Intelligent charging and discharging power distribution method for hybrid energy storage system

Abstract

The invention relates to the technical field of hybrid energy storage systems, and particularly discloses an intelligent charge and discharge power distribution method for a hybrid energy storage system, which comprises the following steps of: firstly, acquiring state data of an energy storage unit, and extracting key indexes including charge state, temperature, cycle index and response speed from the state data; then, based on the key indexes, distributing weights for each index, calculating to obtain a score of each key index, further, based on the score of each key index, adopting a weighted average method to obtain a comprehensive score of the energy storage unit, dividing the energy storage unit into an excellent energy storage unit and a common energy storage unit according to the comprehensive score, and finally, obtaining a final energy storage unit. Based on the total power requirement of the hybrid energy storage system, power distribution of the excellent energy storage unit and the common energy storage unit is dynamically adjusted, the overall efficiency and reliability of the energy storage system can be improved, and meanwhile the service life of the energy storage unit is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage systems, and in particular to a charging and discharging power intelligent distribution method for hybrid energy storage systems. Background Art

[0002] With the development of new energy technologies, energy storage systems have become a key technology for balancing electricity supply and demand and improving energy utilization efficiency. Hybrid energy storage systems can effectively leverage the advantages of different types of energy storage units (such as lithium-ion batteries, supercapacitors, etc.) to achieve efficient energy storage and release. However, how to reasonably allocate the charging and discharging power of different energy storage units to optimize the performance of the entire system and extend its service life is an important challenge currently faced.

[0003] At present, the power allocation strategy of most hybrid energy storage systems is relatively simple, usually based on the static allocation of the energy storage unit's state of charge (SOC), and fails to fully consider the influence of other key factors, such as temperature, number of cycles and response speed. These factors have a significant impact on the performance and life of the energy storage unit. Therefore, a more intelligent method is needed to comprehensively evaluate the status of the energy storage unit and dynamically adjust the power allocation accordingly.

[0004] The present invention aims to provide a method for intelligently distributing charging and discharging power for a hybrid energy storage system, which comprehensively evaluates key indicators of energy storage units and dynamically adjusts the power distribution of energy storage units, thereby improving the operating efficiency and reliability of the entire system. Summary of the invention

[0005] The purpose of the present invention is to provide a method for intelligently allocating charging and discharging power for a hybrid energy storage system to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A charging and discharging power intelligent allocation method for a hybrid energy storage system, comprising:

[0008] Obtain status data of energy storage units and perform preprocessing;

[0009] Among them, the state data includes charge state data, temperature data, cycle number data and response speed data;

[0010] Based on the status data of the energy storage unit, a weight is assigned to each status data to obtain a score for each status data;

[0011] Based on the score of each state data, a comprehensive score of the fast-charging and discharging energy storage unit and a comprehensive score of the long-life energy storage unit are obtained;

[0012] Based on the comprehensive scores of fast-charging and discharging energy storage units and the comprehensive scores of long-life energy storage units, the energy storage units are divided into excellent energy storage units and ordinary energy storage units;

[0013] Analyze the total power demand of the hybrid energy storage system based on excellent energy storage units and ordinary energy storage units;

[0014] Based on the total power demand of the hybrid energy storage system, the power distribution of excellent energy storage units and ordinary energy storage units is dynamically adjusted.

[0015] As a further solution of the present invention: the process of obtaining the score of each status data is as follows:

[0016] Get the current value I flowing into and out of the energy storage unit, through the formula The change in state of charge ΔSOC is obtained, where t is the time point, t=1, 2, ..., t, I(t) is the current at time t, C r is the rated capacity of the energy storage unit;

[0017] Based on the change in state of charge ΔSOC, the formula SOC new =ΔSOC+SOC chu Get the current state of charge SOC new ;

[0018] Based on the current state of charge SOC new , assign a weight W to it soc , through the formula Get the current state of charge score SOCs;

[0019] Get the temperature value Tw of the energy storage unit and assign a weight W to the temperature value Tw Tw , through the formula Obtaining a temperature score Tws of the energy storage unit, wherein Tw1 is the minimum value within a preset optimal temperature range of the energy storage unit, and Tw2 is the maximum value within a preset optimal temperature range of the energy storage unit;

[0020] Get the time required for the energy storage unit to complete the state transition from receiving the command, record it as the response time RT, and assign a weight of W to the response time RT RT , through the formula Get the response time score RTs of the energy storage unit;

[0021] Read the number of complete charge and discharge cycles experienced by the energy storage unit, record it as the cycle number CS, and assign a weight W to the cycle number CS , through the formula The cycle number score CSs of the energy storage unit is obtained.

