A charging and discharging management system and method for a photovoltaic energy storage system

Abstract

This application provides a charge / discharge management system and method for a photovoltaic energy storage system. By using real-time health and state-of-charge parameters and the dynamic aging coefficient of each lithium-ion battery module, the charge / discharge state of each lithium-ion battery module is dynamically prioritized, generating a dynamic priority sequence. Based on this sequence, all lithium-ion battery modules are divided into a priority response group and a delayed response group. The charge / discharge of the photovoltaic energy storage system is then coordinated based on the total charge / discharge power command, the priority response group, and the delayed response group. Using the scheme of this application, the charge / discharge of the photovoltaic energy storage system can be coordinated based on the differences in aging acceleration of lithium-ion batteries across different voltage plateaus.

Description

Technical Field

[0001] This application relates to the field of charge and discharge management technology, and more specifically, to a charge and discharge management system and method for a photovoltaic energy storage system. Background Technology

[0002] Charge and discharge management is a core control component of a battery system. By monitoring voltage, current, and temperature in real time, it precisely regulates the charging and discharging process to prevent overcharging, over-discharging, and overheating. It also enables battery balancing, state estimation, and safety protection, thereby improving charging efficiency, extending cycle life, and ensuring safe use. It is widely used in electric vehicles, energy storage, and portable electronic devices.

[0003] With the large-scale application of distributed photovoltaic (PV) power generation, PV energy storage systems have become key equipment for smoothing power fluctuations, achieving peak shaving and valley filling, and improving self-consumption rates. These systems typically consist of multiple lithium-ion battery modules connected in parallel to provide sufficient capacity and power support. The aging rate of lithium-ion batteries is affected not only by temperature and cycle count but also significantly depends on the voltage plateau region of their state of charge: in the high-voltage and low-voltage plateau regions, side reactions intensify, and the aging rate increases exponentially. Existing charge and discharge management methods often allocate power based on a simple sorting of battery state of charge or health status, without establishing a refined dynamic evaluation mechanism to address the differences in aging acceleration at different voltage plateau regions. This results in some battery modules operating under harsh conditions for extended periods, creating bottlenecks in their lifespan and limiting the overall service life of the system. Meanwhile, under the time-of-use pricing mechanism, energy storage systems need to balance economic dispatch and lifespan protection. Traditional methods make decisions on these two aspects separately, lacking a coordinated control mechanism that deeply couples aging suppression strategies with charge and discharge power commands. This makes it difficult to delay battery aging to the greatest extent while ensuring economic efficiency. Therefore, how to coordinate the charge and discharge of photovoltaic energy storage systems based on the differences in aging acceleration of lithium-ion batteries in different voltage plateau segments has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a charge and discharge management system and method for a photovoltaic energy storage system, which can coordinate the charge and discharge of the photovoltaic energy storage system based on the differences in aging acceleration of lithium-ion batteries in different voltage platform segments.

[0005] In a first aspect, this application provides a charging and discharging management method for a photovoltaic energy storage system, the photovoltaic energy storage system comprising multiple lithium-ion battery modules, the method comprising the following steps: Obtain real-time health and state of charge parameters for each lithium-ion battery pack; The dynamic aging coefficient of each lithium-ion battery module is determined based on the voltage plateau range of the state of charge of the photovoltaic energy storage system. Based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, the charge and discharge states of each lithium-ion battery pack are dynamically prioritized to generate a dynamic priority sequence, and all lithium-ion battery packs are divided into a priority response group and a delayed response group based on the dynamic priority sequence. The total charging and discharging power command of the photovoltaic energy storage system is determined based on the photovoltaic power generation, load power and time-of-use electricity price of the photovoltaic energy storage system in the current period. The charging and discharging of the photovoltaic energy storage system is coordinated and controlled by the total charging and discharging power command, the priority response group, and the delay response group.

[0006] In some embodiments, obtaining the real-time health status parameters and state of charge parameters of each lithium-ion battery pack specifically includes: The battery management system collects data on the terminal voltage, operating current, surface temperature, and internal resistance of each lithium-ion battery pack in real time. The real-time state of charge parameters of each lithium-ion battery pack are determined based on the terminal voltage and the operating current. The real-time health status parameters of each lithium-ion battery pack are determined based on the internal resistance data, the surface temperature, and the historical cycle count, wherein the historical cycle count is equivalent to the equivalent number of full charge and discharge cycles at a standard charge-discharge rate and a standard temperature of 25°C.

[0007] In some embodiments, determining the dynamic aging coefficient of each lithium-ion battery module based on the voltage plateau range of the photovoltaic energy storage system's state of charge specifically includes: The average state of charge of the photovoltaic energy storage system is obtained, and the current voltage plateau segment corresponding to the average state of charge is determined, wherein the voltage plateau segment includes a low voltage plateau segment, a medium voltage plateau segment, and a high voltage plateau segment. Based on the mapping relationship between the pre-calibrated voltage plateau segment and the aging acceleration factor of the photovoltaic energy storage system, the target aging acceleration factor corresponding to the current voltage plateau segment is determined. Based on the real-time health status parameters of each lithium-ion battery pack and the target aging acceleration factor, the dynamic aging coefficient of each lithium-ion battery pack is determined.

[0008] In some embodiments, the dynamic priority evaluation of the charge / discharge state of each lithium-ion battery pack is performed based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, and a dynamic priority sequence is generated, specifically including: The health status parameter and the dynamic aging coefficient are set as negative correlation factors for the charge and discharge states of each lithium-ion battery pack. If the current condition is discharge, the state of charge parameter is set as a positive correlation factor for the charge and discharge state of each lithium-ion battery pack; if the current condition is charging, the state of charge parameter is set as a negative correlation factor for the charge and discharge state of each lithium-ion battery pack. Based on the above settings, calculate the priority score for each lithium-ion battery pack. The priority scores are sorted from high to low to generate a dynamic priority sequence.

