Energy storage unit power distribution optimization method based on energy efficiency and state parameters

Abstract

The invention discloses an energy storage unit power distribution optimization method based on energy efficiency and state parameters. The method comprises the following steps: setting a power distribution strategy considering the energy efficiency of an energy storage unit; comprising the following steps: calculating the efficiency of an energy storage converter and the efficiency of an energy storage battery pack, and multiplying the efficiency of the energy storage converter by the efficiency of the energy storage battery pack to obtain the efficiency of an energy storage unit; setting a first objective function, and performing power distribution among the energy storage units based on the first objective function; and / or, setting a power distribution strategy considering SOC and SOH of the energy storage unit; the method includes setting second, third and fourth objective functions; when the energy storage unit participates in the electric energy market and needs to be charged, power distribution needs to meet the first objective function and the second objective function; when the energy storage unit participates in the electric energy market and needs to discharge, the power distribution needs to meet the first and third objective functions; when the energy storage unit participates in frequency modulation, power distribution should meet the first and fourth objective functions.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution of each energy storage unit inside an energy storage power station, and particularly relates to an optimization method for power distribution of energy storage units based on energy efficiency and state parameters. Background Art

[0002] The energy efficiency of the energy storage unit will directly affect the cost of the energy storage power station. A lower energy efficiency will not only lead to excessive energy loss, but also cause the energy storage unit to perform excessive charge and discharge cycles in order to achieve the expected output power, accelerating the aging of the energy storage. In addition, in the energy storage power station, the state of charge (SOC) is an important indicator characterizing the actual available capacity of the energy storage and is also a key parameter for evaluating the consistency of the energy storage. Due to differences in impedance, aging degree, and operating temperature between batteries, the phenomenon of SOC inconsistency will occur between each energy storage unit. This inconsistency will cause overcharging or over-discharging of some energy storage units, ultimately leading to a significant reduction in the overall cycle life of the energy storage. From the perspective of technical economy, the coordinated balance of the state of charge and the state of health (SOH) essentially constructs a performance management system for the energy storage power station over the entire life cycle. This system combines the immediate operating efficiency and long-term reliability, while ensuring the economic benefits of the energy storage, is also conducive to maximizing social benefits.

[0003] Existing research, on the one hand, ignores the difference in energy conversion efficiency of energy storage units under different load rates; on the other hand, the balanced control of the SOC and SOH of energy storage units is usually considered separately, lacking a unified coordination control strategy, resulting in the energy storage power station being difficult to exert its best performance and reducing the overall operating efficiency. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an optimization method for power distribution of energy storage units based on energy efficiency and state parameters.

[0005] In a first aspect, an embodiment of the present invention provides an optimization method for power distribution of energy storage units based on energy efficiency and state parameters, the method comprising:

[0006] Setting a power distribution strategy considering the energy efficiency of the energy storage unit; including: calculating the efficiency of the energy storage converter and the efficiency of the energy storage battery pack, multiplying the efficiency of the energy storage converter by the efficiency of the energy storage battery pack to obtain the efficiency of the energy storage unit; setting a first objective function, the first objective function being used to minimize the total energy loss of the energy storage units participating in the regulation, and performing power distribution between each energy storage unit based on the first objective function;

[0007] and / or,

[0008] Set a power distribution strategy considering the SOC and SOH of energy storage units; including: setting a second objective function according to the first weight, the charging priority of available energy storage units based on SOC, and the output power of energy storage units; setting a third objective function according to the second weight, the discharging priority of available energy storage units based on SOC, and the output power of energy storage units; setting a fourth objective function according to the third weight, the number of energy storage units participating in frequency modulation, and the health status of energy storage units participating in frequency modulation; when the energy storage unit participates in the electricity energy market and needs to be charged, the power distribution should satisfy the first objective function and the second objective function; when the energy storage unit participates in the electricity energy market and needs to discharge, the power distribution should satisfy the first objective function and the third objective function; when the energy storage unit participates in frequency modulation, the power distribution should satisfy the first objective function and the fourth objective function.

[0009] In a second aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, the memory is coupled to the processor; wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the above-mentioned energy storage unit power distribution optimization method based on energy efficiency and state parameters.

[0010] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned energy storage unit power distribution optimization method based on energy efficiency and state parameters.

[0011] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, it implements the above-mentioned energy storage unit power distribution optimization method based on energy efficiency and state parameters.

