Novel intelligent energy storage power station energy storage adjusting method and system

Abstract

The invention relates to the technical field of energy storage adjustment, in particular to a novel intelligent energy storage power station energy storage adjustment method and system. According to module internal resistance and module capacity distribution at different moments, the charging and discharging loss coefficient of each power storage module at each moment is obtained; combining the module output power and the module temperature of each power storage module at each moment to obtain a health index of each power storage module at each moment; obtaining the power grid load degree at each moment according to the power grid output power and the power grid frequency at each moment; according to the power grid load degree distribution at different moments, the power grid load change degree at the next moment relative to the real-time moment is obtained; the power grid load bearing weight of each power storage module at the next moment is obtained, and the load distribution power of each power storage module at the next moment is obtained; and the stored energy is adjusted. According to the method, the energy storage effect is improved by obtaining the appropriate power distributed to the power grid load by each power storage module.

Description

Technical Field

[0001] The present invention relates to the field of energy storage regulation technology, and in particular to a novel intelligent energy storage power station energy storage regulation method and system. Background Art

[0002] Smart energy storage power stations achieve optimal power distribution and energy storage control through real-time monitoring and intelligent scheduling of power generation and storage modules. They are used to balance grid loads, store renewable energy such as solar and wind energy, and improve the reliability and flexibility of the power system, while providing stable power support for the grid.

[0003] In the prior art, according to the analysis of historical data, a fixed power supply is set based on experience to provide electric energy to the grid through an internal energy storage module. However, since the initial performance of different energy storage modules is not completely consistent, the inconsistent charging and discharging patterns during long-term use lead to differences in aging conditions. Using a fixed power supply will cause the energy storage module to overload and reduce frequency, resulting in poor energy storage effect. Summary of the invention

[0004] In order to solve the technical problem that the initial performance of different power storage modules is not completely consistent and the fixed power supply power leads to poor energy storage effect, the purpose of the present invention is to provide a new intelligent energy storage power station energy storage regulation method and system, and the technical solution adopted is as follows: The present invention proposes a novel intelligent energy storage power station energy storage regulation method, the method comprising: Acquire multi-dimensional parameters of different power storage modules in the energy storage power station, as well as grid output power and grid frequency in a time sequence, wherein the multi-dimensional parameters at least include module output power, module capacity, module internal resistance, and module temperature; According to the module internal resistance and module capacity distribution of each power storage module at different times, the charge and discharge loss coefficient of each power storage module at each time is obtained; according to the module output power, module temperature and charge and discharge loss coefficient of each power storage module at each time, the health index of each power storage module at each time is obtained; According to the grid output power and grid frequency at each moment, the grid load degree at each moment is obtained; according to the grid load degree distribution at different moments, the grid predicted load degree corresponding to the next moment at the real time is obtained, and the degree of grid load change at the next moment is obtained; According to the health indicators of different power storage modules at real time and the degree of change of the power grid load at the next moment, the power grid load weight of each power storage module at the next moment is obtained; according to the power grid load weight of each power storage module at the next moment and the preset power grid load demand power, the load distribution power of each power storage module at the next moment is obtained; The energy storage is adjusted according to the load distribution power of each storage module at the next moment.

[0005] Furthermore, the method for obtaining the charge and discharge loss coefficient includes: Obtain the nominal capacity of the power storage module; obtain the difference between the nominal capacity of the power storage module and the maximum capacity of the module, and calculate the ratio of the difference result and the nominal capacity as the capacity attenuation rate; The capacity attenuation rate of each energy storage module and the internal resistance of the module at each moment are combined to obtain the charge and discharge loss coefficient of each energy storage module at each moment.

[0006] Furthermore, the method for obtaining the health index includes: Normalizing the module output power of the power storage module at each moment as the first health coefficient; The module temperature and charge-discharge loss coefficient are integrated and negatively correlated to obtain the second health coefficient. The product of the first health coefficient and the second health coefficient is obtained as the health indicator of the power storage module at each moment.