[0009] In some embodiments, dividing all lithium-ion battery packs into priority response groups and delayed response groups based on the dynamic priority sequence specifically includes: Set dynamic priority threshold; Lithium-ion battery packs with priority scores higher than the dynamic priority threshold in the dynamic priority sequence are classified into priority response groups, and lithium-ion battery packs with priority scores lower than or equal to the dynamic priority threshold are classified into delay response groups. After a preset time interval, dynamic priority evaluation is re-executed and the membership of the priority response group and the delayed response group is updated.

[0010] In some embodiments, determining the total charging and discharging power instruction of the photovoltaic energy storage system based on the photovoltaic power generation, load power, and time-of-use electricity price of the photovoltaic energy storage system at the current time period specifically includes: Obtain the photovoltaic power generation, load power, and time-of-use electricity price of the photovoltaic energy storage system for the current period; Calculate the power difference between the photovoltaic power generation and the load power; Obtain the preset charging and discharging strategy logic; Based on the positive and negative states of the power difference, the time-of-use electricity price of the power grid, and the preset charging and discharging strategy logic, a total charging and discharging power command for the photovoltaic energy storage system is generated.

[0011] In some embodiments, the coordinated control of the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group, and the delay response group specifically includes: When the total charge / discharge power command is detected, the lithium-ion battery packs in the priority response group are activated to perform charge / discharge response; During the response process of the priority response group, it is identified in real time whether there are specific blocks in the group whose dynamic aging coefficient exceeds the preset aging threshold value. If the specific block is identified, the charge / discharge rate of the specific block is actively limited under the constraint of the current total charge / discharge power command to prevent it from aging further. After a preset response delay, the lithium-ion battery modules in the delay response group are activated for charging and discharging to compensate for the power difference caused by the power limitation of the specific module; wherein, the starting power ramp of the delay response group is controlled to be a smooth transition to smooth out the sudden change in total power of the system caused by module switching.

[0012] Secondly, this application provides a charge and discharge management system for a photovoltaic energy storage system, comprising: The acquisition module is used to acquire the real-time health status parameters and state of charge parameters of each lithium-ion battery pack. The processing module is used to determine the dynamic aging coefficient of each lithium-ion battery module based on the voltage plateau segment where the state of charge of the photovoltaic energy storage system is located; The processing module is further configured to perform dynamic priority evaluation of the charge and discharge status of each lithium-ion battery pack based on the health status parameters, the state of charge parameters and the dynamic aging coefficient, generate a dynamic priority sequence, and divide all lithium-ion battery packs into a priority response group and a delayed response group based on the dynamic priority sequence. The processing module is also used to determine the total charging and discharging power command of the photovoltaic energy storage system based on the photovoltaic power generation, load power and grid time-of-use electricity price of the photovoltaic energy storage system in the current period. The execution module is used to coordinate the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group, and the delay response group.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described charge and discharge management method for a photovoltaic energy storage system.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described charging and discharging management method for a photovoltaic energy storage system.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The charging and discharging management system and method for photovoltaic energy storage systems provided in this application firstly acquire real-time health status and state of charge parameters, providing a precise data foundation for subsequent differentiated assessments; secondly, it introduces a mapping relationship between voltage plateau segments and aging acceleration factors, quantifying the nonlinear impact of different charge ranges on battery degradation into a dynamic aging coefficient, thus providing a comparable quantitative standard for aging risk; thirdly, it integrates health status, state of charge, and dynamic aging coefficients into a dynamic priority score, and divides the system into priority response groups and delayed response groups according to the score, strategically placing blocks with severe aging acceleration or poor health status into low-priority or delayed response sequences. The first step involves implementing aging suppression from the scheduling source. The fourth step combines photovoltaic output, load power, and time-of-use pricing to generate a total charge and discharge command, unifying economic scheduling objectives and power balance requirements into a single power command. This provides an execution benchmark that balances economy and safety for coordinated control. The fifth step utilizes coordinated control—priority groups responding first, delayed groups compensating for delays, and actively limiting power in high-aging modules—to reduce the accelerated aging impact of severe voltage plateau sections on weak modules while ensuring power response requirements and economic benefits. Ultimately, this achieves integrated coordinated management of battery aging suppression, power balance response, and economic operation. Using this solution, the charging and discharging of photovoltaic energy storage systems can be coordinated based on the differences in accelerated aging of lithium-ion batteries across different voltage plateau sections. Attached Figure Description

[0016] Figure 1 This is an exemplary flowchart of a charge and discharge management method for a photovoltaic energy storage system according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of the dynamic aging coefficient according to some embodiments of this application; Figure 3 This is a flowchart illustrating the working application of charge and discharge management of a photovoltaic energy storage system according to some embodiments of this application; Figure 4 This is a schematic diagram of the charge and discharge management system of a photovoltaic energy storage system according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a charge and discharge management method for a photovoltaic energy storage system according to some embodiments of this application. Detailed Implementation

[0017] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] refer to Figure 1The figure is an exemplary flowchart of a charge and discharge management method for a photovoltaic energy storage system according to some embodiments of this application. The charge and discharge management method for the photovoltaic energy storage system mainly includes the following steps: In step 101, the real-time health status parameters and state of charge parameters of each lithium-ion battery pack are obtained.

[0019] In some embodiments, obtaining the real-time health and state of charge parameters of each lithium-ion battery pack can be achieved using the following steps: The battery management system collects data on the terminal voltage, operating current, surface temperature, and internal resistance of each lithium-ion battery pack in real time. The real-time state of charge parameters of each lithium-ion battery pack are determined based on the terminal voltage and the operating current. The real-time health status parameters of each lithium-ion battery pack are determined based on the internal resistance data, the surface temperature, and the historical cycle count, wherein the historical cycle count is equivalent to the equivalent number of full charge and discharge cycles at a standard charge-discharge rate and a standard temperature of 25°C.