[0022] As a further solution of the present invention: the process of obtaining the comprehensive score of the fast-charging and discharging energy storage unit and the comprehensive score of the long-life energy storage unit is as follows:

[0023] By the formula KSs = W SOC ×SOCs+W Tw ×Tw+W RT ×RTs to obtain the comprehensive score KSs of the fast-charging and discharging energy storage unit;

[0024] By the formula SMs = W SOC ×SOCs+W Tw ×Tw+W CS ×CSs to obtain the comprehensive score SMs of the long-life energy storage unit.

[0025] As a further solution of the present invention: the process of obtaining excellent energy storage units and ordinary energy storage units is as follows:

[0026] Based on the comprehensive score KSs of the fast-charging and discharging energy storage unit and the comprehensive score SMs of the long-life energy storage unit, the comprehensive score of each energy storage unit is compared with the comprehensive score threshold of the energy storage unit;

[0027] If the comprehensive score of the energy storage unit is greater than or equal to the comprehensive score threshold of the energy storage unit, the energy storage unit is determined as an excellent energy storage unit;

[0028] If the comprehensive score of the energy storage unit is less than the comprehensive score threshold of the energy storage unit, the energy storage unit is defined as a common energy storage unit.

[0029] As a further solution of the present invention: based on the excellent energy storage unit and the ordinary energy storage unit, the load data of the hybrid energy storage system when the time subunit is in the middle moment is obtained;

[0030] Integrate all load data in the time sub-unit to obtain a load data set;

[0031] Obtain the load mean and load standard deviation of the load data set to obtain the standard value FZB of the load data;

[0032] Based on the standard value FZB of the load data, create the feature FZZ for the load data lagged by i time subunits t-i , among which, FZB t-i Represents the load data of the ti-th time subunit, where t is the time point and i is the number of preset time points in the past.

[0033] As a further solution of the present invention: based on the standard value FZB of the load data, a characteristic value is created for each time subunit, which is recorded as the time subunit characteristic TSJ n, where n = 1, 2, ..., m, m is the total number of time subunits, and the feature of the nth time subunit can be expressed as TSJ n =n;

[0034] Integrate all time sub-unit features to obtain a time sub-unit dataset;

[0035] Get the mean and standard deviation of the time subunit feature of the time subunit data set to obtain the standardized feature TSS of the time subunit feature n .

[0036] As a further solution of the present invention: based on the standardized feature TSS n and hysteresis characteristic FZZ t-i , through the formula Get the predicted load demand FZB wei ;

[0037] Among them, FZZ t-i,wei is the actual load data of the first i time subunits in the future prediction period, TSS n,wei The future prediction period belongs to the nth time subunit of the day, b i Indicates the hysteresis characteristic FZZ t-i,wei The coefficient of i+n TSS is the time feature n,wei The coefficient of , l is the number of lagged features used, and m represents the number of time sub-units divided within a day.

[0038] As a further solution of the present invention: based on the predicted load demand FZBwei, the currently available discharge power Pe of the energy storage system is obtained;

[0039] By the formula ZGL=FZB wei -Pe obtains the total power demand ZGL of the hybrid energy storage system;

[0040] If the total power demand ZGL is greater than 0, a charging signal is generated;

[0041] If the total power demand ZGL is less than 0, a discharge signal is generated.

