battery can be determined based on the historical usage information and / or test data of the retired battery.

[0116] In practical applications, the historical usage information of the aforementioned retired batteries includes, but is not limited to, the nameplate information of the retired batteries and their historical operating data.

[0117] The nameplate information of retired batteries can be the information on the nameplate from when the battery left the factory. This nameplate information can be used to determine the battery model and its electrical parameters such as rated power, rated charge / discharge voltage, and rated charge / discharge current. Typically, the nameplate information can be determined from the battery nameplate itself. Generally, when a battery leaves the factory, its nameplate records parameters such as battery model, rated power, rated charge / discharge voltage, and rated charge / discharge current. Therefore, the nameplate information can be determined simply by checking the battery's nameplate. For batteries with damaged or missing nameplates, the nameplate information can be determined based on the power requirements of the same electrical equipment, or by comparing it with batteries of the same model produced in the same batch.

[0118] Historical operating data of retired batteries includes the application scenarios of the retired battery (such as its application in electric vehicles or electric bicycles), and various battery parameters during the application process, such as charging and discharging current, cell temperature, cell state of charge, balancing parameters, etc. during historical operation.

[0119] The retired batteries mentioned in this application refer to battery boxes or battery packs that have been retired from electric vehicles and other electrical equipment without any processing. Typically, a battery management system (BMS) is installed in the battery box / pack to manage and maintain the batteries, monitor them to reduce overcharging or over-discharging, thereby extending their lifespan. The BMS also has the function of recording and storing data, that is, recording the status data of the batteries in the battery box during operation, such as historical charging and discharging current, cell temperature, cell state of charge, balancing parameters, and number of charge and discharge cycles. Specifically, obtaining the historical operating data of the retired battery can be achieved by acquiring the historical operating data recorded by the BMS in the battery box / pack of the retired battery. That is, after obtaining the retired battery, the historical operating status data of the batteries in the battery box / pack recorded and stored in the BMS of the retired battery is read. Specifically, the historical operating data recorded and stored in the BMS can be read through a data cable connection or other means, and then sent to the server through upload or other means so that the server can obtain the historical operating data.

[0120] In some implementations, provided that the battery box / battery pack has a data upload function, the operating data of the aforementioned battery box / battery pack can be directly uploaded to the server, so that after the retired battery enters the energy storage power plant, the server can obtain the historical operating data related to the retired battery through the relevant identification information of the retired battery.

[0121] In some implementations, if the operating data of the battery box / battery pack is not recorded and saved by the BMS module, the historical operating data related to the retired battery can be obtained through the module that records and saves the historical operating data of the battery box / battery pack.

[0122] In practical applications, after obtaining the historical usage information of retired batteries, the maximum allowable charge and discharge current, the maximum allowable number of charge and discharge cycles, the maximum capacity, internal resistance, balancing parameters, and other status information of retired batteries can be determined based on the historical usage information.

[0123] In some embodiments, to better understand the current usage status of retired batteries, corresponding tests, such as charge-discharge tests, can be performed on the retired batteries to determine their status information, such as the maximum allowable charge-discharge current, the maximum allowable number of charge-discharge cycles, the maximum capacity, internal resistance, and equalization parameters.

[0124] In practical applications, after obtaining the status information of the retired battery, the status information of the retired battery is compared with the matching calculation threshold corresponding to the response capability dimension in each matching degree calculation model. If the status information of the retired battery exceeds the matching calculation threshold corresponding to the response capability dimension, the matching coefficient of the response capability dimension is set to 1. If the status information of the retired battery does not exceed the matching calculation threshold corresponding to the response capability dimension, the matching coefficient of the response capability dimension is set to 0.

[0125] For example, suppose the maximum allowable charging current of a retired battery in its status information is 1.8A, while the matching threshold for charging current in the matching degree calculation model for the grid secondary frequency regulation scenario is 3A. Since 1.8A < 3A, the matching coefficient for this response capability dimension is 0. In the matching degree calculation model for the grid peak shaving scenario, the matching threshold for charging current is 1.2A. Since 1.8A > 1.2A, the matching coefficient for this response capability dimension is 1. As another example, suppose the capacity of a retired battery in its status information is 100kWh, and the matching threshold for capacity in the matching degree calculation model for the grid secondary frequency regulation scenario is 60kWh. Since 100kWh > 60kWh, the matching coefficient for this response capability dimension is 1. In the matching degree calculation model for the grid peak shaving scenario, the matching threshold for capacity is 80kWh. Since 100kWh > 80kWh, the matching coefficient for this response capability dimension is 1.