[0020] In practice, firstly, the battery management system collects the terminal voltage, operating current, surface temperature, and internal resistance data of each lithium-ion battery pack in real time at a preset sampling frequency. The sampling frequency is set according to the system response requirements, for example, 1Hz. The internal resistance data is obtained by short-time pulse excitation measurement during the charge-discharge interval using the hybrid pulse power characteristic test method. Subsequently, based on the real-time collected terminal voltage and operating current, the extended Kalman filter algorithm is used to estimate the state of charge (SOC) of each lithium-ion battery pack. This algorithm is based on the battery equivalent circuit model and iteratively corrects the SOC estimate by using the error between the predicted and measured terminal voltage values ​​to obtain converged real-time SOC parameters. Next, based on the real-time collected internal resistance data and surface temperature, combined with the historical cycle count, a preset capacity decay is used. The model determines the real-time health status parameters of each lithium-ion battery pack. The capacity decay model is established based on the Arrhenius formula and power-law aging law, expressing the internal resistance growth rate and capacity decay rate as functions of temperature and cycle number. The historical cycle number is calculated by using the ampere-hour integral method to determine the cumulative charge and discharge capacity of the battery pack, and then dividing it by the rated capacity to obtain the total cycle number. The temperature acceleration factor and the rate acceleration factor are used to convert the cycle number under different operating conditions into equivalent full charge and discharge cycles at the standard charge and discharge rate and at a standard temperature of 25°C. The temperature acceleration factor is calculated by the Arrhenius equation, and the rate acceleration factor is determined by interpolation based on the rate-life curve provided by the battery manufacturer. The actual cycle number is divided by the product of the temperature acceleration factor and the rate acceleration factor to obtain the equivalent full charge and discharge cycles.

[0021] It should be noted that the health status parameter in this application represents the percentage of the current usable capacity of the lithium-ion battery pack relative to its initial rated capacity, reflecting the degree of capacity decay and internal resistance increase caused by factors such as active lithium loss, electrode material degradation, and solid electrolyte interface film thickening during battery pack use; the state of charge parameter represents the percentage of the current remaining capacity of the lithium-ion battery pack relative to its current usable capacity, reflecting the relationship between the depth of charge / discharge and the remaining usable energy at a specific moment; the battery management system refers to an electronic management unit that performs real-time monitoring, state estimation, safety protection, and equalization control of the lithium-ion battery pack, possessing electrical... It features voltage, current, and temperature acquisition functions, as well as online internal resistance identification. The internal resistance data represents the combined value of ohmic and polarization internal resistance exhibited by the lithium-ion battery pack during charging and discharging. This value is calculated through voltage response under pulse excitation, reflecting changes in the battery's internal conductivity and ion diffusion capability. The historical cycle count represents the total number of charge-discharge cycles completed by the lithium-ion battery pack since its commissioning, reflecting the degree of battery pack lifespan depletion. The equivalent full charge-discharge count represents the number of full charge-discharge cycles converted from actual charging and discharging cycles under different operating conditions to the standard charge-discharge rate and the standard temperature of 25°C, eliminating the influence of operating condition differences on aging evaluation.

[0022] In step 102, the dynamic aging coefficient of each lithium-ion battery module is determined based on the voltage plateau segment where the state of charge of the photovoltaic energy storage system is located.

[0023] In some embodiments, reference Figure 2 The figure is an exemplary flowchart of determining the dynamic aging coefficient in some embodiments of this application. In this embodiment, the dynamic aging coefficient of each lithium-ion battery module is determined based on the voltage plateau segment of the state of charge of the photovoltaic energy storage system, which can be achieved by the following steps: In step 1021, the average state of charge of the photovoltaic energy storage system is obtained, and the current voltage plateau segment corresponding to the average state of charge is determined, wherein the voltage plateau segment includes a low voltage plateau segment, a medium voltage plateau segment, and a high voltage plateau segment. In step 1022, the target aging acceleration factor corresponding to the current voltage platform segment is determined according to the mapping relationship between the pre-calibrated voltage platform segment and the aging acceleration factor of the photovoltaic energy storage system. In step 1023, the dynamic aging coefficient of each lithium-ion battery pack is determined based on the real-time health status parameters of each lithium-ion battery pack and the target aging acceleration factor.

[0024] In specific implementation, obtaining the overall average state of charge (SOC) of the photovoltaic energy storage system and determining the current voltage plateau segment corresponding to the average SOC can be achieved in the following way: Obtain the overall average SOC of the photovoltaic energy storage system by performing an arithmetic average of the real-time SOC parameters of all lithium-ion battery modules to obtain the current average SOC of the photovoltaic energy storage system; then, based on the nonlinear relationship curve between the battery open-circuit voltage and the SOC, the entire charging and discharging voltage range is pre-divided into three voltage plateau segments: the low voltage plateau segment corresponds to the SOC range of 0% to 30%, the medium voltage plateau segment corresponds to the SOC range of 30% to 80%, and the high voltage plateau segment corresponds to the SOC range of 80% to 100%. Based on the calculated average SOC, the segment into which it falls is determined, thus identifying the current voltage plateau segment.

[0025] In addition, in specific implementation, the target aging acceleration factor corresponding to the current voltage plateau segment can be determined based on the mapping relationship between the pre-calibrated voltage plateau segment and the aging acceleration factor of the photovoltaic energy storage system. This can be achieved in the following way: retrieve the mapping relationship between the voltage plateau segment and the aging acceleration factor pre-calibrated by the photovoltaic energy storage system through cyclic aging experiments before commissioning. The calibration process of this mapping relationship is as follows: select lithium-ion battery modules of the same model as samples, and conduct charge-discharge cyclic aging experiments under different temperature conditions in the state of charge ranges corresponding to the low voltage plateau region, medium voltage plateau region, and high voltage plateau region, respectively. The capacity retention rate and internal resistance growth rate of each cycle are recorded in real time. The Arrhenius equation is used to fit the relationship between the capacity decay rate and temperature in each voltage plateau region. The activation energy of the reaction is extracted. The aging rate at 25°C in the medium voltage plateau region is used as the benchmark value. The aging rates at various temperatures in the low voltage plateau region and the high voltage plateau region are compared with the benchmark value. The target aging acceleration factor for each voltage plateau region is obtained by normalization. A one-to-one mapping relationship between voltage plateau regions and aging acceleration factors is established. Then, according to the current voltage plateau region, the corresponding target aging acceleration factor is determined directly by looking up the table from the mapping relationship. Furthermore, the dynamic aging coefficient of each lithium-ion battery pack can be determined based on its real-time health status parameters and the target aging acceleration factor. Specifically, based on the real-time health status parameters and the target aging acceleration factor obtained from a table, the dynamic aging coefficient of each lithium-ion battery pack is determined through weighted calculation. The calculation formula is: Dynamic aging coefficient = Target aging acceleration factor × (1 + (1 − Health status parameter) / Health status parameter × Correction coefficient). The correction coefficient is determined based on the slope of the battery pack's internal resistance growth, with a value ranging from 0.1 to 0.5. This formula further amplifies the dynamic aging coefficient of battery packs with poorer health status compared to the target aging acceleration factor. Since the health status parameter is a percentage, a smaller health status parameter corresponds to a larger (1 − Health status parameter) value, resulting in a non-linear growth trend in the dynamic aging coefficient, accurately reflecting the actual physical characteristics of accelerated degradation of aging batteries under harsh voltage platforms.