[0007] Furthermore, the method for acquiring the power grid load degree includes: The ratio between the grid output power and the grid frequency at each moment is obtained as the grid load degree at each moment.

[0008] Furthermore, the method for obtaining the predicted load degree of the power grid includes: According to the difference distribution of the power grid load degree between different times, the power grid load trend value at each moment is obtained; The power grid load degree at the real time is gain-adjusted according to the power grid load trend value at the real time to obtain the power grid predicted load degree corresponding to the next moment at the real time.

[0009] Furthermore, the method for acquiring the power grid load trend value includes: The difference in the power grid load between the next moment and the previous moment within the neighborhood range of each moment is obtained as the load difference; the average of the load difference between all moments is obtained and normalized as the power grid load trend value at each moment.

[0010] Furthermore, the method for obtaining the degree of change of the power grid load includes: The degree of grid load change is obtained according to the deviation of the grid load predicted at the next moment relative to the grid load at the real moment.

[0011] Furthermore, the method for obtaining the power grid load weight includes: The health index of each power storage module at the real time, the preset maximum output power and the degree of change of the grid load at the next moment are integrated to obtain the local load bearing degree of each power storage module at the next moment; Obtain the average of the grid load bearing weights of all power storage modules at the next moment as the overall load bearing level; The ratio of the local load bearing degree and the overall load bearing level of each power storage module at the next moment is obtained as the grid load bearing weight of each power storage module at the next moment.

[0012] Furthermore, the method for acquiring the load distribution power includes: The product of the grid load weight of the power storage module at the next moment and the preset load power demand is obtained to obtain the load distribution power at the next moment.

[0013] The present invention also proposes a new type of intelligent energy storage power station energy storage regulation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the new type of intelligent energy storage power station energy storage regulation method.

[0014] The present invention has the following beneficial effects: The present invention obtains the charge and discharge loss coefficient of each power storage module at each moment according to the module internal resistance and module capacity distribution of each power storage module at different moments, which is helpful to evaluate the energy loss of the power storage module during the charge and discharge process; obtains the health index of each power storage module at each moment according to the module output power, module temperature and charge and discharge loss coefficient of each power storage module at each moment, and comprehensively evaluates the health status of the power storage module; obtains the power grid load degree at each moment according to the power grid output power and power grid frequency at each moment, and accurately judges the load condition of the power grid; obtains the real-time corresponding to the next moment according to the power grid load degree distribution at different moments The predicted load degree of the power grid at the moment, and the degree of change of the power grid load at the next moment are obtained. The prediction is based on historical data, and the load change of the power grid can be understood in advance; according to the health indicators of different power storage modules at the real time and the degree of change of the power grid load corresponding to the next moment, the power grid load bearing weight of each power storage module at the next moment is obtained to ensure the fairness and efficiency of load distribution; according to the power grid load bearing weight of each power storage module at the next moment and the preset power grid load demand power, the load distribution power of each power storage module at the next moment is obtained, dynamic power distribution is realized, and the load of each power storage module is balanced; the energy storage is adjusted. The present invention improves the energy storage effect by obtaining the appropriate power for each power storage module to distribute the power to the power grid load. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a novel intelligent energy storage power station energy storage regulation method provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining load bearing weight provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a new type of intelligent energy storage power station energy storage regulation method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a novel intelligent energy storage power station energy storage regulation method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a method flow chart of a novel intelligent energy storage power station energy storage regulation method provided by an embodiment of the present invention, specifically comprising: Step S1: Acquire multi-dimensional parameters of different power storage modules in the energy storage power station, as well as grid output power and grid frequency in a time sequence, wherein the multi-dimensional parameters at least include module output power, module capacity, module internal resistance and module temperature.

