Lithium battery-flow battery hybrid energy storage system power distribution method

Abstract

The invention relates to the technical field of energy storage, in particular to a lithium battery-flow battery hybrid energy storage system power distribution method, which comprises the following steps of: 1, selecting a power distribution mode, 2, distributing a lithium battery and a flow battery according to a capacity proportion, 3, preferentially distributing the lithium battery or the flow battery, and 4, optimizing power distribution based on an artificial bee colony algorithm. Step 5, dynamic adjustment and optimization of a power distribution mode, step 6, analysis and prediction of system operation data, optimization of a target function by using an artificial bee colony algorithm, and realization of optimized power distribution based on an intelligent algorithm, thereby not only improving the accuracy and scientificity of power distribution, but also enabling the system to have a stronger adaptive ability, and improving the reliability of power distribution. The method can better meet the requirements of a complex and changeable power system, improves the intelligent level of the system, and provides powerful support for the efficient operation of an energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a power distribution method for a lithium battery-flow battery hybrid energy storage system. Background Art

[0002] With increasing demands for storage duration and intrinsic safety in large-scale energy storage power stations, the industrialization of flow batteries has rapidly advanced. Flow batteries offer high safety, long cycle life, and excellent charge and discharge performance, but they have shortcomings in scenarios requiring fast response or high power output. Hybrid energy storage systems combining flow batteries with lithium batteries, combining the fast response of lithium batteries with the long cycle life of flow batteries, can meet the diverse energy storage response needs of power systems.

[0003] The power distribution of liquid flow-lithium hybrid energy storage systems must meet practical application factors such as system voltage stability, capacity allocation, and the maximum charge and discharge power of the energy storage system. Commonly used power distribution methods for hybrid energy storage systems include low-pass filtering, multiple sliding mean filtering, fuzzy control and dynamic mode division, and collaborative optimization. However, due to the high initial investment and construction costs and low energy density of liquid flow batteries, their current application is limited, leaving a gap in research on power distribution methods for liquid flow-lithium hybrid energy storage systems. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a power distribution method for a lithium battery-flow battery hybrid energy storage system, thereby solving the technical problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A power distribution method for a lithium battery-flow battery hybrid energy storage system comprises the following steps:

[0007] Step 1: Select the power distribution mode. Based on the current charge and discharge status of the energy storage system and the load type, select the power distribution mode of the lithium battery-flow battery hybrid energy storage system.

[0008] Step 2: Ensure that the power output of the two batteries is proportional to their capacities through simple proportional allocation. This is suitable for scenarios where system response speed is not a priority and balanced utilization of the capacities of the two batteries is required.

[0009] Step 3: The power of the lithium battery or liquid flow battery in the hybrid energy storage system will be allocated first. If the hybrid energy storage system needs to respond quickly, the lithium battery priority allocation mode can be selected; if a long charging and discharging time is required, the liquid flow battery priority allocation mode can be selected;

[0010] Step 4: Establish an objective function with the goal of maximizing the remaining available energy storage capacity of the lithium battery-flow battery hybrid energy storage system to ensure that the lithium battery and flow battery can reserve energy storage capacity;

[0011] Step 5: Dynamically adjust and optimize the power allocation mode. When the system operation requirements cannot be met, the system will automatically switch to other modes.

[0012] Step 6: During the system operation, a large amount of operation data collected will be used for analysis and prediction through analysis of system operation data under different modes.

[0013] In one possible implementation, there are three modes in the power allocation mode selection:

[0014] (1) Lithium batteries and flow batteries are allocated according to capacity ratio;

[0015] (2) Lithium batteries or flow batteries are given priority;

[0016] (3) Optimized power allocation based on artificial bee colony algorithm.