[0042] As a further solution of the present invention: based on the charging signal, the power of the excellent energy storage unit and the ordinary energy storage unit is distributed, specifically:

[0043] Based on the charging signal, the maximum charging power PCD of the hybrid energy storage system is obtained max and maximum discharge power PFD max ;

[0044] The total power demand ZGL is combined with the maximum charging power PCD of the hybrid energy storage systemmax Make comparisons;

[0045] If the total power demand ZGL is less than or equal to the maximum charging power PCD of the hybrid energy storage system max , then it is determined that the excellent energy storage unit can fully bear the charging demand, through the formula YX cd =min(ZGL,PCD max ) Get the charging power YX of the excellent energy storage unit cd ;

[0046] If the total power demand ZGL is greater than the maximum charging power PCD of the hybrid energy storage system max , it is determined that the excellent energy storage unit can only partially bear the charging demand, and the ordinary energy storage unit bears the remaining power demand;

[0047] PT by formula cd =min(ZGL-PCD max , PCD max ) to obtain the charging power PT of the ordinary energy storage unit cd .

[0048] As a further solution of the present invention: based on the discharge signal, the power of the excellent energy storage unit and the ordinary energy storage unit is distributed, specifically:

[0049] Based on the discharge signal, the absolute value of the total power demand ZGL is compared with the maximum charging power PCD of the energy storage system. max Make comparisons;

[0050] If the absolute value of the total power demand ZGL is less than or equal to the maximum discharge power PCD of the hybrid energy storage system max , then the excellent energy storage unit can fully bear the discharge demand, through the formula YX fd =min(ZGL,PFD max ) to obtain the discharge power YX of the excellent energy storage unit fd ;

[0051] If the absolute value of the total power demand ZGL is greater than the maximum discharge power PCD of the energy storage system max , it is determined that the excellent energy storage unit can only partially bear the discharge demand, and the ordinary energy storage unit bears the remaining power demand;

[0052] PT by formula fd =min(ZGL|-PCD max , PCD max ) to obtain the discharge power PT of the common energy storage unit fd .

[0053] Beneficial effects of the present invention:

[0054] (1) The present invention dynamically adjusts the power distribution between different energy storage units through an intelligent algorithm, so that in scenarios where a quick response is required, energy storage units with a higher response speed are preferentially used, while in scenarios where a long-term stable output is required, more reliance is placed on energy storage units with a high energy density. In this way, not only the response speed of the entire energy storage system is improved, but also the flexibility of the system is enhanced, thereby significantly improving the overall efficiency of the energy storage system. For example, in the application of grid frequency regulation, the present invention can quickly mobilize supercapacitors for short-term high-frequency power compensation, while reasonably allocating the power output of the battery, effectively maintaining the stability of the grid frequency, and reducing unnecessary energy loss;

[0055] (2) The present invention evaluates the key performance indicators of the energy storage unit and allocates power accordingly, thereby ensuring the overall performance of the system while effectively avoiding the problem of shortened life due to overuse of a single energy storage unit. For example, when it is detected that the state of charge (SOC) of a certain energy storage unit is close to the boundary of its recommended operating range, the present invention will automatically reduce the charging and discharging power of the energy storage unit and increase the power allocation of other energy storage units to ensure that all energy storage units operate in the best working state, thereby avoiding performance degradation and damage risks caused by overcharging or over-discharging. This method not only helps to balance the usage frequency of each energy storage unit, but also significantly prolongs the service life of the entire energy storage system;

[0056] (3) The present invention achieves optimal resource allocation through intelligent management of energy storage units, which not only improves the overall performance of the system, but also brings obvious economic benefits. On the one hand, the service life of the energy storage unit is extended, which reduces the need for equipment replacement and maintenance costs. On the other hand, through the effective use of different types of energy storage units, the efficiency of energy conversion and storage is improved, energy loss is reduced, and thus operating costs are reduced. In addition, the present invention can flexibly adjust the working mode of the energy storage unit according to different application scenarios. For example, it can automatically adjust the charging and discharging strategy during peak and valley periods to help users save electricity expenses. In summary, the present invention not only achieves innovation at the technical level, but also brings tangible benefits to enterprises and individual users at the economic level. It has high practical value and social significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] Figure 1 It is a flow chart of a method for intelligently allocating charging and discharging power for a hybrid energy storage system according to the present invention;

[0059] Figure 2 It is a flow chart of the division of excellent energy storage units and ordinary energy storage units in a method for intelligent allocation of charging and discharging power for a hybrid energy storage system of the present invention;

[0060] Figure 3 It is a flow chart of energy storage unit allocation in a charging state in a method for intelligent allocation of charging and discharging power for a hybrid energy storage system according to the present invention;