[0021] The electric energy in the new intelligent energy storage power station is converted from natural energy into direct current through a power generation device and the direct current is stored in the power storage device, and the direct current is converted into alternating current through a conversion device and then input into the power grid, wherein the energy storage device includes multiple energy storage modules to provide the required energy storage and output capacity for the power storage station, which is the core part affecting the performance and efficiency of the power storage station; in the embodiment of the present invention, in order to ensure the power generation stability of the intelligent energy storage power station, a method for regulating energy storage is proposed, and it is necessary to analyze the data during the operation of the energy storage power station. First, the operating data such as current, voltage, output power and physical parameters such as internal resistance, temperature and capacity of different power storage modules in the energy storage power station are obtained through relevant sensors, and the power grid related data is obtained through the monitoring system on the power grid side, and the status of the power storage module and the power grid is observed; the multi-dimensional parameters of different power storage modules in the energy storage power station, as well as the power grid output power and power grid frequency are obtained in time sequence, and the multi-dimensional parameters at least include module output power, module capacity, module internal resistance and module temperature.

[0022] It should be noted that in the embodiment of the present invention, at each moment, if there are multiple sensors of the same type measuring the corresponding parameters of the power storage module, the sensor data of the same type are averaged as the value of the corresponding parameter at each moment.

[0023] It should be noted that, in the embodiments of the present invention, in order to facilitate subsequent processing of the data and avoid differences between the units and magnitudes of the values ​​of the data, the data is standardized to eliminate the influence of the dimension in the data calculation.

[0024] Step S2: According to the module internal resistance and module capacity distribution of each storage module at different times, the charge and discharge loss coefficient of each storage module at each time is obtained; according to the module output power, module temperature and charge and discharge loss coefficient of each storage module at each time, the health index of each storage module at each time is obtained.

[0025] The module internal resistance reflects the resistance encountered by the current flowing through the module when the energy storage module is working. The larger the module internal resistance, the lower the charging and discharging efficiency and the greater the internal resistance loss. The module capacity distribution can evaluate the performance of each energy storage module and reflect the loss of the energy storage module. According to the module internal resistance and module capacity distribution of each energy storage module at different times, the charging and discharging loss coefficient of each energy storage module at each time is obtained.

[0026] Preferably, in one embodiment of the present invention, the method for obtaining the charge and discharge loss coefficient includes: Obtain the nominal capacity of the power storage module; obtain the difference between the nominal capacity of the power storage module and the maximum capacity of the module, and calculate the ratio of the difference result and the nominal capacity as the capacity attenuation rate; The capacity attenuation rate of each energy storage module and the internal resistance of the module at each moment are combined to obtain the charge and discharge loss coefficient of each energy storage module at each moment.

[0027] It should be noted that, in some embodiments of the present invention, data may be fused by addition or other methods, and the specific means are well known to those skilled in the art and will not be elaborated here.

[0028] In one embodiment of the present invention, for any power storage module, the formula for the charge and discharge loss coefficient is expressed as: ; in, Indicates that the power storage module is The charge and discharge loss coefficient at the moment; Indicates the nominal capacity of the power storage module; Indicates the maximum module capacity of the power storage module; Indicates The internal resistance of the module at the moment.

[0029] In the formula for the charge and discharge loss coefficient, Indicates the maximum capacity of the module, that is, the actual capacity of the power storage module at all times; It means calculating the difference between the nominal capacity of the energy storage module and the maximum capacity of the module, and calculating the ratio of the difference result to the nominal capacity, that is, the capacity attenuation rate. The larger the difference, the greater the difference between the nominal capacity and the maximum capacity of the module, and the greater the loss; the larger the internal resistance of the energy storage module, the more likely the number of charge and discharge cycles is, and the greater the charge and discharge loss coefficient is.

[0030] It should be noted that, in one embodiment of the present invention, the nominal capacity, i.e., the standard capacity value that the power storage module can achieve under standard conditions, is obtained in advance by the implementation personnel based on relevant professional information provided at the factory, and will not be elaborated here.