[0017] In one possible implementation, when the lithium battery and the flow battery are allocated according to the capacity ratio, the power distribution of the hybrid energy storage system is calculated as follows:

[0018]

[0019] Among them, P Li,max is the rated power of the lithium battery in the hybrid energy storage system, P fb,max is the rated power of the flow battery, P hess is the total rated power of the hybrid energy storage system, P target (t) is the target power, P Li (t) is the power allocated to the lithium battery, P fb (t) is the power allocated to the flow battery; the following conditions must be met when using this allocation mode:

[0020] P target (t)<P Li,max +P fb,max

[0021] That is, the target power needs to be less than the sum of the rated powers of the lithium battery and the flow battery.

[0022] In a possible implementation, when lithium batteries or flow batteries are prioritized, the power allocation of the hybrid energy storage system is calculated as follows:

[0023]

[0024] Let P hess,max=P Li,max +P fb,max

[0025]

[0026] Among them, when choosing flow battery priority allocation:

[0027]

[0028] In a hybrid energy storage system, power is allocated preferentially to lithium batteries or flow batteries based on actual demand.

[0029] In one possible implementation, in the optimized power allocation based on the artificial bee colony algorithm, the objective function established according to this goal is:

[0030]

[0031] Among them, C Li (t-1) and C fb (t-1) is the amount of electricity stored in the lithium battery and the flow battery at the end of the previous cycle, T is the execution cycle of the control instruction, P output (t) is the output power of the hybrid energy storage system, P load (t) is the load demand power; when the hybrid energy storage system is in the discharging state, the energy storage capacity is the capacity stored by the energy storage system; when the hybrid energy storage system is in the charging state, the energy storage capacity is the difference between the rated capacity of the hybrid energy storage system and the current stored electricity.

[0032] In one possible implementation, in the power allocation optimization based on the artificial bee colony algorithm, after determining the objective function, constraints need to be set for optimizing the objective function using the artificial bee colony algorithm. The constraints of the hybrid energy storage system are as follows:

[0033] (1) During the operation of the hybrid energy storage system, the current storage capacity of the lithium battery and the flow battery does not exceed the set maximum and minimum values:

[0034] C Li,min ≤C Li (t)≤E Li ,C fb,min ≤C fb (t)≤E fb

[0035] (2) The output power of the hybrid energy storage system is subject to the age, configuration capacity, and conditions such as lithium-ion batteries and flow batteries. In actual use, it cannot exceed the limit:

[0036]

[0037] Among them, Phess (t) is the output power of the hybrid energy storage system, P hess,maxd is the maximum discharge power of the hybrid energy storage system, P hess,maxc is the maximum charging power of the hybrid energy storage system;

[0038] (3) To ensure that the output power of lithium batteries and flow batteries obtained by solving the objective function is guaranteed to be executed, the following scheduling constraints are set:

[0039]

[0040] This constraint indicates that the capacity of the hybrid energy storage system cannot be negative when smoothing the unbalanced power demanded by the load, so as to avoid the situation where the hybrid energy storage system is out of operation due to insufficient capacity.

[0041] In a possible implementation, in the optimization power allocation based on the artificial bee colony algorithm, the artificial bee colony algorithm is used to optimize the above objective function. In the artificial bee colony algorithm, bees are divided into employed bees, observer bees and scout bees. The parameters of the initial nectar source random generation process are set, the maximum number is S×D (D is the dimension), and the number of times the nectar source is abandoned is determined to be t L , the number of iterations terminated; the number of nectar sources S×D is equal to the number of employed bees and follower bees. Initializing the nectar source is to assign a random value within the value range to all dimensions of each nectar source through a formula, thereby randomly generating S×D initial nectar sources. The employed bee corresponding to the i-th nectar source searches for a new nectar source according to the following nectar source formula:

[0042] x′ id =x id +rand[0,1](x id -x kd ),k≠i

[0043] Among them, x id is the solution of the i-th honey source, i∈{1,2,...,SD}, x′ id As a new possible solution, the newly generated solution X′ i ={x′ i1 ,x′ i2 ,...,x′ iD} and the previous solution X i ={x i1 ,x i2 ,...,x iD} for comparison, a greedy strategy is adopted to retain the better solution; the fitness of the nectar source is calculated as follows:

[0044]

[0045] Among them, S is the number of employed bees.