[0026] It should be noted that the voltage plateau region in this application refers to several characteristic intervals into which the entire charge-discharge voltage range is divided based on the nonlinear relationship between the open-circuit voltage and the state of charge of a lithium-ion battery. Significant differences exist in the electrochemical reaction rate, side reaction activity, and aging kinetics within each interval. The low voltage plateau region represents the voltage range corresponding to a lower state of charge, where the negative electrode graphite interlayer spacing changes significantly, the solid electrolyte interface film repeatedly breaks and repairs, and the risk of lithium dendrite precipitation increases. The medium voltage plateau region represents the voltage range corresponding to a middle state of charge, where the battery's operating state is relatively stable, internal resistance and polarization are moderate, and the side reaction rate is low. The high voltage plateau region represents the voltage range corresponding to a higher state of charge. The corresponding voltage range indicates that the cathode material is highly oxidizable, increasing the risk of electrolyte decomposition and accelerating the dissolution of transition metal ions. The aging acceleration factor represents the aging rate multiple of the lithium-ion battery pack under a specific voltage plateau range relative to the standard operating conditions of the medium voltage plateau range, reflecting the nonlinear accelerating effect of different state of charge ranges on battery life. The target aging acceleration factor represents the value of the aging acceleration factor corresponding to the voltage plateau range where the current average state of charge is located, providing a basic reference for calculating the dynamic aging coefficient. The dynamic aging coefficient is a quantitative indicator of the current aging acceleration degree of the lithium-ion battery pack, reflecting the accelerating trend of capacity decay and internal resistance increase of the battery pack under specific operating conditions. The larger the value, the faster the aging rate of the pack under the current conditions.

[0027] In step 103, the charge and discharge states of each lithium-ion battery pack are dynamically prioritized based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, generating a dynamic priority sequence, and all lithium-ion battery packs are divided into a priority response group and a delayed response group based on the dynamic priority sequence.

[0028] In some embodiments, the dynamic priority evaluation of the charge / discharge state of each lithium-ion battery pack based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, and the generation of a dynamic priority sequence, can be achieved through the following steps: The health status parameter and the dynamic aging coefficient are set as negative correlation factors for the charge and discharge states of each lithium-ion battery pack. If the current condition is discharge, the state of charge parameter is set as a positive correlation factor for the charge and discharge state of each lithium-ion battery pack; if the current condition is charging, the state of charge parameter is set as a negative correlation factor for the charge and discharge state of each lithium-ion battery pack. Based on the above settings, calculate the priority score for each lithium-ion battery pack. The priority scores are sorted from high to low to generate a dynamic priority sequence.

[0029] In practice, firstly, the real-time health status parameters, state of charge parameters, and dynamic aging coefficients of each lithium-ion battery module are normalized. The original values ​​of each parameter are mapped to the [0,1] interval using the min-max normalization method. The normalization formula is: Normalized value = (Original value - Minimum value of this parameter among all modules) / (Maximum value of this parameter among all modules - Minimum value of this parameter among all modules). Subsequently, the evaluation polarity of each parameter is determined. The larger the normalized value of the health status parameter, the better the battery condition. However, it needs to be given a negative correlation attribute in the priority scoring, that is, the module with a better health status has a greater positive contribution to the scoring. A higher normalized aging coefficient indicates more severe accelerated aging, thus it is assigned a negative correlation attribute. This means that modules with higher dynamic aging coefficients have a greater negative contribution to the scoring. The state of charge (SOC) parameter's evaluation polarity is determined based on the current charging and discharging conditions of the photovoltaic energy storage system. If the system is in a discharging state, the SOC parameter acts as a positive correlation factor; a higher normalized SOC value indicates more dischargeable capacity and a greater positive contribution to the priority scoring, thereby increasing the priority of high SOC modules in discharging. Conversely, if the system is in a charging state, the SOC parameter acts as a negative correlation factor; a lower normalized SOC value indicates more rechargeable capacity and a greater positive contribution to the priority scoring. The greater the contribution, the higher the priority of low-charge-state modules participating in charging, i.e., (1 − normalized state of charge value) is used as input in the actual calculation. Next, the normalized health state value, normalized dynamic aging coefficient, and normalized state of charge value, which determine the evaluation polarity, are substituted into a preset weighted scoring formula to calculate the priority score for each lithium-ion battery module. The weighted scoring formula is: Priority Score = W1 × Normalized Health State Value + W2 × (1 − Normalized Dynamic Aging Coefficient) + W3 × Normalized State of Charge Value after Operating Condition Adjustment, where W1 is the health state weight, W2 is the dynamic aging coefficient weight, and W3 is the state of charge weight, and the sum of the weights of the three is 1. When the average state of charge of the photovoltaic energy storage system is in the high-voltage plateau region or the low-voltage plateau region, the value of W2 automatically increases, making the aging suppression strategy play a dominant role in the priority ranking. Specifically, in the medium-voltage plateau region, W1 is 0.35, W2 is 0.35, and W3 is 0.30; in the high-voltage plateau region or the low-voltage plateau region, W1 is 0.25, W2 is 0.55, and W3 is 0.20. By adjusting the weight allocation, the priority score of the blocks with poor health and severe aging acceleration in the two voltage plateau regions is greatly reduced. Finally, the priority scores of all lithium-ion battery blocks are sorted from high to low to generate the dynamic priority sequence at the current moment.