[0031] The module output power can intuitively understand the module's output capacity. The larger the module output power, the greater the output capacity and the healthier the operating status. The module temperature can timely understand the module's working status. The higher the module temperature, the faster the chemical reaction and physical change of the internal module materials, the faster the aging speed and the worse the health condition. The charge and discharge loss coefficient reflects the energy loss of the power storage module during the charge and discharge process. The larger the charge and discharge loss coefficient, the more serious the energy loss and the smaller the health index. Comprehensive analysis of module output power, module temperature and charge and discharge loss coefficient can more comprehensively reflect the health of each power storage module. According to the module output power, module temperature and charge and discharge loss coefficient of each power storage module at each moment, the health index of each power storage module at each moment is obtained.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining health indicators includes: Normalizing the module output power of the power storage module at each moment as the first health coefficient; The module temperature and charge-discharge loss coefficient are integrated and negatively correlated to obtain the second health coefficient. The product of the first health coefficient and the second health coefficient is obtained as the health indicator of the power storage module at each moment.

[0033] In one embodiment of the present invention, the formula of the health index is expressed as: ; in, Indicates that the power storage module is Health indicators at all times; Indicates that the power storage module is The module output power at the moment; Indicates the rated output power of the power storage module; Indicates that the power storage module is The charge and discharge loss coefficient at the moment; Indicates that the power storage module is The module temperature at the moment.

[0034] In the formula of health index, Indicates that the calculation of the power storage module is The ratio of the module output power at the moment to the rated output power, that is, the ratio of the module output power at the moment The module output power at the moment is normalized to obtain the first influence coefficient; the greater the module output power, the closer it is to the rated output power, the more normal the operation of the power storage module at the corresponding moment, and the greater the first influence coefficient; the greater the charging and discharging loss coefficient of the power storage module, the greater the module temperature of the power storage module, the greater the operating consumption of the power storage module, and the smaller the health index of the power storage module.

[0035] It should be noted that the rated output power refers to the maximum power that the energy storage module can continuously output under specified standard conditions, and the implementation personnel can obtain it in advance based on relevant instructions or technical manuals.

[0036] Step S3: Obtain the grid load degree at each moment according to the grid output power and grid frequency at each moment; obtain the grid predicted load degree corresponding to the next moment at the real time according to the grid load degree distribution at different moments, and obtain the grid load change degree at the next moment.

[0037] The grid output power directly shows the load demand of the grid. The greater the grid output power, the greater the load demand. The grid frequency reflects the balance between the output capacity of the power storage module and the load in the grid. The greater the grid frequency, the smaller the load. The grid load degree at each moment is obtained based on the grid output power and grid frequency at each moment.

[0038] Preferably, in one embodiment of the present invention, the method for acquiring the power grid load degree includes: The ratio between the grid output power and the grid frequency at each moment is obtained as the grid load degree at each moment.

[0039] It should be noted that, in one embodiment of the present invention, the greater the grid output power at each moment and the smaller the grid frequency, the greater the grid load, which affects the operation efficiency of the grid.

[0040] The load degree of the power grid can reflect the operating status of the power grid. By analyzing the distribution of the load degree of the power grid at different times, the law and trend of load changes can be revealed, which is helpful for the prediction accuracy of the load degree at the next moment. According to the distribution of the load degree of the power grid at different times, the predicted load degree of the power grid corresponding to the real time at the next moment is obtained, and the degree of change of the load of the power grid at the next moment is obtained.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the power grid load trend value includes: According to the difference distribution of the power grid load degree between different times, the power grid load trend value at each moment is obtained; In one embodiment of the present invention, a method for obtaining a power grid load trend value includes: The difference in the power grid load between the next moment and the previous moment within the neighborhood range of each moment is obtained as the load difference; the average of the load difference between all moments is obtained and normalized as the power grid load trend value at each moment.

[0042] In one embodiment of the present invention, the formula for the grid load trend value is expressed as: ; in, Indicates The grid load trend value at the moment; Indicates The grid load at the moment; Indicates The grid load at the moment; Indicates The number of moments in the neighborhood of a moment; Represents the normalization function.