[0046] In one possible implementation, in the optimized power allocation based on the artificial bee colony algorithm, if the observer bees find that the local solution is better than the current solution during the process of searching for a local solution near the current solution, a replacement process is performed, and each observer bee selects a nectar source based on probability. The probability formula is:

[0047]

[0048] For the selected nectar source, the observer bees search for new possible solutions according to the above probability formula. When all employed bees and observer bees have searched the entire search space, the fitness value of a nectar source is within a given step (i.e., the control parameter t L ) is not improved, the nectar source is discarded, and the hired bees corresponding to the nectar source become scout bees, which search for new solutions using the following formula:

[0049]

[0050] in, and are the upper and lower bounds of the d-th dimension respectively.

[0051] In one possible implementation, during the dynamic adjustment and optimization of the power allocation mode, when a sudden increase in load demand occurs and the current mode prioritizes flow batteries, but the power output of the flow batteries cannot meet the demand, the system will automatically switch to a lithium battery priority allocation mode to take advantage of the lithium battery's rapid response capability.

[0052] At the same time, based on the analysis of long-term operating data, the system will optimize the power allocation mode. If the system efficiency of the capacity ratio allocation mode is lower than expected within a certain operating cycle, the system will improve the overall performance of the system by adjusting the allocation ratio.

[0053] In one possible implementation, the advantages and disadvantages of each mode are evaluated in the analysis and prediction of the system operation data, and a basis is provided for future system design and optimization. In the scenario application, the optimization allocation mode based on the artificial bee colony algorithm shows significant performance advantages, and the system will give priority to recommending this mode.

[0054] In addition, by analyzing historical and real-time data, the system predicts future power demand and load change trends. If the system detects that load demand increases regularly during specific periods of the day, the system will adjust the power distribution mode in advance to ensure that sufficient power support can be provided during peak load periods.

[0055] Beneficial effects compared with existing technologies:

[0056] 1. This solution uses an artificial bee colony algorithm to optimize the objective function and achieve optimized power distribution based on an intelligent algorithm. This not only improves the accuracy and scientific nature of power distribution, but also makes the system more adaptable, enabling it to better cope with complex and changing power system demands, enhances the system's intelligence level, and provides strong support for the efficient operation of the energy storage system.

[0057] 2. This solution dynamically adjusts and optimizes the power distribution of the hybrid energy storage system. When the system cannot meet operating requirements, it can automatically switch to other modes. This prevents system crashes when load demand suddenly increases or system anomalies occur, enhances system reliability, and ensures stable power supply.

[0058] 3. In this solution, three power distribution modes can be flexibly selected according to different scenario requirements, which can optimize the energy utilization of each battery in the lithium battery-flow battery hybrid energy storage system. Compared with traditional systems operating in a single mode, it can effectively improve the overall system efficiency and reduce energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings.

[0060] Figure 1 It is a schematic diagram of the process of the present invention;

[0061] Figure 2 Schematic diagram of the artificial bee colony algorithm of the present invention. DETAILED DESCRIPTION

[0062] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various forms, so the present invention is not limited to the embodiments described below.

[0063] The technical solution in the embodiments of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0064] Example:

[0065] Please refer to Figure 1 As shown, this embodiment introduces a power distribution method for a lithium battery-flow battery hybrid energy storage system, including the following steps:

[0066] Step 1: Select the power distribution mode. Based on the current charge and discharge status of the energy storage system and the load type, select the power distribution mode of the lithium battery-flow battery hybrid energy storage system. There are three modes:

[0067] (1) Lithium batteries and flow batteries are allocated according to capacity ratio;

[0068] (2) Lithium batteries or flow batteries are given priority;

[0069] (3) Optimized power allocation based on artificial bee colony algorithm.