[0030] It should be noted that, in this application, the negative correlation factor indicates that in the priority score calculation, the larger the value of this parameter, the greater the negative impact on the final score result; the positive correlation factor indicates that in the priority score calculation, the larger the value of this parameter, the greater the positive impact on the final score result; the priority score represents a numerical value that quantifies the priority of lithium-ion battery modules in participating in the charge and discharge response at the current moment, with a higher score indicating that the module should participate in the charge and discharge response with greater priority; the dynamic priority sequence represents a sequence formed by arranging all lithium-ion battery modules according to their priority scores from high to low, reflecting the priority order of each battery module participating in charge and discharge at the current moment, and this sequence is updated in real time with the system operating conditions.

[0031] In some embodiments, dividing all lithium-ion battery packs into priority response groups and delayed response groups based on the dynamic priority sequence can be achieved using the following steps: Set dynamic priority threshold; Lithium-ion battery packs with priority scores higher than the dynamic priority threshold in the dynamic priority sequence are classified into priority response groups, and lithium-ion battery packs with priority scores lower than or equal to the dynamic priority threshold are classified into delay response groups. After a preset time interval, dynamic priority evaluation is re-executed and the membership of the priority response group and the delayed response group is updated.

[0032] In practice, a dynamic priority threshold is set based on the amplitude of the current total charge and discharge power command. When the amplitude of the total charge and discharge power command is large, more lithium-ion battery modules are needed to participate in the charge and discharge response. At this time, the dynamic priority threshold is appropriately lowered so that more modules can enter the priority response group to meet the high power demand. The calculation formula for the dynamic priority threshold is: Dynamic priority threshold = Base threshold × Power command adjustment coefficient. The base threshold is taken as the average value of the priority scores in the historical operating data. The power command adjustment coefficient is determined according to the proportion of the total charge and discharge power command amplitude to the total rated power of the system. The larger the proportion, the smaller the adjustment coefficient. The value range is from 0.5 to 1.0, so that the threshold is lower when the power demand is greater and there are more members in the priority response group.

[0033] In addition, in the specific implementation, the priority score of each lithium-ion battery block in the dynamic priority sequence is compared with the dynamic priority threshold one by one. Blocks with scores higher than the dynamic priority threshold are determined to be members of the priority response group, and blocks with scores lower than or equal to the dynamic priority threshold are determined to be members of the delayed response group.

[0034] It should be noted that a preset time interval is set, which is determined based on the system load fluctuation frequency and the photovoltaic power generation change rate, with a value ranging from 5 minutes to 30 minutes. After each preset time interval, the real-time parameters of each lithium-ion battery module are collected again, the dynamic aging coefficient and priority score are recalculated, the dynamic priority sequence is regenerated, and the membership of the priority response group and the delayed response group is updated. This enables the dynamic rotation of group members according to the system operating status, avoiding the same module from being in a heavy load state of priority response for a long time, which would accelerate aging.

[0035] Additionally, it should be noted that the dynamic priority threshold in this application represents the priority score boundary value used to divide the priority response group and the delayed response group. This threshold is dynamically adjusted according to the amplitude of the current total charge / discharge power command. The power command adjustment coefficient represents the correction factor of the total charge / discharge power command amplitude on the dynamic priority threshold, reflecting the correspondence between the power demand and the group size. The priority response group represents the set of lithium-ion battery blocks with higher priority scores in the dynamic priority sequence that should participate in the charge / discharge response first. These blocks typically have a good health condition, a small dynamic aging coefficient, and a state of charge that matches the current operating conditions. The delayed response group represents the set of lithium-ion battery blocks with lower priority scores in the dynamic priority sequence that should participate in the charge / discharge response later. These blocks typically have a poor health condition or a large dynamic aging coefficient, and their delayed call is used to slow down their aging process. The preset time interval represents the time interval between two dynamic priority assessments, used to control the frequency of group updates.

[0036] In step 104, the total charging and discharging power command of the photovoltaic energy storage system is determined based on the photovoltaic power generation, load power and grid time-of-use electricity price of the photovoltaic energy storage system in the current period.

[0037] In some embodiments, determining the total charging and discharging power command of the photovoltaic energy storage system based on the photovoltaic power generation, load power, and time-of-use electricity price of the photovoltaic energy storage system in the current period can be achieved through the following steps: Obtain the photovoltaic power generation, load power, and time-of-use electricity price of the photovoltaic energy storage system for the current period; Calculate the power difference between the photovoltaic power generation and the load power; Obtain the preset charging and discharging strategy logic; Based on the positive and negative states of the power difference, the time-of-use electricity price of the power grid, and the preset charging and discharging strategy logic, a total charging and discharging power command for the photovoltaic energy storage system is generated.

[0038] In practice, the output power of the photovoltaic array during the current period is obtained in real time through the communication interface of the photovoltaic inverter as the photovoltaic power generation power. The power load during the current period is obtained in real time through the smart meter on the load side. The time-of-use electricity price information of the grid during the current period is obtained from the grid energy management system through the communication module. The time-of-use electricity price information includes the specific time period division of peak electricity price, normal electricity price and valley electricity price and the corresponding unit price of electricity for each time period.

[0039] In addition, in specific implementation, the power difference between photovoltaic power generation and load power is calculated. Power difference = photovoltaic power generation - load power. When the power difference is positive, it means that the photovoltaic power generation is greater than the load power, and the system has surplus power that can be fed to the grid or charged to the battery. When the power difference is negative, it means that the photovoltaic power generation is less than the load power, and the system has a power gap that needs to be filled by discharging the battery or drawing power from the grid.