[0043] In the formula of the grid load trend value, Indicates Moment and The difference in grid load between the two times is called load difference; It represents the mean of the load difference between all moments in the neighborhood. The larger the mean, the larger the load difference, indicating that the overall grid load is increasing and the grid load trend value is larger. On the contrary, the smaller the mean, the smaller the load difference, indicating that the overall grid load is decreasing and the grid load trend value is smaller.

[0044] It should be noted that, in one embodiment of the present invention, the neighborhood range is based on each moment and is composed of all historical moments; in other embodiments of the present invention, the neighborhood range can be specifically set according to specific circumstances, and is not limited or elaborated here.

[0045] The power grid load degree at the real time is gain-adjusted according to the power grid load trend value at the real time to obtain the power grid predicted load degree corresponding to the next moment at the real time.

[0046] In one embodiment of the present invention, the formula for predicting the load degree of the power grid is expressed as: ; in, Indicates The predicted load of the power grid at the moment; Indicates real time The grid load degree; Indicates real time The grid load trend value.

[0047] In the formula for predicting the load degree of the power grid, the larger the trend value of the power grid load, the more of an overall growth trend it shows, the more it tends to increase the load degree of the power grid, and the larger the predicted load degree of the power grid.

[0048] Preferably, in one embodiment of the present invention, the method for obtaining the degree of change of the power grid load includes: The degree of grid load change is obtained according to the deviation of the grid load predicted at the next moment relative to the grid load at the real moment.

[0049] It should be noted that, in one embodiment of the present invention, the degree of change of the power grid load can be expressed by calculating the ratio between the power grid predicted load degree at the next moment and the power grid load degree at the real time. The larger the ratio, the larger the power grid predicted load degree at the next moment, the larger the power grid load degree relative to the real time, the larger the degree of change of the power grid load. The smaller the ratio, the smaller the power grid predicted load degree at the next moment, the smaller the power grid load degree relative to the real time, and the smaller the degree of change of the power grid load. In other embodiments of the present invention, the degree of change of the power grid load can also be expressed by calculating the difference between the power grid predicted load degree at the next moment and the power grid load degree at the real time. The larger the difference, the larger the power grid predicted load degree at the next moment relative to the power grid load degree at the real time, the larger the degree of change of the power grid load. The smaller the difference, the smaller the power grid predicted load degree at the next moment relative to the power grid load degree at the real time, and the smaller the degree of change of the power grid load. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0050] Step S4: According to the health indicators of different power storage modules at the real time and the degree of change of the power grid load at the next moment, the power grid load bearing weight of each power storage module at the next moment is obtained; according to the power grid load bearing weight of each power storage module at the next moment and the preset power grid load demand power, the load distribution power of each power storage module at the next moment is obtained.

[0051] In general, the output power of the grid load needs to be borne evenly by all energy storage modules, so that the worse the health status of the energy storage module, the greater the pressure on the output power, resulting in overload. Therefore, considering that the different health status of different energy storage modules will lead to differences in the corresponding power output capacity, combined with the degree of change in the grid load at the next moment, the greater the health status, the greater the degree of change in the grid load, the greater the pressure to be borne, and the greater the weight of the grid load. According to the health indicators of different energy storage modules at the real time and the degree of change in the grid load at the corresponding next moment, the grid load weight of each energy storage module at the next moment is obtained.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the grid load bearing weight is as follows: Figure 2 , which shows a flow chart of a method for obtaining a power grid load bearing weight, including: Step S201: The health index of each power storage module at the real time, the preset maximum output power and the degree of change of the grid load corresponding to the next moment are integrated to obtain the local load bearing degree of each power storage module at the next moment.

[0053] The larger the health index, the larger the preset maximum output power, the greater the degree of grid load variation, the more power the storage module needs to provide to the grid, and the greater the local load bearing capacity.