[0126] After determining the matching coefficient for each response capability dimension, the matching coefficient for each response capability dimension is multiplied by the corresponding weight coefficient to obtain the matching degree for each response capability dimension. The sum of the matching degrees for all response capability dimensions is determined as the matching degree between the retired battery and the power grid regulation scenario, which is the result output by the matching degree calculation model.

[0127] In some implementations, if the state information of a retired battery exceeds the matching calculation threshold corresponding to the response capability dimension, the matching coefficient of the response capability dimension can be determined based on the state information and the matching calculation threshold. For example, the matching coefficient of the response capability dimension can be the quotient of the state information and the matching calculation threshold.

[0128] For example, assuming the capacity of a retired battery in its status information is 100kWh, and the matching threshold for capacity in the grid secondary frequency regulation scenario matching calculation model is 50kWh, since 100kWh > 50kWh, the matching coefficient for this response capability dimension is 2(100kWh / 50kWh). Assuming the matching threshold for capacity in the grid peak shaving scenario matching calculation model is 80kWh, since 100kWh > 80kWh, the matching coefficient for this response capability dimension is 1.25.

[0129] In practical applications, if a certain response capability of a retired battery does not meet the response capability requirements (matching calculation threshold) of a certain power grid regulation scenario, the matching degree output by the matching degree calculation model corresponding to the power grid regulation scenario can be 0. That is, as long as one response capability does not meet the requirements, it is determined that the retired battery is not matched with the power grid regulation scenario.

[0130] The state information of the retired battery is compared with the matching calculation threshold of the corresponding response capability dimension in the matching degree calculation model for each grid regulation scenario to determine the total matching coefficient of the retired battery. Then, the selection direction of the retired battery is determined based on the matching coefficient between the retired battery and each grid regulation scenario. For details on how to determine the selection direction of the retired battery based on the matching coefficient between the retired battery and each battery regulation scenario, please refer to the relevant discussion above, which will not be repeated here.

[0131] In some implementations, the aforementioned application scenario matching model can also be a neural network model. This model is trained using a large amount of sample data (sample pairs consisting of historical state data of retired batteries and the corresponding matching results). The trained neural network model can then determine the matching direction of the retired battery based on the input state information. In other words, the trained neural network model is the application scenario matching model, capable of analyzing the state data of retired batteries, extracting their state features, and learning the matching direction based on these features.

[0132] It should be noted that the training process of a neural network model can include training and validation processes. The samples can be divided into training samples and validation samples. After training is completed, if the neural network model can converge, it means that the neural network model has been trained successfully.

[0133] In S12, if the selection direction of the retired batteries meets the application requirements, the assembly strategy of the retired batteries is determined based on the selection direction of the retired batteries and the energy storage parameters of the energy storage plant.

[0134] In practical applications, the energy storage parameters of an energy storage power plant mainly refer to the operating parameters of the energy storage core of the power plant.

[0135] In practical applications, when constructing the energy storage core of an energy storage power plant, the energy storage core can be divided into multiple energy storage zones. Each energy storage zone corresponds to different grid functions. Among them, grid functions can include grid transmission functions and grid regulation functions. Grid transmission functions refer to the function of using the energy storage core to output the electrical energy required by the grid.

[0136] For example, such as Figure 4 As shown, the energy storage core can be divided into three energy storage zones. The first energy storage zone A1 is equipped with retired batteries that meet the secondary frequency regulation requirements of the power grid, the second energy storage zone A2 is equipped with retired batteries that meet the peak shaving requirements of the power grid, and the third energy storage zone A3 is equipped with retired batteries that meet the daily power transmission requirements of the energy storage grid.

[0137] It is understandable that the grid regulation function corresponding to the energy storage zone can correspond to the above-mentioned grid regulation scenario (that is, the selection direction). For example, the first energy storage zone A1 is equipped with retired batteries that meet the requirements of the secondary frequency regulation of the grid, which corresponds to the secondary frequency regulation scenario of the grid.

[0138] It should be noted that the size of each energy storage zone can be determined according to actual application needs, and this application does not impose specific restrictions here.