[0061] Figure 4 It is a flow chart of energy storage unit allocation in a discharge state in a method for intelligent allocation of charging and discharging power for a hybrid energy storage system according to the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] See also Figure 1 As shown, the present invention is a charging and discharging power intelligent distribution method for a hybrid energy storage system, comprising:

[0065] Obtaining status data of the energy storage unit, including state of charge data, temperature data, cycle number data, and response speed data;

[0066] Based on the status data of the energy storage unit, a weight is assigned to each status data to obtain a score for each status data;

[0067] Based on the score of each state data, a comprehensive score of the energy storage unit integrating all state data is obtained;

[0068] Based on the comprehensive scores of the energy storage units, the energy storage units are divided into excellent energy storage units and ordinary energy storage units;

[0069] Analyze the total power demand of the hybrid energy storage system based on excellent energy storage units and ordinary energy storage units;

[0070] Based on the total power demand of the hybrid energy storage system, the power distribution of excellent energy storage units and ordinary energy storage units is dynamically adjusted.

[0071] Example 2

[0072] See also Figure 2 As shown, the type of each energy storage unit is obtained, and the energy storage units are classified into fast charging and discharging type and long life type according to different types of energy storage units;

[0073] Among them, fast-charging and discharging types include but are not limited to batteries with low energy density and high power density, such as lithium-ion batteries and supercapacitors, and long-life energy storage units include but are not limited to batteries with high energy density and low power, such as lead-acid batteries, sodium-sulfur batteries and flow batteries;

[0074] Get the current state of charge of each energy storage unit, specifically:

[0075] When the energy storage unit is fully charged, the current state of charge is calibrated to 100%, which is recorded as the initial state of charge SOC chu ;

[0076] Divide the monitoring cycle into a number of time sub-units of equal length, and monitor the current values ​​flowing into and out of the energy storage unit when the time sub-unit is in the middle moment;

[0077] All current values ​​within the monitoring period are integrated into current groups in time series, and the formula The change in state of charge ΔSOC is obtained, where t is the time point, t=1, 2, ..., t, I(t) is the current at time t (positive value indicates charging, negative value indicates discharging), C r is the rated capacity of the energy storage unit;

[0078] Based on the change in state of charge ΔSOC, the formula SOC new =ΔSOC+SOC chu Get the current state of charge SOC new ;

[0079] Based on the current state of charge SOC new , assign a weight W to it soc , through the formula Get the current state of charge score SOCs;

[0080] When the time subunit is in the middle moment, the temperature value Tw of the energy storage unit is collected, and a weight W is assigned to the temperature value Tw. Tw , through the formula Obtaining a temperature score Tws of the energy storage unit, wherein Tw1 is the minimum value within a preset optimal temperature range of the energy storage unit, and Tw2 is the maximum value within a preset optimal temperature range of the energy storage unit;

[0081] Monitor and record the time required for the energy storage unit in the time subunit to complete the state transition from receiving the instruction, which is recorded as the response time RT. The weight assigned to the response time RT is W. RT , through the formula Get the response time score RTs of the energy storage unit;

[0082] Read the number of complete charge and discharge cycles experienced by the energy storage unit, record it as the cycle number CS, and assign a weight W to the cycle number CS , through the formula Get the cycle number score CSs of the energy storage unit;

[0083] It should be noted that the application scenarios and characteristics of fast-charge and discharge energy storage units are different from those of long-life energy storage units, and the most important indicators for evaluating the status of energy storage units are also different. For fast-charge and discharge energy storage units, response time is more important, and the faster the response speed, the higher the score. For long-life energy storage units, the number of cycles is more important, and the more cycles, the lower the score. Therefore, the state of charge, temperature and response time are used to evaluate fast-charge and discharge energy storage units, and the state of charge, temperature and number of cycles are used to evaluate long-life energy storage units.