[0054] It should be noted that in some embodiments of the present invention, the health index, the preset maximum output power and the corresponding grid load change degree can be integrated by addition or multiplication. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0055] It should be noted that, in the embodiments of the present invention, the preset maximum output power refers to the maximum output power that the power storage module can withstand without being damaged, and can be obtained in advance by implementation personnel based on relevant professional knowledge.

[0056] Step S202: Obtain the average of the local load bearing degrees of all the power storage modules at the next moment as the overall load bearing level.

[0057] By taking the average value to quantify and count the local load bearing degrees of all energy storage modules at the next moment, we can understand the overall load capacity of the entire energy storage power station, which is convenient for data comparison in subsequent calculation processes.

[0058] Step S203: Obtain the ratio of the local load bearing degree and the overall load bearing level of each power storage module at the next moment as the grid load bearing weight of each power storage module at the next moment.

[0059] In one embodiment of the present invention, the formula for the grid load weight is expressed as: ; in, Indicates The power storage module is in the The grid load at the moment bears the weight; Indicates The power storage module is in real time health indicators; Indicates The preset maximum output power of each storage module; Indicates The power storage module is in real time health indicators; Indicates The preset maximum output power of each storage module; Indicates the next moment relative to the real time The degree of grid load variation; Indicates the number of power storage modules.

[0060] In the formula for the grid load weight, Indicates the calculation The power storage module is in real time The product of the health index and the preset maximum output power, and the product result and the next moment relative to the The grid load change degree at each moment is added up, that is, the local load bearing degree of each power storage module at the next moment; It means obtaining the average of the local load bearing degree of all power storage modules at the next moment, that is, the overall load bearing level; the greater the local load bearing degree relative to the overall load bearing level, the greater the grid load bearing weight of the corresponding power storage module at the next moment.

[0061] By allocating power to the energy storage modules based on the grid load weight, the energy storage resources of each energy storage module can be utilized more reasonably to avoid overloading or underloading of certain modules. The energy storage module with a larger load weight can output more power to the grid, and the energy storage module with a smaller load weight can output less power to the grid. The load distribution power of each energy storage module at the next moment is obtained based on the grid load weight of each energy storage module at the next moment and the preset grid load demand power.

[0062] Preferably, in one embodiment of the present invention, the method for acquiring load distribution power includes: The product of the grid load weight of the power storage module at the next moment and the preset grid load demand power is obtained to obtain the load distribution power at the next moment.

[0063] In one embodiment of the present invention, the load distribution power formula is expressed as: ; in, Indicates The power storage module is in the Load distribution power at the moment; Indicates The power storage module is in the The grid load at the moment bears the weight; Indicates The preset grid load demand power at the moment.

[0064] In the load sharing power formula, The power storage module is in the The greater the weight of the grid load at a given moment, the more power the energy storage module can output to the grid. The greater the preset grid load demand power, the greater the load distribution power.

[0065] It should be noted that, in the embodiments of the present invention, the preset grid load demand power may be specifically set by implementation personnel according to specific circumstances and relevant professional knowledge.

[0066] Step S5: adjusting the energy storage according to the load distribution power of each power storage module at the next moment.

[0067] Different energy storage modules have differences in performance. By obtaining the load distribution power of each energy storage module at the next moment, it can be ensured that the energy storage module can provide appropriate power output under different load conditions to meet the needs of the power system, help reduce excessive charging and discharging of the energy storage module, and improve the operating stability of the system.

[0068] In summary, the present invention obtains the charge-discharge loss coefficient of each power storage module at each moment according to the module internal resistance and module capacity distribution at different moments; obtains the health index of each power storage module at each moment in combination with the module output power and module temperature of each power storage module at each moment; obtains the power grid load degree at each moment according to the power grid output power and power grid frequency at each moment; obtains the degree of change of the power grid load at the next moment relative to the real time according to the power grid load degree distribution at different moments; and then obtains the power grid load bearing weight of each power storage module at the next moment; obtains the load distribution power of each power storage module at the next moment in combination with the preset power grid load demand power; and adjusts the energy storage. The present invention improves the energy storage effect by obtaining the appropriate power for each power storage module to distribute the power to the power grid load.