[0139] Understandable Figure 4 For illustrative purposes only, the aforementioned energy storage core can be further divided into more types of energy storage zones, such as energy storage zones that meet the peak shaving requirements of the power grid, energy storage zones that meet the valley filling requirements of the power grid, etc. This application does not impose any specific limitations.

[0140] After determining the selection direction of the retired battery in S11, it is possible to further determine whether the selection direction of the retired battery meets the application requirements. If the selection direction of the retired battery meets the application requirements, the assembly strategy of the retired battery can be determined based on the selection direction of the retired battery and the energy storage parameters of the energy storage plant.

[0141] Here, the above-mentioned assembly strategy is used to indicate the installation information of the energy storage core of the retired battery energy storage battery, including the target installation location and the target installation time. The target installation location refers to the specific location where the retired battery is installed in the energy storage core, and the target installation time refers to when the retired battery will be installed in the corresponding location.

[0142] Understandably, the specific method for determining whether the selected configuration of the retired battery meets the application requirements is as follows: if the selected configuration of the retired battery does not meet the requirements for energy storage reuse, then the selected configuration of the retired battery is determined to not meet the application requirements; if the selected configuration of the retired battery is any other than not meeting the requirements for energy storage reuse, then the selected configuration of the retired battery is determined to meet the application requirements.

[0143] In one embodiment of this application, if the selection direction of the retired battery does not meet the application requirements, the retired battery can be scrapped. The scrapping operation can be to directly output the scrapped retired battery as a product to the waste recycling agency, or to perform disassembly, harmless treatment and other operations on the scrapped battery before outputting it.

[0144] The energy storage parameters of the aforementioned energy storage core may include its energy storage capacity, output power, charging efficiency, discharging efficiency, cycle life, energy density, etc.

[0145] After determining the selection direction for retired batteries, energy storage plants can further obtain the energy storage parameters of the corresponding energy storage zones. Then, based on these parameters, they can analyze the most suitable location for the retired battery and determine this location as its installation position. The most suitable location for a retired battery refers to the position where its performance is best achieved.

[0146] In practical applications, once the selection direction of retired batteries is determined, the corresponding energy storage zones can be identified based on this selection direction. For example, assuming the selection direction of retired batteries is for grid peak shaving scenarios, the energy storage parameters of the energy storage zones that meet the grid peak shaving requirements can be obtained.

[0147] After obtaining the energy storage parameters of the energy storage zone, the performance of each installed retired battery in the energy storage zone can be analyzed. The location of the battery with the worst performance (i.e. the lowest battery performance score) among the installed retired batteries (called the target battery) is determined as the installation location of the retired battery. In order to improve the overall performance of the energy storage power plant, the battery with the worst performance can be removed, that is, the installation time of the retired battery is determined as immediate installation.

[0148] It should be noted that battery performance ratings can be determined based on a battery performance rating model. Battery performance ratings can be calculated from two dimensions: State of Health (SOH) and battery balance parameters. The mean SOH of each battery in the energy storage zone is determined, and then the SOH of each retired battery is compared to this mean to calculate its score. For example, the ratio of the difference between the retired battery's SOH and the mean (called the battery health difference) to the mean SOH is determined as the score for that dimension. Similarly, the score for the battery balance parameter dimension can be calculated. Then, the scores from both dimensions are summed to obtain the battery performance rating for the retired battery.

[0149] Alternatively, in other embodiments, a battery health coefficient threshold and a battery balance performance threshold can be set according to the actual application, and then the battery performance score can be determined based on these two thresholds.

[0150] It is understood that in other embodiments, battery performance may be determined solely based on the battery health coefficient, with the battery having the lowest health coefficient being identified as the target battery. Of course, other evaluation dimensions can also be used to determine battery performance scores, and this application does not impose specific limitations on this.

[0151] Among them, the state of health (SOH), also known as the battery degradation coefficient, is used to reflect the degree of battery aging and its health status. The higher the SOH value, the lower the degree of battery aging and the higher the health status; the lower the SOH value, the higher the degree of battery aging and the lower the health status.

[0152] In some embodiments, after identifying the worst-performing battery among the installed retired batteries as the target battery, the remaining cycle life of the target battery can be further determined, the removal time of the target battery can be estimated, and the removal time of the target battery can be determined as the installation time of the retired battery.