[0012] Compared with the prior art, the beneficial effects of the present invention are:

[0013] The present invention considers the energy conversion efficiency difference of energy storage units under different load rates, and by setting a power distribution strategy considering the energy efficiency of energy storage units and a power distribution strategy considering the SOC and SOH of energy storage units, the SOC and SOH of energy storage units are balanced and controlled, and a unified coordinated control strategy is constructed; the method of the present invention can not only realize adaptive power distribution based on the state parameters of energy storage units, effectively avoid the problem that the overall performance of the system decreases due to the excessive aging of individual units, but also show obvious advantages in energy loss control, thereby prolonging the overall service life of the energy storage power station and improving the overall operation efficiency of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0015] Figure 1 Schematic flowchart of the energy storage unit power distribution optimization method based on energy efficiency and state parameters provided by the embodiments of the present invention;

[0016] Figure 2 Schematic flowchart of the DM - MOGA algorithm provided by the embodiments of the present invention;

[0017] Figure 3 Schematic diagram of an electronic device provided by the embodiments of the present invention. Detailed implementation manners

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] It should be noted that, without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0020] As Figure 1 shown, the present invention provides an energy storage unit power distribution optimization method based on energy efficiency and state parameters. The method includes:

[0021] Set a power distribution strategy considering the energy efficiency of the energy storage unit; including: calculating the efficiency of the energy storage converter and the efficiency of the energy storage battery pack, multiplying the efficiency of the energy storage converter by the efficiency of the energy storage battery pack to obtain the efficiency of the energy storage unit; setting a first objective function, where the first objective function is used to minimize the total energy loss of the energy storage units participating in regulation, and performing power distribution among the energy storage units based on the first objective function;

[0022] And / or,

[0023] Set a power distribution strategy considering the SOC and SOH of the energy storage unit; including: setting a second objective function according to the first weight, the charging priority of the available energy storage unit based on SOC, and the output power of the energy storage unit; setting a third objective function according to the second weight, the discharging priority of the available energy storage unit based on SOC, and the output power of the energy storage unit; setting a fourth objective function according to the third weight, the number of energy storage units participating in frequency modulation, and the health status of the energy storage units participating in frequency modulation; when the energy storage unit participates in the electricity energy market and needs to be charged, the power distribution should satisfy the first objective function and the second objective function; when the energy storage unit participates in the electricity energy market and needs to discharge, the power distribution should satisfy the first objective function and the third objective function; when the energy storage unit participates in frequency modulation, the power distribution should satisfy the first objective function and the fourth objective function.

[0024] Furthermore, the power conversion system (PCS) in the energy storage power station is responsible for power conversion and control. When the energy storage is charging, the PCS converts the alternating current input from the power grid into direct current to efficiently charge the battery pack; when discharging, it inversely converts the direct current output from the battery pack into alternating current that meets the requirements of the power grid and smoothly incorporates it into the power grid, thus realizing the power supply to the external load. The PCS also has the function of power regulation for the energy storage power station and plays an indispensable role in aspects such as smoothing power fluctuations, participating in power grid frequency modulation and voltage regulation, and optimizing power quality, effectively improving the interaction ability and adaptability between the energy storage power station and the power grid.

[0025] However, there are inevitably energy losses during the operation of the PCS. With the change of the PCS load rate, its energy losses usually show a certain pattern. At low load rates, the switching losses account for a relatively large proportion, while at high load rates, the conduction losses gradually become the main part of the losses. Therefore, the load rate of the PCS is used to represent its efficiency, and the specific expression is as follows:

[0026]

[0027] In the formula, η PCS represents the efficiency of the power conversion system, and a, b, and c are all fitting quantities of the losses of the power conversion system varying with current, and L PCS is the load rate of the power conversion system.

[0028] Furthermore, the energy consumption characteristics of the energy storage battery pack are relatively complex. On the one hand, losses are generated due to the conventional ohmic internal resistance, and this kind of loss is closely related to the current; on the other hand, due to the continuous change of the current, polarization internal resistance losses will also be caused. In addition, the energy consumption level of the battery is not only determined by the internal resistance, and factors such as battery temperature, SOC, and the magnitude of charge and discharge power will all have an impact on it.

[0029] When the battery SOC is within the specific range of 0.2 to 0.9, the battery efficiency can basically maintain a stable state. Given that the charge and discharge range of the energy storage power station is within this range, for the energy storage unit, the influence of the SOC state on the energy consumption of the battery pack is relatively weak and can be ignored here. In addition, during the entire operation of the energy storage power station, temperature control systems such as air cooling or liquid cooling equipped in the station will continuously play a role. Through a series of precise temperature monitoring and adjustment mechanisms, the temperature in the station is accurately and stably controlled within the preset temperature value range. Given this stable temperature environment condition, when constructing the energy consumption model of the battery pack, there is no need to consider the factor of temperature, which can simplify the complexity of the model to a certain extent.