[0069] The present invention also proposes a novel intelligent energy storage power station energy storage regulation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of a novel intelligent energy storage power station energy storage regulation method.

[0070] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A new type of intelligent energy storage power station energy storage regulation method, characterized in that: The method comprises: Acquire multi-dimensional parameters of different power storage modules in the energy storage power station, as well as grid output power and grid frequency in a time sequence, wherein the multi-dimensional parameters at least include module output power, module capacity, module internal resistance, and module temperature; According to the module internal resistance and module capacity distribution of each power storage module at different times, the charge and discharge loss coefficient of each power storage module at each time is obtained; according to the module output power, module temperature and charge and discharge loss coefficient of each power storage module at each time, the health index of each power storage module at each time is obtained; According to the grid output power and grid frequency at each moment, the grid load degree at each moment is obtained; according to the grid load degree distribution at different moments, the grid predicted load degree corresponding to the next moment at the real time is obtained, and the degree of grid load change at the next moment is obtained; According to the health indicators of different power storage modules at real time and the degree of change of the power grid load at the next moment, the power grid load weight of each power storage module at the next moment is obtained; according to the power grid load weight of each power storage module at the next moment and the preset power grid load demand power, the load distribution power of each power storage module at the next moment is obtained; The energy storage is adjusted according to the load distribution power of each storage module at the next moment.

2. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the charge and discharge loss coefficient includes: Obtain the nominal capacity of the power storage module; obtain the difference between the nominal capacity of the power storage module and the maximum capacity of the module, and calculate the ratio of the difference result and the nominal capacity as the capacity attenuation rate; The capacity attenuation rate of each energy storage module and the internal resistance of the module at each moment are combined to obtain the charge and discharge loss coefficient of each energy storage module at each moment.

3. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the health indicator includes: Normalizing the module output power of the power storage module at each moment as the first health coefficient; The module temperature and charge-discharge loss coefficient are integrated and negatively correlated to obtain the second health coefficient. The product of the first health coefficient and the second health coefficient is obtained as the health indicator of the power storage module at each moment.

4. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the power grid load degree includes: The ratio between the grid output power and the grid frequency at each moment is obtained as the grid load degree at each moment.

5. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the predicted load degree of the power grid includes: According to the difference distribution of the power grid load degree between different times, the power grid load trend value at each moment is obtained; The power grid load degree at the real time is gain-adjusted according to the power grid load trend value at the real time to obtain the power grid predicted load degree corresponding to the next moment at the real time.

6. A novel intelligent energy storage power station energy storage regulation method according to claim 5, characterized in that: The method for obtaining the power grid load trend value comprises: The difference in the power grid load between the next moment and the previous moment within the neighborhood range of each moment is obtained as the load difference; the average of the load difference between all moments is obtained and normalized as the power grid load trend value at each moment.

7. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the degree of change of the power grid load includes: The degree of grid load change is obtained according to the deviation of the grid load predicted at the next moment relative to the grid load at the real moment.

8. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for obtaining the power grid load bearing weight includes: The health index of each power storage module at the real time, the preset maximum output power and the degree of change of the grid load at the next moment are integrated to obtain the local load bearing degree of each power storage module at the next moment; Obtain the average of the grid load bearing weights of all power storage modules at the next moment as the overall load bearing level; The ratio of the local load bearing degree and the overall load bearing level of each power storage module at the next moment is obtained as the grid load bearing weight of each power storage module at the next moment.

9. A novel intelligent energy storage power station energy storage regulation method according to claim 1, characterized in that: The method for acquiring the load distribution power includes: The product of the grid load weight of the power storage module at the next moment and the preset load power demand is obtained to obtain the load distribution power at the next moment.

10. A novel intelligent energy storage power station energy storage regulation system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a new type of intelligent energy storage power station energy storage regulation method as described in any one of claims 1 to 9 are implemented.

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