[0153] In some embodiments, after determining the energy storage parameters of the energy storage zone, the energy storage plant can also analyze the compatibility between retired batteries and other installed retired batteries. Then, the installation location of the most compatible retired battery (referred to as the target battery) is determined as the installation location of the retired battery. The remaining cycle life of the target battery is then determined, and the dismantling time of the target battery is estimated. Finally, the dismantling time of the target battery is determined as the installation time of the retired battery. This minimizes the impact of replacing retired batteries on the energy storage parameters of the entire energy storage core.

[0154] Here, the above-mentioned determination of the most suitable retired battery as the target battery can be achieved by comparing the retired battery with the installed retired batteries from multiple dimensions such as battery model, battery capacity, and battery balance parameters, comprehensively calculating the similarity between the retired battery and the installed retired batteries, and then determining the installed battery with the highest similarity as the target battery (i.e., the most suitable battery).

[0155] It is understandable that the process of determining the similarity between retired batteries and installed retired batteries based on multiple dimensions such as battery model, battery capacity, and battery balance parameters can be performed based on a pre-trained battery similarity recognition network. That is, a neural network model capable of battery similarity recognition can be pre-built, and then the neural network model can be trained to obtain a pre-trained battery similarity recognition network. This network can be implemented using various existing neural network models.

[0156] In some embodiments, a mathematical model for calculating battery similarity can also be constructed. Based on multiple dimensions such as battery model, battery capacity, battery balance parameters, and battery charging and discharging parameters, a mathematical model for calculating battery similarity (i.e., battery similarity calculation model) can be constructed, and the similarity between retired batteries and each installed retired battery can be calculated using the battery similarity calculation model.

[0157] It should be noted that, in the embodiments of this application, the dimensions selected for the battery similarity calculation model may specifically include battery model, battery capacity, and battery charging and discharging parameters, etc. In the specific calculation process, if the battery models are the same, the similarity coefficient for the battery model dimension is 1; if the battery models are different, the similarity coefficient for the battery model dimension is 0. The similarity coefficient for the battery capacity dimension is represented by the ratio of the absolute value of the battery capacity difference (i.e., the difference between the battery capacity of the retired battery and the battery capacity of the installed retired battery) to the battery capacity of the retired battery. The similarity coefficient for the battery charging voltage can be represented by the ratio of the absolute value of the battery charging voltage difference (i.e., the difference between the battery charging voltage of the retired battery and the battery charging voltage of the installed retired battery) to the charging voltage of the retired battery. The similarity coefficient for the battery charging current can be represented by the ratio of the absolute value of the battery charging current difference (i.e., the difference between the battery charging current of the retired battery and the battery charging current of the installed retired battery) to the charging current of the retired battery. The similarity coefficient for the battery discharging current can be represented by the ratio of the absolute value of the battery discharging current difference (i.e., the difference between the battery discharging current of the retired battery and the battery discharging current of the installed retired battery) to the discharging current of the retired battery.

[0158] After determining the similarity coefficients for each dimension, the similarity coefficients for each dimension are summed to obtain the similarity between the retired battery and the installed retired battery. Alternatively, different weight coefficients are set for each dimension, and the similarity coefficients for each dimension are summed in a weighted manner to obtain the similarity between the retired battery and the installed retired battery.

[0159] In some embodiments, after determining the installation location of retired batteries, the energy storage plant can simulate the operating conditions (i.e., simulated operating conditions) after installing the retired batteries into the energy storage core corresponding to the energy storage zone. It can then determine whether the installation location of the retired batteries is the most suitable location (i.e., whether the simulated operating conditions meet the operating requirements) from dimensions such as battery balance and battery utilization sufficiency. If not, the installation location of the batteries can be re-determined. Specifically, the originally determined target batteries can be removed from the alternatives, and then the target batteries can be re-determined from the remaining installed retired batteries. If so, the installation strategy is determined.

[0160] It should be noted that the above-mentioned operational status model can be output based on existing simulation models, and this application does not impose specific restrictions on it.

[0161] In some embodiments, after determining the energy storage parameters of the energy storage zone, the energy storage plant can also identify the retired battery with the shortest remaining cycle life (referred to as the target battery), and then determine the target installation time based on the remaining cycle life of the target battery. This allows the retired battery to be applied to the energy storage plant as quickly as possible.

[0162] In practical applications, determining the installation time of a decommissioned battery based on the remaining cycle life of the target battery can specifically be as follows:

[0163] Obtain historical operating data of the aforementioned energy storage power plants;

[0164] The power input and output model of the energy storage power plant is determined based on the historical operating data of the energy storage power plant;

[0165] Estimate the operating parameters of the target battery based on the power input-output model of the energy storage plant;

[0166] Based on the remaining cycle life and usage parameters of the target battery, predict the disassembly time of the target battery.