[0070] Step 2: Lithium batteries and flow batteries are allocated according to capacity ratio

[0071] When (1) lithium batteries and flow batteries are allocated according to capacity ratio, the power distribution of the hybrid energy storage system is calculated as follows:

[0072]

[0073] Among them, P Li,max is the rated power of the lithium battery in the hybrid energy storage system, P fb,max is the rated power of the flow battery, P hess is the total rated power of the hybrid energy storage system, P target (t) is the target power, P Li (t) is the power allocated to the lithium battery, P fb (t) is the power allocated to the flow battery; the following conditions must be met when using this allocation mode:

[0074] P target (t)<P Li,max +P fb,max

[0075] That is, the target power needs to be less than the sum of the rated powers of the lithium battery and the flow battery.

[0076] Step 3: Prioritize lithium batteries or flow batteries

[0077] When (2) lithium battery or flow battery priority allocation is selected, and lithium battery priority allocation is selected, the power allocation calculation of the hybrid energy storage system is as follows:

[0078]

[0079] Let P hess,max =P Li,max +P fb,max

[0080]

[0081] Among them, when choosing flow battery priority allocation:

[0082]

[0083] In this allocation mode, the power of the lithium battery or liquid flow battery in the hybrid energy storage system will be allocated first. If the hybrid energy storage system needs to respond quickly, the lithium battery priority allocation mode can be selected; if a long charging and discharging time is required, the liquid flow battery priority allocation mode can be selected.

[0084] Step 4: Optimizing power allocation based on artificial bee colony algorithm

[0085] When (3) is selected as the optimized power allocation based on the artificial bee colony algorithm, the objective function is established with the goal of maximizing the remaining available energy storage capacity of the lithium battery-flow battery hybrid energy storage system to the total capacity, and ensuring that the lithium battery and flow battery can reserve a certain amount of energy storage capacity as much as possible. The objective function established based on this goal is:

[0086]

[0087] Among them, C Li (t-1) and C fb (t-1) is the amount of electricity stored in the lithium battery and the flow battery at the end of the previous cycle, T is the execution cycle of the control instruction, P output (t) is the output power of the hybrid energy storage system, P load (t) is the load demand power; when the hybrid energy storage system is in the discharging state, the available energy storage capacity is the capacity stored by the energy storage system; when the hybrid energy storage system is in the charging state, the available energy storage capacity is the difference between the rated capacity of the hybrid energy storage system and the current stored power.

[0088] After determining the objective function, constraints need to be set when optimizing the objective function using the artificial bee colony algorithm. The constraints of the hybrid energy storage system are as follows:

[0089] (1) During the operation of the hybrid energy storage system, the current storage capacity of the lithium battery and the flow battery cannot exceed the set maximum and minimum values:

[0090] C Li,min ≤C Li (t)≤E Li ,C fb,min ≤C fb (t)≤E fb

[0091] (2) The output power of the hybrid energy storage system is subject to the age, configuration capacity, and conditions such as lithium-ion batteries and flow batteries. In actual use, it cannot exceed the limit:

[0092]

[0093] Among them, P hess (t) is the output power of the hybrid energy storage system, P hess,maxdis the maximum discharge power of the hybrid energy storage system, P hess,maxc is the maximum charging power of the hybrid energy storage system;

[0094] (3) To ensure that the output power of lithium batteries and flow batteries solved by the objective function is executable, the following schedulability constraints are set:

[0095]

[0096] This constraint indicates that when the hybrid energy storage system is smoothing out the unbalanced power demanded by the load, the available capacity cannot be negative, thus preventing the hybrid energy storage system from exiting operation due to insufficient available capacity.

[0097] The artificial bee colony algorithm is used to optimize the above objective function. The algorithm flow is as follows: Figure 2 As shown, the following steps are included:

[0098] In the artificial bee colony algorithm, bees are divided into employed bees, observer bees and scout bees. The parameters of the initial nectar source random generation process are set, the maximum number is S×D (D is the dimension), and the number of times the nectar source is abandoned is determined to be t L , the number of iterations terminated; the number of nectar sources S×D is equal to the number of employed bees and follower bees. Initializing the nectar source is to assign a random value within the value range to all dimensions of each nectar source through a formula, thereby randomly generating S×D initial nectar sources. The employed bee corresponding to the i-th nectar source searches for a new nectar source according to the following nectar source formula:

[0099] x′ id =x id +rand[0,1](x id -x kd ),k≠i

[0100] Among them, x id is the solution of the i-th honey source, i∈{1,2,...,SD}, x′ id As a new possible solution, the newly generated possible solution X i ′={x′ i1 ,x′ i2 ,…,x′ iD} and the previous solution X i ={x i1 ,x i2 ,…,x iD} for comparison, a greedy strategy is adopted to retain the better solution; the fitness of the nectar source is calculated as follows:

[0101]

[0102] Among them, S is the number of employed bees;

[0103] If the observer bee finds that the local solution is better than the current solution during the process of searching for a local solution near the current solution, it will perform a replacement process. Each observer bee selects a nectar source based on probability. The probability formula is:

[0104]

[0105] For the selected nectar source, the observer bees search for new possible solutions according to the above probability formula. When all employed bees and observer bees have searched the entire search space, if the fitness value of a nectar source is within a given step (i.e., the control parameter t L ) is not improved, the nectar source is discarded, and the hired bees corresponding to the nectar source become scout bees, which search for new possible solutions using the following formula:

[0106]

[0107] in, and are the upper and lower bounds of the d-th dimension respectively.

[0108] Step 5: Dynamic adjustment and optimization of power distribution mode

[0109] Dynamically adjust and optimize the power allocation mode. When the system operation requirements cannot be met, the system will automatically switch to other modes. When the load demand suddenly increases, if the current mode is liquid flow battery priority allocation, but the power output of the liquid flow battery cannot meet the demand, the system will automatically switch to lithium battery priority allocation mode to take advantage of the lithium battery's fast response capability.

[0110] At the same time, based on the analysis of long-term operating data, the system will optimize the power allocation mode. If the system efficiency of the capacity ratio allocation mode is lower than expected within a certain operating cycle, the system will improve the overall performance of the system by adjusting the allocation ratio or optimizing the algorithm parameters.

[0111] Step 6: Analysis and prediction of system operation data

[0112] During system operation, a large amount of collected operational data will be used for analysis and prediction. By analyzing the system operation data under different modes, the advantages and disadvantages of each mode can be evaluated, and a basis for future system design and optimization can be provided. In scenario applications, the optimization allocation mode based on the artificial bee colony algorithm shows significant performance advantages, and the system will give priority to recommending this mode.

[0113] In addition, by analyzing historical and real-time data, the system can predict future power demand and load change trends. If the system detects that load demand increases regularly during specific periods of the day, the system will adjust the power distribution mode in advance to ensure that sufficient power support can be provided during peak load periods.

[0114] Finally, it should be noted that the above embodiments are merely examples for the purpose of illustrating the present invention and are not intended to limit the embodiments. Those skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. It is not necessary and impossible to provide an exhaustive list of all embodiments. However, obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A power distribution method for a lithium battery-flow battery hybrid energy storage system, characterized in that: The following steps are involved: Step 1: Select the power distribution mode. Based on the current charge and discharge status of the energy storage system and the load type, select the power distribution mode of the lithium battery-flow battery hybrid energy storage system. Step 2: Lithium batteries and flow batteries are allocated based on their capacity ratio. This simple proportional allocation ensures that the power output of the two batteries is proportional to their capacity. This is suitable for scenarios where system response speed is not a high requirement and balanced utilization of the capacity of the two batteries is required. Step 3: Prioritize lithium batteries or flow batteries. The power of lithium batteries or flow batteries in the hybrid energy storage system will be allocated first. If you need a fast response speed for the hybrid energy storage system, you can choose the lithium battery priority allocation mode; if you need a long charge and discharge time, you can choose the flow battery priority allocation mode. Step 4: Optimize power allocation based on the artificial bee colony algorithm, establish an objective function with the goal of maximizing the remaining available energy storage capacity of the lithium battery-flow battery hybrid energy storage system to ensure that the lithium battery and flow battery can reserve energy storage capacity; Step 5: Dynamically adjust and optimize the power allocation mode. Dynamically adjust and optimize the power allocation mode. When the system operation requirements cannot be met, the system will automatically switch to other modes. Step 6: Analysis and prediction of system operation data. During the system operation process, a large amount of operation data collected will be used for analysis and prediction through analysis of system operation data under different modes.