[0040] In addition, during the specific implementation, a preset charging and discharging strategy logic is invoked. This logic comprehensively determines the magnitude and direction of charging and discharging power based on the positive or negative state of the power difference and the time-of-use electricity price of the grid. Specifically, the charging and discharging strategy logic is as follows: when the power difference is positive and it is during peak electricity price periods, surplus power is prioritized to feed power back to the grid to obtain higher returns, and the total charging and discharging power command is a discharging command; when the power difference is positive and it is during off-peak electricity price periods, surplus power is prioritized to charge the lithium-ion battery packs to store low-priced energy, and the total charging and discharging power command is a charging command; when the power difference is negative and it is during peak electricity price periods, the lithium-ion battery packs are prioritized to discharge to compensate for the power gap and avoid purchasing electricity at high prices, and the total charging and discharging power command is a discharging command; when the power difference is negative and it is during off-peak electricity price periods, low-priced electricity is prioritized to draw power from the grid while charging the battery packs, and the total charging and discharging power command is a charging command; when it is during normal electricity price periods, the positive or negative value of the power difference directly determines the charging and discharging direction.

[0041] In addition, in specific implementation, according to the above charging and discharging strategy logic, the total charging and discharging power command for the current moment is output in each control cycle. The amplitude of the command is limited by the absolute value of the power difference combined with the upper limit of the total charging and discharging power of the battery pack. The limiting process is as follows: if the absolute value of the power difference exceeds the preset proportion of the total rated power of the lithium-ion battery pack that can participate in charging and discharging, for example, 80%, then the amplitude of the total charging and discharging power command is limited to 80% of the total rated power to prevent the battery pack from overloaded.

[0042] It should be noted that, in this application, photovoltaic power generation refers to the DC power output of the photovoltaic array in the photovoltaic energy storage system at the current moment, reflecting the power generation capacity jointly determined by solar irradiance, ambient temperature, and photovoltaic module conversion efficiency; load power refers to the power consumed by the load connected to the photovoltaic energy storage system at the current moment, reflecting the actual electricity demand on the user side; grid time-of-use price refers to the differentiated electricity pricing system formulated by the grid company based on peak and off-peak electricity periods, reflecting the power supply and demand relationship and power supply cost at different times; power difference refers to the difference between photovoltaic power generation and load power, reflecting the net power surplus or deficit state of the photovoltaic energy storage system at the current moment; total charge and discharge power command refers to the target value of charge and discharge power issued by the photovoltaic energy storage system to all lithium-ion battery modules in the current control cycle, with positive values ​​indicating discharge commands and negative values ​​indicating charge commands. This command integrates power balance requirements and economic optimization objectives.

[0043] In step 105, the charging and discharging of the photovoltaic energy storage system is coordinated and controlled by the total charging and discharging power command, the priority response group, and the delay response group.

[0044] In some embodiments, the coordinated control of the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group, and the delay response group can be achieved by the following steps: When the total charge / discharge power command is detected, the lithium-ion battery packs in the priority response group are activated to perform charge / discharge response; During the response process of the priority response group, it is identified in real time whether there are specific blocks in the group whose dynamic aging coefficient exceeds the preset aging threshold value. If the specific block is identified, the charge / discharge rate of the specific block is actively limited under the constraint of the current total charge / discharge power command to prevent it from aging further. After a preset response delay, the lithium-ion battery modules in the delay response group are activated for charging and discharging to compensate for the power difference caused by the power limitation of the specific module; wherein, the starting power ramp of the delay response group is controlled to be a smooth transition to smooth out the sudden change in total power of the system caused by module switching.

[0045] In practice, the system monitors in real time whether the energy management system issues new total charge and discharge power commands. Once a command update is detected, the total charge and discharge power command is immediately used as the input target value for coordinated control. According to the dynamic priority sequence, all lithium-ion battery blocks in the priority response group are activated and participate in the charge and discharge response. The power allocation method is as follows: within the priority response group, power is allocated according to the priority score of each member block from high to low. The higher the priority score, the larger the share of charge and discharge power allocated. The power allocated to each block = total charge and discharge power command × (priority score of the block / sum of priority scores of all blocks in the priority response group). At the same time, it is subject to the power limit constraints corresponding to the rated power and health status parameters of each block. For blocks whose health status parameters are lower than the preset health threshold, for example, health status parameters are lower than 80%, their maximum allowable charge and discharge rate is reduced proportionally. The reduction coefficient is the ratio of the health status parameter to the preset health threshold.

[0046] In addition, during the charging and discharging process of the priority response group, the dynamic aging coefficient of each lithium-ion battery block in the group is monitored in real time. The real-time dynamic aging coefficient of each block is compared with a preset aging threshold value. The preset aging threshold value is set according to the system's tolerance for the aging rate of the battery blocks, and the value ranges from 1.5 to 3.0. When the dynamic aging coefficient of a specific block exceeds the preset aging threshold value, it is determined that the specific block is in an accelerated aging state, triggering protective power limiting measures to actively limit the charging and discharging rate of the specific block. The limiting method is to multiply the original allocated power by a limiting coefficient, where the limiting coefficient = preset aging threshold value / real-time dynamic aging coefficient of the specific block. This makes the power limiting range greater for blocks with higher dynamic aging coefficients, thereby reducing the electrochemical stress on the block and preventing it from aging further.

[0047] It should be noted that the specific implementation steps of the preset aging threshold value can be described as follows: By querying the cycle aging test data of the same type of lithium-ion battery pack at standard charge-discharge rate and standard temperature of 25℃, obtain the statistical value of the dynamic aging coefficient corresponding to the capacity decay to 80% of the initial capacity in the medium voltage plateau range, and use this as the benchmark aging tolerance value; according to the tolerance requirements of the photovoltaic energy storage system for the aging rate of the battery pack in actual operation, multiply the benchmark aging tolerance value by a tolerance adjustment factor. The tolerance adjustment factor ranges from 0.6 to 1.2. When the system has a low tolerance for aging rate, a higher value is used. The smaller value is used to trigger protective power limiting measures earlier, while a larger value is used when the tolerance is high to allow the battery module to undertake more power tasks. The benchmark aging tolerance value is modified by section based on the target aging acceleration factor of the voltage plateau section where the current photovoltaic energy storage system is located. The section correction coefficient is equal to the ratio of the target aging acceleration factor of the current voltage plateau section to the target aging acceleration factor of the medium voltage plateau section. The benchmark aging tolerance value multiplied by the tolerance adjustment factor and divided by the section correction coefficient is used as the preset aging threshold value under the current operating conditions, so that the threshold value can be adaptively adjusted with the system tolerance setting and the voltage plateau section.