[0167] In practical applications, the historical operating data of the aforementioned energy storage power plant can be the operating data of the energy storage power plant within a month, the operating data of the energy storage power plant in the previous week, or the operating data of the energy storage power plant in a single day (i.e., the daily load curve of the previous day). Here, we will take the daily load curve of the previous day as an example to illustrate the historical operating data of the energy storage power plant.

[0168] The power input-output model of the aforementioned energy storage power plant can also be characterized based on the load. The power input-output model is specifically used to determine the load changes of the energy storage power plant over a historical period. Based on this power input-output model, the historical regulation of the power grid can be determined. The aforementioned power input-output model can be based on existing simulation models, and this application does not impose any specific restrictions on it.

[0169] After obtaining the daily load curve of the energy storage plant the previous day, the application status of the energy storage zone corresponding to the selected direction of the retired battery can be determined. Then, it is possible to predict how many times the retired batteries in the energy storage zone need to be called up each day to achieve grid regulation, and the number of times each retired battery is used each time. This determines the specific number of times the retired battery participates in grid regulation each day. Then, based on the specific number of times the retired battery participates in grid regulation each day and the remaining cycle number of the retired battery, the remaining utilization time of the battery is determined, that is, the predicted dismantling time of the target battery is determined. This dismantling time can then be determined as the installation time of the retired battery.

[0170] In some embodiments, since the daily load curve of the previous day can only reflect the load change of the power grid on the previous day, the weekly change of the daily load of the energy storage power grid can also be determined based on the daily load curve of the previous week. Then, the predicted dismantling time of the target battery can be adjusted by combining the peak and off-peak dates of each week.

[0171] In some embodiments, the monthly variation of the daily load of the energy storage grid can be determined based on the daily load curve of the previous month, and then the predicted dismantling time of the target battery can be adjusted by combining the peak and off-peak dates in the monthly variation.

[0172] In some embodiments, since temperature changes also have a certain impact on daily load changes, the daily load curve and temperature data of the previous month can be obtained to determine the relationship between temperature changes and load changes. Then, temperature forecast data for a future period can be obtained, and the predicted disassembly time of the target battery can be adjusted in combination with the temperature forecast data.

[0173] In some embodiments, an assembly strategy generation model can be constructed to generate the assembly strategy for retired batteries. Specifically, the assembly strategy generation model is used to determine the corresponding energy storage partition based on the selection direction of the retired battery, and then determine the assembly strategy for the retired battery based on the state information of the retired battery and the energy storage parameters of the energy storage partition.

[0174] Therefore, it can be seen that the battery selection method provided in this application embodiment can, based on this, provide a battery selection method, device and energy storage power plant, which can determine the suitable grid regulation scenario for the retired battery based on its battery state, and then determine the installation strategy of the retired battery based on the energy storage parameters of the energy storage core of the energy storage power plant and the selection direction of the retired battery, thereby enabling the retired battery to be used more reasonably, that is, the retired battery can be applied in the grid regulation scenario to which the retired battery is applicable, and an energy storage core that conforms to various grid regulation scenarios can be constructed based on the retired battery.

[0175] Please see Figure 5 , Figure 5 This illustration shows a schematic diagram of the implementation flow of a battery selection method provided in another embodiment of this application, as shown below. Figure 5 As shown, the embodiments of this application also include the following steps:

[0176] In S13, retired batteries are installed according to the installation strategy.

[0177] In practical applications, the above installation strategy includes the installation location and time of the retired battery, so the assembly modules in the energy storage plant can be controlled to install the retired battery at the corresponding installation time.

[0178] It should be noted that the above assembly module may include a transport device and an assembly mechanism. The transport device can transport the retired batteries from the area where they are stored to the energy storage core, and the assembly mechanism can disassemble the target battery and then install the retired battery into the corresponding installation position to realize the assembly of the retired battery.

[0179] It should be noted that the aforementioned transport device and assembly mechanism can be implemented using intelligent robots, intelligent robotic arms, and other devices, and this application does not impose specific restrictions on them.

[0180] The battery selection method provided in the embodiments of this application has been described above. Next, the battery selection device corresponding to the embodiments of this application will be described:

[0181] Please see Figure 6 , Figure 6 A schematic diagram of the battery selection device provided in an embodiment of this application is given. Figure 6 As shown, the battery selection device may include a selection direction determination module 61 and an installation strategy determination module 62.