[0030] In summary, the energy consumption model of the energy storage battery pack is affected by the ohmic internal resistance, polarization internal resistance, current, and charge and discharge power. Since during the operation of the energy storage power station, the power value on the DC side can be calculated by the product of the DC voltage and current. And the power value on the DC side is very similar to the command power value in terms of the change trend. Based on this characteristic, using the load rate of the energy storage PCS to equivalently replace the current helps to simplify the analysis process and can reflect the power-related characteristics and laws of the energy storage during operation to a certain extent. Therefore, in this example, the efficiency expression of the energy storage battery pack is as follows:

[0031]

[0032] In the formula, A is the sum of the ohmic internal resistance and polarization internal resistance of the entire battery pack (not equal to the linear sum of the resistances of all series-connected single cells in the battery pack), B is the efficiency correction fitting quantity of the battery pack, and L PCS is the load rate of the energy storage converter.

[0033] Furthermore, in order to accurately and effectively evaluate the above-built PCS efficiency model and energy storage battery pack efficiency model, the following model evaluation indicators are specifically set: the coefficient of determination, which is mainly used to reflect the fitting effect of the model, as follows:

[0034]

[0035] In the formula, X SSE is the sum of squared residuals, X SST is the total sum of squared deviations, and R 2 is closer to 1, indicating that the model has a better fitting effect on the data. n is the number of fitting data points, y i is the fitting data value of the model, is the true value of the existing data, is the average value of the existing data.

[0036] In summary, in this example, the efficiency of the energy storage unit is obtained by multiplying the PCS efficiency and the battery pack efficiency, and the expression is as follows:

[0037] η i = η PCS · η batt

[0038] In the formula, η i represents the efficiency of the i-th energy storage unit, η PCS represents the efficiency of the energy storage converter, and η batt represents the efficiency of the energy storage battery pack.

[0039] When performing power distribution among energy storage units, the total energy loss of the energy storage units participating in regulation should be minimized, and the following objective function needs to be satisfied:

[0040]

[0041] In the formula, K is the number of energy storage units participating in regulation, P i is the output power value of the i-th energy storage unit, and η i is the efficiency of the i-th energy storage unit.

[0042] Furthermore, the battery charge and discharge characteristic experiments show that when the battery SOC exceeds 90% during charging or is lower than 10% during discharging, performance and safety issues may be caused by polarization reactions inside. Therefore, during the power distribution process among all energy storage units, charging is prohibited when SOC exceeds 90%, and discharging is prohibited when SOC is lower than 10%. This will not be elaborated further later. When the energy storage only participates in the electricity energy market during a certain period, that is, the power command is only positive (or negative) within that hour. First, the charging priority and discharging priority of available energy storage units are sorted according to SOC. The smaller the value, the higher the priority. Specifically, it is as follows:

[0043]

[0044] In the formula, S i is the SOC value of the i-th energy storage unit.

[0045] According to the total power command, determine the number of energy storage units participating in the response. The expression is as follows:

[0046] K = [|P ea | / (P N · β)] + 1

[0047] In the formula, [α] is the rounded value calculation of α, β represents the minimum charge and discharge efficiency limit of the energy storage unit (in this example, β = 0.8), N is the total number of energy storage units in the energy storage power station (K ≤ N), and P eaRepresents the total power output of all energy storage units, P N Represents the maximum limit value of the power output of the energy storage unit.

[0048] Furthermore, set the minimum and maximum limit values of the power output of the energy storage unit, and the expression is as follows:

[0049] β·P N ≤P i ≤P N

[0050] Furthermore, if charging is required in this hour, when the energy storage performs the charging task, the energy storage units with SOC lower than the average value should receive adjustment instructions prior to those with SOC higher than the average value. And among multiple energy storage units with SOC lower than or higher than the average value, instructions are allocated according to the priority of the energy storage unit's SOH from high to low. For this purpose, the following second objective function is constructed:

[0051]

[0052]

[0053] In the formula, P i is the output power of energy storage unit i, P ea is the total power instruction, w i , σ are both weights, is the average value of the state of charge of all energy storage units participating in the regulation, SOC i is the SOC value of the i-th energy storage unit, H i is the health state of the energy storage unit, ΔE i is the cumulative charge and discharge amount of energy storage unit i, n 100％ is the number of cycles of the energy storage at the percentage charge and discharge depth, E i is the rated capacity of energy storage unit i, S is the set of energy storage units with SOC higher than the average value among all energy storage units participating in the regulation, M is the set of energy storage units with SOC lower than the average value, P i c (t) is the charging power of energy storage unit i at time t, P i d (t) is the discharging power of energy storage unit i at time t, Δt is the time interval.