[0048] In addition, in specific implementation, if the total charging and discharging power of the priority response group is insufficient to meet the requirements of the total charging and discharging power command due to the power limitation of a specific block, the current power difference is calculated. The power difference = the amplitude of the total charging and discharging power command - the total power actually output by the priority response group. A preset response delay is set. This preset response delay is determined based on the allowable fluctuation range of system power change and the response time constant of the lithium-ion battery block. The value range is 0.5 seconds to 5 seconds. After the preset response delay, when the power difference exceeds the trigger threshold, the lithium-ion battery block in the delayed response group is started to charge and discharge to compensate for the power difference. The power allocation within the delayed response group is also allocated from high to low according to the priority score of each member block. The power allocated to each block does not exceed the power limit corresponding to its own rated power and health status parameters.

[0049] In addition, in specific implementation, when the delay response group is put into operation, its starting power ramp is controlled to be a smooth transition. The slope of the power ramp is set according to the maximum power change rate allowed by the system, and the value range is 10% to 50% of the rated power per second. Through power limiting and low-pass filtering smoothing, the output power of the delay response group gradually rises from zero to the target value, avoiding sudden changes in the total power of the system caused by the instantaneous full power input of the module, and ensuring the power stability of the photovoltaic energy storage system and the grid interaction point.

[0050] It should be noted that the preset aging threshold value in this application represents the critical value of the dynamic aging coefficient for determining whether a lithium-ion battery pack is in an accelerated aging state. When the dynamic aging coefficient exceeds this threshold value, the system determines that the pack faces an excessively high aging risk and requires protective power limiting measures. A specific pack refers to a lithium-ion battery pack within the priority response group whose dynamic aging coefficient exceeds the preset aging threshold value; this pack's aging rate under current operating conditions is significantly higher than normal. The charge / discharge rate represents the ratio of the lithium-ion battery pack's charge / discharge current to its rated capacity, reflecting the charge / discharge intensity of the battery pack. The response delay represents the time from the start of the priority response group's response to the delayed response time. The time interval between the start-up of the two battery groups is used to coordinate the timing of their activation. The start-up power ramp represents the process curve of the output power of the delayed response group gradually increasing from zero to the target value. A smooth transition is achieved by controlling the slope of the power change. The total power surge represents the rapid jump in the system output power caused by the instantaneous activation or deactivation of the battery group, which may have an adverse impact on the power quality of the grid. Cooperative control represents a comprehensive control strategy that coordinates the activation timing, power allocation, and power change rate of the priority response group and the delayed response group to jointly complete the charging and discharging tasks required by the total charging and discharging power command, while also taking into account the aging suppression of the battery groups and the stability of the system power.

[0051] In some embodiments, reference Figure 3 This figure is a flowchart illustrating the working application of the photovoltaic energy storage system's charge and discharge management in some embodiments of this application. As shown in the figure, 110 represents a photovoltaic energy storage operation scenario containing multiple lithium-ion battery modules. The health status, state of charge, and system photovoltaic power generation and load power of each battery module change dynamically during the charge and discharge process. 120 is a data acquisition unit that collects the health status and state of charge parameters of each battery module in real time, and simultaneously obtains the photovoltaic power generation, load power, and grid time-of-use electricity price data for the current period. After the collected data is transmitted to the control processing server at 130, the server sequentially completes core logic processing such as dynamic aging coefficient calculation, battery module charge and discharge priority evaluation and grouping, and total charge and discharge power command generation. Finally, the charge and discharge control terminal at 140 realizes refined and coordinated charge and discharge control of the photovoltaic energy storage system based on the commands and grouping results.

[0052] In another aspect, in some embodiments, this application provides a charge and discharge management system for a photovoltaic energy storage system, with reference to... Figure 4 The figure is a schematic diagram of the structure of the charge and discharge management system of a photovoltaic energy storage system according to some embodiments of this application. The charge and discharge management system 400 of the photovoltaic energy storage system includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire the real-time health status parameters and charge status parameters of each lithium-ion battery pack. Processing module 402, in this application, is used to determine the dynamic aging coefficient of each lithium-ion battery module based on the voltage plateau segment where the state of charge of the photovoltaic energy storage system is located. It should be noted that the processing module 402 in this application is also used to perform dynamic priority evaluation of the charge and discharge state of each lithium-ion battery pack based on the health state parameters, the state of charge parameters and the dynamic aging coefficient, generate a dynamic priority sequence, and divide all lithium-ion battery packs into a priority response group and a delayed response group based on the dynamic priority sequence. In addition, it should be noted that the processing module 402 in this application is also used to determine the total charging and discharging power command of the photovoltaic energy storage system based on the photovoltaic power generation, load power and grid time-of-use electricity price of the photovoltaic energy storage system in the current period. The execution module 403 in this application is mainly used to coordinate the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group and the delay response group.

[0053] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described charge and discharge management method for a photovoltaic energy storage system.

[0054] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a charge / discharge management method for a photovoltaic energy storage system according to some embodiments of this application. The charge / discharge management method for the photovoltaic energy storage system in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0055] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0056] The communication bus 502 can be used to transmit information between the aforementioned components.

[0057] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0058] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0059] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0060] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0061] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0062] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described charging and discharging management method for a photovoltaic energy storage system.