[0182] The aforementioned selection direction determination module 61 is used to determine the selection direction of retired batteries based on the status information and application scenario selection model of retired batteries.

[0183] The application scenario selection model is constructed based on the power grid regulation scenario.

[0184] The installation strategy determination module 62 is used to determine the installation strategy of retired batteries based on the selection direction of retired batteries and the energy storage parameters of the energy storage plant, provided that the selection direction of retired batteries meets the application requirements. The installation strategy is used to indicate the installation information of retired batteries in the energy storage core of the energy storage plant.

[0185] In one implementation, the above application scenario selection model includes demand matching calculation models corresponding to different power grid regulation scenarios, and the above selection direction determination module 61 is specifically used to obtain the status information of retired batteries.

[0186] The status information of retired batteries is input into the demand matching calculation model corresponding to each power grid regulation scenario to calculate the matching degree between retired batteries and each power grid regulation scenario.

[0187] The application scenario selection model determines the selection direction of retired batteries based on their matching degree with various power grid regulation scenarios.

[0188] In one implementation, the energy storage core of the above-mentioned energy storage power plant includes multiple energy storage zones, with different energy storage zones corresponding to different grid functions. The above-mentioned installation strategy determination module 62 is specifically used to determine the corresponding energy storage zone according to the selection direction of the retired battery.

[0189] Obtain the energy storage parameters of the energy storage zone;

[0190] The target battery is determined based on the energy storage parameters of the energy storage zone;

[0191] Determine the assembly strategy for retired batteries based on the battery information of the target battery.

[0192] In one implementation, the target battery is determined based on the energy storage parameters of the energy storage zone, including:

[0193] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0194] Based on the battery parameters of each installed retired battery, the retired battery with the lowest battery performance score is determined, and the retired battery with the lowest battery performance score is designated as the target battery.

[0195] In another implementation, the target battery is determined based on the energy storage parameters of the energy storage zone, including:

[0196] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0197] Calculate the similarity between the retired battery and each of the installed retired batteries;

[0198] The installed retired battery with the highest similarity to the retired battery was identified as the target battery.

[0199] In another implementation, the target battery is determined based on the energy storage parameters of the energy storage zone, including:

[0200] Obtain the battery parameters of each installed retired battery in the energy storage zone;

[0201] Based on the battery parameters of each installed retired battery, the installed retired battery with the shortest remaining cycle life is determined, and the retired battery with the shortest remaining cycle life is determined as the target battery.

[0202] In one implementation, the battery selection device further includes a simulation module, which is specifically used to simulate the simulated operation of the energy storage core after the retired battery is installed in the energy storage zone, based on the energy storage parameters of the retired battery and the energy storage zone.

[0203] If the simulated operation of the energy storage core does not meet the operational requirements, the installation strategy for retired batteries will be redefined.

[0204] In one implementation, the battery information of the target battery includes the installation location and remaining cycle life of the target battery. Based on the battery information of the target battery, a strategy for assembling retired batteries is determined, including:

[0205] Obtain historical operating data from energy storage power plants;

[0206] The power input-output model of the energy storage power plant is determined based on its historical operating data.

[0207] Estimate the operating parameters of the target battery based on the power input-output model of the energy storage plant;

[0208] Predict the disassembly time of the target battery based on its remaining cycle life and usage parameters;

[0209] The installation location of the target battery is defined as the target installation location, and the removal time of the target battery is defined as the target installation time.

[0210] It should be understood that, Figure 6 In the structural block diagram of the battery selection device shown, each module is used to perform... Figures 1 to 5 The steps in the corresponding embodiments, and for Figures 1 to 5 The steps in the corresponding embodiments have been explained in detail in the above embodiments. Please refer to them for details. Figures 1 to 5 as well as Figures 1 to 5 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0211] Based on this, the battery selection device provided in this application embodiment can also determine the suitable grid regulation scenario based on the battery status of the retired battery, and then determine the installation strategy of the retired battery based on the energy storage parameters of the energy storage core of the energy storage power plant and the selection direction of the retired battery, and assemble it based on the installation strategy, so that the retired battery can be used more reasonably, that is, the retired battery can be applied in the grid regulation scenario to which the retired battery is applicable, and realize the need to build an energy storage core that meets various grid regulation scenarios based on the retired battery.