[0054] For energy storage units with SOC lower than the average value, their weight And for energy storage units with SOC higher than the average value, their weight Since the denominators are respectively the sum of the health states within their respective sets, and usually, is less than Therefore, under the same health condition, the weight of the energy storage unit with an SOC lower than the average value will be greater, so it can preferentially undertake the charging instruction. Similarly, among multiple energy storage units with an SOC lower than the average value, the weight is proportional to their respective health conditions, that is, the better the health condition, the i greater, and its weight w i will also be greater. Therefore, the energy storage unit with a good health condition will undertake more charging instructions.

[0055] Furthermore, when the energy storage unit participates in the electric energy market and needs to be charged, the power distribution should satisfy the first objective function and the second objective function; the expressions are as follows:

[0056]

[0057] Furthermore, if it needs to discharge in that hour, when the energy storage executes the discharging task, the priority of the energy storage unit to undertake the power instruction is opposite to that during charging. At this time, the objective function is similar to that during charging, but the weight setting is different; the expression for setting the third objective function is as follows:

[0058]

[0059] In the formula, is the average value of the state of charge of all energy storage units participating in the regulation, SOC i is the SOC value of the i-th energy storage unit, H i is the health condition of the energy storage unit i, S is the set of energy storage units with an SOC higher than the average value among all energy storage units participating in the regulation, and M is the set of energy storage units with an SOC lower than the average value.

[0060] It should be noted that the priority of the SOC state is reflected in: when discharging, due to the opposite size relationship of the denominator compared with charging, under the same health condition, the energy storage unit with an SOC higher than the average value has a greater weight and can preferentially undertake the power instruction, meeting the design requirements. The priority of the health condition is reflected within their respective intervals. The better the health condition, the greater the weight, that is, the energy storage unit with a good health condition will undertake more power instructions.

[0061] Furthermore, when the energy storage unit participates in the electric energy market and needs to discharge, the power distribution should satisfy the first objective function and the third objective function; the expressions are as follows:

[0062]

[0063] In the formula, since the power is set to be negative when discharging, in order to minimize the energy loss, an absolute value symbol needs to be added to the power in F1.

[0064] Furthermore, if the energy storage participates in frequency regulation within a certain hour, that is, the power command can be positive or negative within that hour. Different from participating in the electric energy market, since the frequency regulation command changes every 2 seconds and there is a possibility of charge-discharge state transition between the previous and the next time, and considering that the responsive frequency regulation command is energy-neutral within a certain period of time, it is necessary to preferentially select energy storage units with an SOC of around 0.5.

[0065] First, rank the energy storage units participating in frequency regulation according to the SOC of the energy storage units as follows:

[0066]

[0067] In the formula, Y i represents the priority of energy storage unit i, α is the weight coefficient, and SOC i is the SOC value of the i-th energy storage unit; the larger Y i , the higher the priority of energy storage unit i.

[0068] Furthermore, determine according to the winning frequency regulation capacity:

[0069] J = [P fr / (P N ·β)] + 1

[0070] J ≤ N

[0071] Among these energy storage units, allocate frequency regulation commands according to the priority from high to low of the health status of the energy storage units. The constructed fourth objective function is as follows:

[0072]

[0073] In the formula, J represents the number of energy storage units participating in frequency regulation, h i is the normalized value of the health status of energy storage unit i, y i is the normalized value of the charge state of energy storage unit i, H i is the health status of energy storage unit i, H j is the health status of energy storage unit j, Y i is the charge state of energy storage unit i, and Y j is the charge state of energy storage unit j.

[0074] When the SOC of energy storage unit i is closer to 0.5, its weight Y i is also larger. Therefore, under the same health status, this type of energy storage unit can undertake more frequency regulation commands; under the condition of similar SOC, the larger h i , the better the health status of the energy storage unit among those participating in frequency regulation can undertake more frequency regulation commands, meeting the requirements.