[0084] Based on the fast charging and discharging energy storage unit, the formula KSs=W SOC ×SOCs+W Tw ×Tw+W RT ×RTs to obtain the comprehensive score KSs of the fast-charging and discharging energy storage unit;

[0085] Based on the long-life energy storage unit, the formula SMs = W SOC ×SOCs+W Tw ×Tw+W CS ×CSs to obtain the comprehensive score SMs of the long-life energy storage unit;

[0086] Based on the comprehensive score KSs of the fast-charging and discharging energy storage unit and the comprehensive score SMs of the long-life energy storage unit, the comprehensive score of each energy storage unit is compared with the comprehensive score threshold of the energy storage unit;

[0087] If the comprehensive score of the energy storage unit is greater than or equal to the comprehensive score threshold of the energy storage unit, the energy storage unit is determined as an excellent energy storage unit;

[0088] If the comprehensive score of the energy storage unit is less than the comprehensive score threshold of the energy storage unit, the energy storage unit is defined as a common energy storage unit.

[0089] Real-time Example 3

[0090] Based on excellent energy storage units and ordinary energy storage units, the monitoring cycle is divided into days, and one day is divided into several time sub-units, and the load data FZS of the hybrid energy storage system when the time sub-unit is in the middle moment is obtained;

[0091] Based on the load data FZS of the hybrid energy storage system when the time subunit is in the middle moment, all the load data FZS in one day are integrated and sorted in time series as a load data set;

[0092] Based on the load data set, the mean value UF of the load data FZS is obtained by mean calculation;

[0093] Based on the mean value UF of the load data FZS, the standard deviation BF of the load data FZS is obtained by standard deviation calculation;

[0094] Based on the mean UF and standard deviation BF of the load data FZS, the formula Calculate and obtain the standard value FZB of the load data;

[0095] Based on the standard value FZB of the load data, create the feature FZZ for the load data lagged by i time subunits t-i , then we have FZZ t-i =FZB t-i , among which, FZB t-i Represents the load data of the ti-th time subunit, where t is the time point and i is the number of preset time points in the past;

[0096] For example, if a time subunit is 1 hour, and a feature with a lag of 1 hour is created, then FZZ t-1 =FZB t-1 ;

[0097] Based on several time subunits in a day, a feature is created for each time subunit in a day, representing the nth time subunit in a day, which is recorded as the time subunit feature TSJ n , the feature of the nth time subunit can be expressed as TSJ n =n, where n=1, 2, ..., m, and m is the total number of time subunits in a day;

[0098] For example, if a day is divided into 24 time subunits, the time subunits are numbered from 0 to 23, a total of 24 data points. For each data point, the corresponding time subunit number is extracted as the feature TSJ n ;

[0099] Based on the time subunit feature TSJ n , integrate all time sub-unit features in a day, and obtain the time sub-unit feature TSJ through the mean calculation formula n The mean value UT;

[0100] Based on the time subunit feature TSJ n The mean UT of the time subunit is calculated by the standard deviation formula to obtain the time subunit characteristic TSJ n The standard deviation of BT;

[0101] By formula Calculate the time subunit feature TSJ n Standardized features of TSSn ;

[0102] Based on standardized features TSS n and hysteresis characteristic FZZ t-i , through the formula Get the predicted load demand FZB wei ;

[0103] Among them, FZZ t-i,wei is the actual load data of the first i time subunits in the future prediction period, TSS n,wei Indicates that the future forecast period belongs to the nth time subunit of a day, b i Indicates the hysteresis characteristic FZZ t-i,wei The coefficient of i+n Represents time feature TSS n,wei The coefficient of , l is the number of lagged features used, and m represents the number of time subunits divided within a day;

[0104] Coefficient b i and b i+n The specific process of obtaining is as follows:

[0105] By the least squares formula Get the residual sum of squares RSS between the predicted value and the actual value;

[0106] Where N is the number of all load data in the monitoring period, i is the number of load data, i = 1, 2, ..., N, l is the number of hysteresis features, j is the index of hysteresis features, j = 1, 2, ..., l, m is the number of time subunits, k is the index of time subunit features, j = 1, 2, ..., m, FZZ t-j,i The value of the jth lagged feature of the ith time subunit, TSS k,j is the value of the kth time subunit feature of the i-th sample;

[0107] By formula Get the coefficient b e The matrix form of the partial derivative equation system is XP T XB e =XP T FZB, through the matrix b e =(XP T XP) -1 XP T FZB calculates b e The least squares estimate of t-j,i and TSS k,j , e is the sequence number of coefficient b, and e is (i, i+n).