[0212] This application also provides an energy storage power plant, which may include the battery matching device described above. The battery matching device in the energy storage power plant can execute the battery matching methods provided in the above embodiments to realize the matching and application of retired batteries.

[0213] Please see Figure 7 , Figure 7 A schematic diagram of the architecture of an energy storage power plant provided in an embodiment of this application is given.

[0214] like Figure 7 As shown, the aforementioned energy storage power plant includes a battery selection module 71, an assembly module 72, and an energy storage module 73.

[0215] The battery selection module 71 described above is used to execute the selection method for retired batteries as described in the various method embodiments above.

[0216] Assembly module 72 is used for the installation strategy of retired batteries, which are installed in the energy storage core.

[0217] Energy storage module 73 is used to fulfill the energy storage needs of the power grid.

[0218] Specifically, this could involve fulfilling the power supply needs of the power grid and / or regulating the power grid.

[0219] Of course, the assembly module 72 mentioned above can also be used to perform disassembly operations, that is, to remove the retired batteries that need to be removed from the energy storage core. For the disassembled scrapped batteries, a scrapping operation can be performed.

[0220] In practical applications, the energy storage power plant provided in this application uses retired batteries as raw materials. Specifically, these retired batteries can be retired power batteries from new energy vehicles. It is understood that the retired batteries can also include other types of retired batteries.

[0221] This application also provides an electronic device. Figure 8 This is a structural block diagram of an electronic device provided in another embodiment of this application. For example... Figure 8 As shown, the electronic device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80, such as a program for generating an evaluation report. When the processor 80 executes the computer program 82, it implements the steps of the various embodiments of the battery management methods described above, for example... Figure 1 S11 to S12 are shown. Alternatively, the processor 80 implements the above when executing the computer program 82. Figure 6 The functions of each module in the corresponding embodiments, for example, Figure 6 For details on the functions of units 61 to 62 shown, please refer to [link / reference needed]. Figure 6 The relevant descriptions in the corresponding embodiments.

[0222] For example, computer program 82 can be divided into one or more modules, one or more of which are stored in memory 81 and executed by processor 80 to complete this application. One or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 82 in electronic device 8. For example, computer program 82 can be divided into various unit modules, each with the specific functions described above.

[0223] Electronic device 8 may include, but is not limited to, processor 80 and memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 8 and does not constitute a limitation on electronic device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0224] The processor 80 can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays, or other programmable logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The memory 81 can be an internal storage unit of the electronic device 8, such as a hard disk or RAM. The memory 81 can also be an external storage device of the electronic device 8, such as a plug-in hard disk, smart memory card, flash memory card, etc., equipped on the electronic device 8. Furthermore, the memory 81 can include both internal storage units and external storage devices of the electronic device 8.

[0225] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0226] This application provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0227] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0228] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0229] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0230] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0231] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0232] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A battery selection method, characterized in that, When applied to energy storage power plants, the battery selection method includes: Based on the status information of retired batteries and the application scenario selection model, the selection direction of the retired batteries is determined. The application scenario selection model is constructed according to the power grid regulation scenario. The power grid regulation scenario includes the power grid secondary frequency regulation scenario and the power grid peak shaving scenario. The application scenario selection model configures matching degree calculation models corresponding to different power grid regulation scenarios. When constructing the matching degree calculation model corresponding to the power grid secondary frequency regulation scenario, the number of charge and discharge cycles, the charge and discharge rate, and the internal resistance are selected as the response capability dimensions. When constructing the matching degree calculation model corresponding to the power grid peak shaving scenario, the battery capacity, the number of charge and discharge cycles, and the internal resistance are selected as the response capability dimensions. If the selection direction of the retired battery meets the application requirements, the assembly strategy of the retired battery is determined according to the selection direction of the retired battery and the energy storage parameters of the energy storage plant; the assembly strategy is used to indicate the installation information of the retired battery in the energy storage core of the energy storage plant. The application scenario selection model includes demand matching calculation models corresponding to different power grid regulation scenarios; the matching model based on the state information and application type of retired batteries includes: Obtain the status information of the retired battery; The status information of the retired battery is input into the demand matching calculation model corresponding to each power grid regulation scenario to calculate the matching degree between the retired battery and each power grid regulation scenario; The application scenario selection model determines the selection direction of the retired battery based on the matching degree between the retired battery and each of the power grid regulation scenarios.