[0075] In addition, the power balance constraints must be met:

[0076]

[0077] Where P fr is the winning bid capacity of frequency modulation, r n is the nth FM signal, and its value range is -1 to 1.

[0078] Furthermore, when the energy storage unit participates in frequency modulation, power allocation should satisfy the first objective function and the fourth objective function, which are expressed as follows:

[0079]

[0080] Furthermore, in this example, Figure 2 As shown, the DM-MOGA algorithm can be used to solve multi-objective functions (i.e., solve the combined first objective function and the second objective function, the combined first objective function and the third objective function, and the combined first objective function and the fourth objective function); the DM-MOGA (Decision Making-Multi-Objective Genetic Algorithm) algorithm is a multi-objective genetic algorithm combined with decision preferences. Its core idea is to introduce the decision maker's preference information in the evolutionary process, so as to more effectively guide the search process to develop towards the area expected by the decision maker. The mathematical expression of the algorithm mainly includes the basic form of the multi-objective optimization problem, the weight vector representation of the decision preference, and the fitness calculation based on the decision preference.

[0081] The multi-objective optimization problem can generally be expressed as:

[0082] minF(x)=[f1(x),f2(x),...,f m (x)] T

[0083] Satisfy the constraints:

[0084]

[0085] Where F(x) is a vector consisting of m objective functions, g i (x) is an inequality constraint, h j (x) is an equality constraint, x l and x u are the upper and lower bounds of the decision variables, respectively.

[0086] The decision maker’s preference information can be expressed by a utility function:

[0087]

[0088] where ω i is the weight of the decision maker for the i-th objective.

[0089] In summary, the present invention considers the energy conversion efficiency difference of the energy storage unit under different load rates, and constructs a unified coordinated control strategy by setting a power distribution strategy considering the energy efficiency of the energy storage unit and a power distribution strategy considering the SOC and SOH of the energy storage unit, so as to balance the control of the SOC and SOH of the energy storage unit; the method of the present invention can not only realize adaptive power distribution based on the state parameters of the energy storage unit, effectively avoid the problem that the overall performance of the system decreases due to the excessive aging of individual units, but also shows obvious advantages in energy loss control, thereby extending the overall service life of the energy storage power station and improving the overall operation efficiency of the energy storage power station.

[0090] Correspondingly, the present application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage unit power distribution optimization method based on energy efficiency and state parameters as described above. As Figure 3 shown, it is a hardware structure diagram of any device with data processing capabilities where the energy storage unit power distribution optimization method based on energy efficiency and state parameters provided by the embodiment of the present invention is located. Except for Figure 3 the processors, memory, and network interfaces shown, any device with data processing capabilities where the device in the embodiment is located usually includes other hardware according to the actual functions of the any device with data processing capabilities, which will not be elaborated here.

[0091] Correspondingly, the present application also provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the energy storage unit power distribution optimization method based on energy efficiency and state parameters as described above is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a FlashCard, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the any device with data processing capabilities, and may also be used to temporarily store data that has been output or will be output.

[0092] The above embodiments are only used to illustrate the design concept and features of the present invention, and the purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made according to the principles and design concepts disclosed by the present invention are within the protection scope of the present invention.

Claims

1. An optimization method for power distribution of energy storage units based on energy efficiency and state parameters, characterized in that The method includes: Setting a power distribution strategy considering the energy efficiency of the energy storage unit; including: calculating the efficiency of the energy storage converter and the efficiency of the energy storage battery pack, multiplying the efficiency of the energy storage converter by the efficiency of the energy storage battery pack to obtain the efficiency of the energy storage unit; setting a first objective function, where the first objective function is used to minimize the total energy loss of the energy storage units participating in regulation, and performing power distribution among the respective energy storage units based on the first objective function; and / or, Setting a power distribution strategy considering the SOC and SOH of the energy storage unit; including: setting a second objective function according to the first weight, the charging priority of the available energy storage units based on the SOC, and the output power of the energy storage unit; setting a third objective function according to the second weight, the discharging priority of the available energy storage units based on the SOC, and the output power of the energy storage unit; setting a fourth objective function according to the third weight, the number of energy storage units participating in frequency modulation, and the health status of the energy storage units participating in frequency modulation; when the energy storage unit participates in the electricity energy market and needs to be charged, the power distribution should satisfy the first objective function and the second objective function; when the energy storage unit participates in the electricity energy market and needs to be discharged, the power distribution should satisfy the first objective function and the third objective function; when the energy storage unit participates in frequency modulation, the power distribution should satisfy the first objective function and the fourth objective function.