[0108] Example 4

[0109] Based on predicted load demand FZB wei , obtain the current available discharge power Pe of the energy storage system;

[0110] By the formula ZGL=FZB wei -Pe obtains the total power demand ZGL of the hybrid energy storage system;

[0111] If the total power demand ZGL is greater than 0, it means that the hybrid energy storage system needs to be charged and a charging signal is generated;

[0112] If the total power demand ZGL is less than 0, it means that the hybrid energy storage system needs to discharge and generate a discharge signal;

[0113] Based on the charging signal and the discharging signal, the power of the excellent energy storage unit and the ordinary energy storage unit is allocated. Specifically:

[0114] Get the maximum charging power PCD of the hybrid energy storage system max and maximum discharge power PFD max ;

[0115] Based on the charging signal, the total power demand ZGL is compared with the maximum charging power PCD of the hybrid energy storage system. max Make comparisons;

[0116] If the total power demand ZGL is less than or equal to the maximum charging power PCD of the hybrid energy storage system max , then it is determined that the excellent energy storage unit can fully bear the charging demand, through the formula YX cd =min(ZGL,PCD max ) Get the charging power YX of the excellent energy storage unit cd , that is, the charging power YX of an excellent energy storage unit cd Equal to the total power demand ZGL and the maximum charging power PCD of the hybrid energy storage system max The smaller value of ;

[0117] If the total power demand ZGL is greater than the maximum charging power PCD of the hybrid energy storage system max , it is determined that the excellent energy storage unit can only partially bear the charging demand, and the ordinary energy storage unit bears the remaining power demand;

[0118] PT by formula cd =min(ZGL-PCD max , PCD max ) to obtain the charging power PT of the ordinary energy storage unit cd , that is, when it is determined that the excellent energy storage unit can only partially bear the charging demand, the charging power PT of the ordinary energy storage unit cdEqual to the remaining power demand and the maximum charging power PCD of the hybrid energy storage system max The smaller value of ;

[0119] Based on the discharge signal, the absolute value of the total power demand ZGL is compared with the maximum charging power PCD of the energy storage system. max Make comparisons;

[0120] If the absolute value of the total power demand ZGL is less than or equal to the maximum discharge power PCD of the hybrid energy storage system max , then the excellent energy storage unit can fully bear the discharge demand, through the formula YX fd =min(ZGL,PFD max ) to obtain the discharge power YX of the excellent energy storage unit fd , that is, the discharge power YX of an excellent energy storage unit cd Equal to the absolute value of the total power demand ZGL and the maximum discharge power PCD of the energy storage system max The smaller value of ;

[0121] If the absolute value of the total power demand ZGL is greater than the maximum discharge power PCD of the energy storage system max , it is determined that the excellent energy storage unit can only partially bear the discharge demand, and the ordinary energy storage unit bears the remaining power demand;

[0122] PT by formula fd =min(ZGL|-PCD max , PCD max ) to obtain the discharge power PT of the common energy storage unit fd , that is, when it is determined that the excellent energy storage unit can only partially bear the discharge demand, the discharge power PT of the ordinary energy storage unit fd Equal to the absolute value of the remaining power demand and the maximum discharge power PCD of the energy storage system max The smaller value of ;

[0123] Continuously monitor the performance of energy storage units, update scores in real time and adjust power allocation strategies as the status of energy storage units changes.

[0124] Working principle of the present invention: (1) The present invention dynamically adjusts the power distribution between different energy storage units through an intelligent algorithm, so that in scenarios where rapid response is required, energy storage units with higher response speed are preferentially used, while in scenarios where long-term stable output is required, more reliance is placed on energy storage units with high energy density. In this way, not only the response speed of the entire energy storage system is improved, but also the flexibility of the system is enhanced, thereby significantly improving the overall efficiency of the energy storage system. For example, in the application of grid frequency regulation, the present invention can quickly mobilize supercapacitors for short-term high-frequency power compensation, while reasonably allocating the power output of the battery, effectively maintaining the stability of the grid frequency, and reducing unnecessary energy loss;