2. The battery selection method according to claim 1, characterized in that, The energy storage core of the energy storage plant includes multiple energy storage zones, each corresponding to a different grid function. Given that the selection of retired batteries meets application requirements, the assembly strategy for the retired batteries is determined based on the selection of retired batteries and the energy storage parameters of the energy storage plant, including: The corresponding energy storage zones are determined based on the selection and matching direction of the retired batteries; Obtain the energy storage parameters of the energy storage zone; The target battery is determined based on the energy storage parameters of the energy storage zone; The assembly strategy for the retired battery is determined based on the battery information of the target battery.

3. The battery selection method according to claim 2, characterized in that, The step of determining the target battery based on the energy storage parameters of the energy storage zone includes: Obtain the battery parameters of each installed retired battery in the energy storage zone; The retired battery with the lowest performance score is determined based on the battery parameters of each installed retired battery, and the retired battery with the lowest performance score is determined as the target battery.

4. The battery selection method according to claim 2, characterized in that, The step of determining the target battery based on the energy storage parameters of the energy storage zone includes: Obtain the battery parameters of each installed retired battery in the energy storage zone; Calculate the similarity between the retired battery and each of the installed retired batteries; The installed retired battery with the highest similarity to the retired battery is identified as the target battery.

5. The battery selection method according to claim 2, characterized in that, The step of determining the target battery based on the energy storage parameters of the energy storage zone includes: Obtain the battery parameters of each installed retired battery in the energy storage zone; Based on the battery parameters of each installed retired battery, the installed retired battery with the shortest remaining cycle life is determined, and the retired battery with the shortest remaining cycle life is determined as the target battery.

6. The battery selection method according to any one of claims 2 to 5, characterized in that, The battery selection method also includes: Based on the energy storage parameters of the retired battery and the energy storage zone, the simulated operation of the energy storage core is simulated after the retired battery is installed in the energy storage zone. If the simulated operating conditions of the energy storage core do not meet the operational requirements, the installation strategy for the retired battery will be redefined.

7. The battery selection method according to any one of claims 2 to 5, characterized in that, The battery information of the target battery includes the installation location of the target battery and the remaining cycle life of the target battery. Determining the assembly strategy of the retired battery based on the battery information of the target battery includes: Obtain the historical operating data of the energy storage power plant; The power input-output model of the energy storage power plant is determined based on its historical operating data. The operating parameters of the target battery are estimated based on the power input-output model of the energy storage plant. Based on the remaining cycle life and usage parameters of the target battery, predict the disassembly time of the target battery; The installation location of the target battery is defined as the target installation location, and the removal time of the target battery is defined as the target installation time.

8. A battery selection device, characterized in that, include: The selection direction determination module is used to determine the selection direction of the retired battery based on the status information and application scenario selection model of the retired battery. The application scenario selection model is constructed based on the power grid regulation scenario; the power grid regulation scenario includes the power grid secondary frequency regulation scenario and the power grid peak shaving scenario; the application scenario selection model configures matching degree calculation models corresponding to different power grid regulation scenarios. When constructing the matching degree calculation model corresponding to the power grid secondary frequency regulation scenario, the number of charge and discharge cycles, the charge and discharge rate, and the internal resistance are selected as the response capability dimensions. When constructing the matching degree calculation model corresponding to the power grid peak shaving scenario, the battery capacity, the number of charge and discharge cycles, and the internal resistance are selected as the response capability dimensions. An assembly strategy determination module is used to determine the assembly strategy of the retired battery based on the selection direction of the retired battery and the energy storage parameters of the energy storage plant, provided that the selection direction of the retired battery meets the application requirements; the assembly strategy is used to indicate the installation information of the retired battery in the energy storage core of the energy storage plant. The application scenario selection model includes demand matching calculation models corresponding to different power grid regulation scenarios; the matching model based on the state information and application type of retired batteries includes: Obtain the status information of the retired battery; The status information of the retired battery is input into the demand matching calculation model corresponding to each power grid regulation scenario to calculate the matching degree between the retired battery and each power grid regulation scenario; The application scenario selection model determines the selection direction of the retired battery based on the matching degree between the retired battery and each of the power grid regulation scenarios.

9. An energy storage power plant, characterized in that, The energy storage power plant includes the battery matching device as described in claim 8, or includes electronic equipment that performs the battery matching method as described in any one of claims 1 to 7.

Images