[0063] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0064] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A charging and discharging management method for a photovoltaic energy storage system, wherein the photovoltaic energy storage system comprises multiple lithium-ion battery modules, characterized in that, The method includes the following steps: Obtain real-time health and state of charge parameters for each lithium-ion battery pack; The dynamic aging coefficient of each lithium-ion battery module is determined based on the voltage plateau range of the state of charge of the photovoltaic energy storage system. Based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, the charge and discharge states of each lithium-ion battery pack are dynamically prioritized to generate a dynamic priority sequence, and all lithium-ion battery packs are divided into a priority response group and a delayed response group based on the dynamic priority sequence. The total charging and discharging power command of the photovoltaic energy storage system is determined based on the photovoltaic power generation, load power and time-of-use electricity price of the photovoltaic energy storage system in the current period. The charging and discharging of the photovoltaic energy storage system is coordinated and controlled by the total charging and discharging power command, the priority response group, and the delay response group.

2. The method as described in claim 1, characterized in that, The real-time health and state of charge parameters for each lithium-ion battery pack include: The battery management system collects data on the terminal voltage, operating current, surface temperature, and internal resistance of each lithium-ion battery pack in real time. The real-time state of charge parameters of each lithium-ion battery pack are determined based on the terminal voltage and the operating current. The real-time health status parameters of each lithium-ion battery pack are determined based on the internal resistance data, the surface temperature, and the historical cycle count, wherein the historical cycle count is equivalent to the equivalent number of full charge and discharge cycles at a standard charge-discharge rate and a standard temperature of 25°C.

3. The method as described in claim 1, characterized in that, The dynamic aging coefficient of each lithium-ion battery module is determined based on the voltage plateau range of the photovoltaic energy storage system's state of charge. Specifically, this includes: The average state of charge of the photovoltaic energy storage system is obtained, and the current voltage plateau segment corresponding to the average state of charge is determined, wherein the voltage plateau segment includes a low voltage plateau segment, a medium voltage plateau segment, and a high voltage plateau segment. Based on the mapping relationship between the pre-calibrated voltage plateau segment and the aging acceleration factor of the photovoltaic energy storage system, the target aging acceleration factor corresponding to the current voltage plateau segment is determined. Based on the real-time health status parameters of each lithium-ion battery pack and the target aging acceleration factor, the dynamic aging coefficient of each lithium-ion battery pack is determined.

4. The method as described in claim 1, characterized in that, Based on the health status parameters, the state of charge parameters, and the dynamic aging coefficient, the charge and discharge states of each lithium-ion battery pack are dynamically prioritized, and a dynamic priority sequence is generated, specifically including: The health status parameter and the dynamic aging coefficient are set as negative correlation factors for the charge and discharge states of each lithium-ion battery pack. If the current condition is discharge, the state of charge parameter is set as a positive correlation factor for the charge and discharge state of each lithium-ion battery pack; if the current condition is charging, the state of charge parameter is set as a negative correlation factor for the charge and discharge state of each lithium-ion battery pack. Based on the above settings, calculate the priority score for each lithium-ion battery pack. The priority scores are sorted from high to low to generate a dynamic priority sequence.

5. The method as described in claim 1, characterized in that, Based on the dynamic priority sequence, all lithium-ion battery packs are divided into priority response groups and delayed response groups, specifically including: Set dynamic priority threshold; Lithium-ion battery packs with priority scores higher than the dynamic priority threshold in the dynamic priority sequence are classified into priority response groups, and lithium-ion battery packs with priority scores lower than or equal to the dynamic priority threshold are classified into delay response groups. After a preset time interval, dynamic priority evaluation is re-executed and the membership of the priority response group and the delayed response group is updated.

6. The method as described in claim 1, characterized in that, The instruction to determine the total charging and discharging power of the photovoltaic energy storage system based on the current photovoltaic power generation, load power, and time-of-use electricity price of the grid specifically includes: Obtain the photovoltaic power generation, load power, and time-of-use electricity price of the photovoltaic energy storage system for the current period; Calculate the power difference between the photovoltaic power generation and the load power; Obtain the preset charging and discharging strategy logic; Based on the positive and negative states of the power difference, the time-of-use electricity price of the power grid, and the preset charging and discharging strategy logic, a total charging and discharging power command for the photovoltaic energy storage system is generated.

7. The method as described in claim 1, characterized in that, The coordinated control of the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group, and the delay response group specifically includes: When the total charge / discharge power command is detected, the lithium-ion battery packs in the priority response group are activated to perform charge / discharge response; During the response process of the priority response group, it is identified in real time whether there are specific blocks in the group whose dynamic aging coefficient exceeds the preset aging threshold value. If the specific block is identified, the charge / discharge rate of the specific block is actively limited under the constraint of the current total charge / discharge power command to prevent it from aging further. After a preset response delay, the lithium-ion battery modules in the delay response group are activated for charging and discharging to compensate for the power difference caused by the power limitation of the specific module; wherein, the starting power ramp of the delay response group is controlled to be a smooth transition to smooth out the sudden change in total power of the system caused by module switching.

8. A charge and discharge management system for a photovoltaic energy storage system, characterized in that, include: The acquisition module is used to acquire the real-time health status parameters and state of charge parameters of each lithium-ion battery pack. The processing module is used to determine the dynamic aging coefficient of each lithium-ion battery module based on the voltage plateau segment where the state of charge of the photovoltaic energy storage system is located; The processing module is further configured to perform dynamic priority evaluation of the charge and discharge status of each lithium-ion battery pack based on the health status parameters, the state of charge parameters and the dynamic aging coefficient, generate a dynamic priority sequence, and divide all lithium-ion battery packs into a priority response group and a delayed response group based on the dynamic priority sequence. The processing module is also used to determine the total charging and discharging power command of the photovoltaic energy storage system based on the photovoltaic power generation, load power and grid time-of-use electricity price of the photovoltaic energy storage system in the current period. The execution module is used to coordinate the charging and discharging of the photovoltaic energy storage system through the total charging and discharging power command, the priority response group, and the delay response group.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the charge / discharge management method for the photovoltaic energy storage system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the charging and discharging management method of the photovoltaic energy storage system as described in any one of claims 1 to 7.

Images