[0125] (2) The present invention evaluates the key performance indicators of the energy storage unit and allocates power accordingly, thereby ensuring the overall performance of the system while effectively avoiding the problem of shortened life due to overuse of a single energy storage unit. For example, when it is detected that the state of charge (SOC) of a certain energy storage unit is close to the boundary of its recommended operating range, the present invention will automatically reduce the charging and discharging power of the energy storage unit and increase the power allocation of other energy storage units to ensure that all energy storage units operate in the best working state, thereby avoiding performance degradation and damage risks caused by overcharging or over-discharging. This method not only helps to balance the usage frequency of each energy storage unit, but also significantly prolongs the service life of the entire energy storage system;

[0126] (3) The present invention achieves optimal resource allocation through intelligent management of energy storage units, which not only improves the overall performance of the system, but also brings obvious economic benefits. On the one hand, the service life of the energy storage unit is extended, which reduces the need for equipment replacement and maintenance costs. On the other hand, through the effective use of different types of energy storage units, the efficiency of energy conversion and storage is improved, energy loss is reduced, and thus operating costs are reduced. In addition, the present invention can flexibly adjust the working mode of the energy storage unit according to different application scenarios. For example, it can automatically adjust the charging and discharging strategy during peak and valley periods to help users save electricity expenses. In summary, the present invention not only achieves innovation at the technical level, but also brings tangible benefits to enterprises and individual users at the economic level. It has high practical value and social significance.

[0127] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for intelligently allocating charging and discharging power for a hybrid energy storage system, characterized in that: Obtain status data of energy storage units and perform preprocessing; Among them, the state data includes charge state data, temperature data, cycle number data and response speed data; Based on the status data of the energy storage unit, a weight is assigned to each status data to obtain a score for each status data; Based on the score of each state data, a comprehensive score of the fast-charging and discharging energy storage unit and a comprehensive score of the long-life energy storage unit are obtained; Based on the comprehensive scores of fast-charging and discharging energy storage units and the comprehensive scores of long-life energy storage units, the energy storage units are divided into excellent energy storage units and ordinary energy storage units; Analyze the total power demand of the hybrid energy storage system based on excellent energy storage units and ordinary energy storage units; Based on the total power demand of the hybrid energy storage system, the power distribution of excellent energy storage units and ordinary energy storage units is dynamically adjusted.

2. According to claim 1, a charging and discharging power intelligent allocation method for a hybrid energy storage system is characterized in that: The process of obtaining the score of each status data is as follows: Get the current value I flowing into and out of the energy storage unit, through the formula The change in state of charge ΔSOC is obtained, where t is the time point, t=1, 2, ..., t, I(t) is the current at time t, C r is the rated capacity of the energy storage unit; Based on the change in state of charge ΔSOC, the formula SOC new =ΔSOC+SOC chu Get the current state of charge SOC new ; Based on the current state of charge SOC new , assign a weight W to it soc , through the formula Get the current state of charge score SOCs; Get the temperature value Tw of the energy storage unit and assign a weight W to the temperature value Tw Tw , through the formula Obtaining a temperature score Tws of the energy storage unit, wherein Tw1 is the minimum value within a preset optimal temperature range of the energy storage unit, and Tw2 is the maximum value within a preset optimal temperature range of the energy storage unit; Get the time required for the energy storage unit to complete the state transition from receiving the command, record it as the response time RT, and assign a weight of W to the response time RT RT , through the formula Get the response time score RTs of the energy storage unit; Read the number of complete charge and discharge cycles experienced by the energy storage unit, record it as the cycle number CS, and assign a weight W to the cycle number CS , through the formula The cycle number score CSs of the energy storage unit is obtained.

3. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 2, characterized in that: The process of obtaining the comprehensive score of the fast-charging and discharging energy storage unit and the comprehensive score of the long-life energy storage unit is as follows: By the formula KSs = W SOC ×SOCs+W Tw ×Tw+W RT ×RTs to obtain the comprehensive score KSs of the fast-charging and discharging energy storage unit; By the formula SMs = W SOC ×SOCs+W Tw ×Tw+W CS ×CSs to obtain the comprehensive score SMs of the long-life energy storage unit.

4. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 3, characterized in that: The process of obtaining excellent energy storage units and ordinary energy storage units is as follows: Based on the comprehensive score KSs of the fast-charging and discharging energy storage unit and the comprehensive score SMs of the long-life energy storage unit, the comprehensive score of each energy storage unit is compared with the comprehensive score threshold of the energy storage unit; If the comprehensive score of the energy storage unit is greater than or equal to the comprehensive score threshold of the energy storage unit, the energy storage unit is determined as an excellent energy storage unit; If the comprehensive score of the energy storage unit is less than the comprehensive score threshold of the energy storage unit, the energy storage unit is defined as a common energy storage unit.

5. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 4, characterized in that: Based on the excellent energy storage unit and the ordinary energy storage unit, the load data of the hybrid energy storage system when the time subunit is in the middle moment is obtained; Integrate all load data in the time sub-unit to obtain a load data set; Obtain the load mean and load standard deviation of the load data set to obtain the standard value FZB of the load data; Based on the standard value FZB of the load data, create the feature FZZ for the load data lagged by i time subunits t-i , among which, FZB t-i Represents the load data of the ti-th time subunit, where t is the time point and i is the number of preset time points in the past.

6. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 5, characterized in that: Based on the standard value FZB of the load data, a characteristic value is created for each time subunit, which is recorded as the time subunit characteristic TSJ n , where n = 1, 2, ..., m, m is the total number of time subunits, and the feature of the nth time subunit can be expressed as TSJ n =n; Integrate all time sub-unit features to obtain a time sub-unit dataset; Get the mean and standard deviation of the time subunit feature of the time subunit data set to obtain the standardized feature TSS of the time subunit feature n .

7. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 6, characterized in that: Based on standardized features TSS n and hysteresis characteristic FZZ t-i , through the formula Get the predicted load demand FZB wei ; Among them, FZZ t-i,wei is the actual load data of the first i time subunits in the future prediction period, TSS n,wei The future prediction period belongs to the nth time subunit of the day, b i Indicates the hysteresis characteristic FZZ t-i,wei The coefficient of i+n TSS is the time feature n,wei The coefficient of , l is the number of lagged features used, and m represents the number of time sub-units divided within a day.

8. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 7, characterized in that: Based on the predicted load demand FZBwei, obtain the current available discharge power Pe of the energy storage system; By the formula ZGL=FZB wei -Pe obtains the total power demand ZGL of the hybrid energy storage system; If the total power demand ZGL is greater than 0, a charging signal is generated; If the total power demand ZGL is less than 0, a discharge signal is generated.

9. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 8, characterized in that: Based on the charging signal, the power of the excellent energy storage unit and the ordinary energy storage unit is allocated, specifically: Based on the charging signal, the maximum charging power PCD of the hybrid energy storage system is obtained max and maximum discharge power PFD max ; The total power demand ZGL is combined with the maximum charging power PCD of the hybrid energy storage system max Make comparisons; If the total power demand ZGL is less than or equal to the maximum charging power PCD of the hybrid energy storage system max , then it is determined that the excellent energy storage unit can fully bear the charging demand, through the formula YX cd =min(ZGL,PCD max ) Get the charging power YX of the excellent energy storage unit cd ; If the total power demand ZGL is greater than the maximum charging power PCD of the hybrid energy storage system max , it is determined that the excellent energy storage unit can only partially bear the charging demand, and the ordinary energy storage unit bears the remaining power demand; PT by formula cd =min(ZGL-PCD max , PCD max ) to obtain the charging power PT of the ordinary energy storage unit cd .

10. The method for intelligently allocating charging and discharging power for a hybrid energy storage system according to claim 9, characterized in that: Based on the discharge signal, the power of the excellent energy storage unit and the ordinary energy storage unit is allocated, specifically: Based on the discharge signal, the absolute value of the total power demand ZGL is compared with the maximum charging power PCD of the energy storage system. max Make comparisons; If the absolute value of the total power demand ZGL is less than or equal to the maximum discharge power PCD of the hybrid energy storage system max , then the excellent energy storage unit can fully bear the discharge demand, through the formula YX fd =min(ZGL,PFD max ) to obtain the discharge power YX of the excellent energy storage unit fd ; If the absolute value of the total power demand ZGL is greater than the maximum discharge power PCD of the energy storage system max , it is determined that the excellent energy storage unit can only partially bear the discharge demand, and the ordinary energy storage unit bears the remaining power demand; PT by formula fd =min(ZGL|-PCD max , PCD max ) to obtain the discharge power PT of the common energy storage unit fd .

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