2. The power allocation optimization method for an energy storage unit based on energy efficiency and state parameters according to claim 1, wherein The expression for the efficiency of the energy storage converter is: where η PCS represents the efficiency of the energy storage converter, and a, b, and c are all fitting quantities of the energy storage converter loss varying with the current, and L PCS is the load rate of the energy storage converter; The expression for the efficiency of the energy storage battery pack is: In the formula, A is the sum of the ohmic internal resistance and the polarization internal resistance of the entire battery pack, and B is the efficiency correction fitting quantity of the battery pack.

3. An optimization method for power distribution of an energy storage unit based on energy efficiency and state parameters according to claim 1 or 2, characterized in that The expression for the first objective function is: Wherein, F1 is the first objective function, K is the number of energy storage units participating in regulation, P i is the output power value of the i-th energy storage unit, and η i is the efficiency of the i-th energy storage unit.

4. An optimization method for power distribution of an energy storage unit based on energy efficiency and state parameters according to claim 1, characterized in that, The process of setting the power distribution strategy considering the SOC and SOH of the energy storage unit further includes: Calculate the charging priority X of the available energy storage units for the SOC c and the discharging priority X d , and the expression is as follows: where S i is the SOC value of the i-th energy storage unit; Determining the number of energy storage units participating in the response according to the total power command, and the expression is as follows: K = [|P ea | / (P N ·β)] + 1 In the formula, β represents the minimum charge-discharge efficiency limit of the energy storage unit, N is the total number of energy storage units in the energy storage power station, and P ea represents the total power output by all energy storage units, and P N represents the maximum limit of the output power of the energy storage unit.

5. The power allocation optimization method of an energy storage unit based on energy efficiency and state parameters according to claim 1 or 4, characterized in that, When the energy storage unit participates in the electricity energy market and needs to be charged, the power distribution should satisfy the first objective function and the second objective function, including: where w i and σ are both weights, and X c represents the charging priority of the SOC for available energy storage units; Among them, the weight w i has the following expression: Wherein, is the average value of the state of charge of all energy storage units participating in the regulation, SOC i is the SOC value of the i-th energy storage unit, H i is the health state of the energy storage unit i, S is the set of energy storage units with SOC higher than the average value among all energy storage units participating in the regulation, M is the set of energy storage units with SOC lower than the average value, ΔE i is the cumulative charge and discharge amount of the energy storage unit i, n 100％ is the number of cycles of the energy storage at the percentage of charge and discharge depth, E i is the rated capacity of the energy storage unit i, P i c (t) is the charging power of the energy storage unit i at time t, P i d (t) is the discharging power of the energy storage unit i at time t, and Δt is the time interval.

6. The power distribution optimization method for an energy storage unit based on energy efficiency and state parameters according to claim 1, characterized in that When the energy storage unit participates in the electricity energy market and needs to be discharged, the power distribution should satisfy the first objective function and the third objective function, including: where ν i and σ are both weights, and X d represents the discharge priority of the SOC for available energy storage units; Among them, the weight ν i has the following expression: Wherein, is the average value of the state of charge of all energy storage units participating in regulation, SOC i is the SOC value of the i-th energy storage unit, H i is the health state of the energy storage unit i, S is the set of energy storage units with SOC higher than the average value among all energy storage units participating in regulation, and M is the set of energy storage units with SOC lower than the average value.

7. A method for optimizing the power distribution of an energy storage unit based on energy efficiency and state parameters according to claim 1, characterized in that When the energy storage unit participates in frequency modulation, the power distribution should satisfy the first objective function and the fourth objective function, including: Where J represents the number of energy storage units participating in frequency modulation, and h i is the normalized value of the health state of energy storage unit i, and y i is the normalized value of the state of charge of energy storage unit i; Among them, the expression for the number J of energy storage units participating in frequency modulation is as follows: J = [P fr / (P N ·β)] + 1 where P fr is the winning bid capacity of frequency modulation; where, H i is the health state of energy storage unit i, H j is the health state of energy storage unit j, Y i is the state of charge of energy storage unit i, Y j is the state of charge of energy storage unit j.

8. An electronic device, comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the power distribution optimization method of the energy storage unit based on energy efficiency and state parameters according to any one of claims 1-7 above.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the power distribution optimization method of the energy storage unit based on energy efficiency and state parameters according to any one of claims 1-7.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, it implements the power distribution optimization method of the energy storage unit based on energy efficiency and state parameters according to any one of claims 1-7.

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