2. A power distribution method for a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: There are three power distribution modes: (1) Lithium batteries and flow batteries are allocated according to capacity ratio; (2) Lithium batteries or flow batteries are given priority; (3) Optimized power allocation based on artificial bee colony algorithm.

3. The power distribution method of a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: In the case where the lithium battery and flow battery are allocated according to the capacity ratio, the power distribution of the hybrid energy storage system is calculated as follows: Among them, P Li,max is the rated power of the lithium battery in the hybrid energy storage system, P fb,max is the rated power of the flow battery, P hess is the total rated power of the hybrid energy storage system, P target (t) is the target power, P Li (t) is the power allocated to the lithium battery, P fb (t) is the power allocated to the flow battery; the following conditions must be met when using this allocation mode: P target (t)<P Li,max +P fb,max That is, the target power needs to be less than the sum of the rated powers of the lithium battery and the flow battery.

4. The power distribution method of a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: In the lithium battery or flow battery priority allocation, when the battery is prioritized, the power allocation calculation of the hybrid energy storage system is as follows: Let P hess,max =P Li,max +P fb,max Among them, when choosing flow battery priority allocation: In a hybrid energy storage system, power is allocated preferentially to lithium batteries or flow batteries based on actual demand.

5. The power distribution method of a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: In the optimization power allocation based on the artificial bee colony algorithm, the objective function established based on this goal is: Among them, C Li (t-1) and C fb (t-1) is the amount of electricity stored in the lithium battery and the flow battery at the end of the previous cycle, T is the execution cycle of the control instruction, P output (t) is the output power of the hybrid energy storage system, P load (t) is the load demand power; when the hybrid energy storage system is in the discharging state, the energy storage capacity is the capacity stored by the energy storage system; when the hybrid energy storage system is in the charging state, the energy storage capacity is the difference between the rated capacity of the hybrid energy storage system and the current stored electricity.

6. A power distribution method for a lithium battery-flow battery hybrid energy storage system according to claim 5, characterized in that: In the power allocation optimization based on the artificial bee colony algorithm, after determining the objective function, it is necessary to set constraints to optimize the objective function using the artificial bee colony algorithm. The constraints of the hybrid energy storage system are as follows: (1) During the operation of the hybrid energy storage system, the current storage capacity of the lithium battery and the flow battery does not exceed the set maximum and minimum values: C Li,min ≤C Li (t)≤E Li ,C fb,min ≤C fb (t)≤E fb (2) The output power of the hybrid energy storage system is subject to the age, configuration capacity, and conditions such as lithium-ion batteries and flow batteries. In actual use, it cannot exceed the limit: Among them, P hess (t) is the output power of the hybrid energy storage system, P hess,maxd is the maximum discharge power of the hybrid energy storage system, P hess,maxc is the maximum charging power of the hybrid energy storage system; (3) To ensure that the output power of lithium batteries and flow batteries obtained by solving the objective function is guaranteed to be executed, the following scheduling constraints are set: This constraint indicates that the capacity of the hybrid energy storage system cannot be negative when smoothing the unbalanced power demanded by the load, so as to avoid the situation where the hybrid energy storage system is out of operation due to insufficient capacity.

7. A power distribution method for a lithium battery-flow battery hybrid energy storage system according to claim 6, characterized in that: In the optimization power allocation based on the artificial bee colony algorithm, the artificial bee colony algorithm is used to optimize the above objective function. In the artificial bee colony algorithm, bees are divided into employed bees, observer bees and scout bees. The parameters of the initial nectar source random generation process are set, the maximum number is S×D (D is the dimension), and the number of times the nectar source is abandoned is determined to be t L , the number of iterations terminated; the number of nectar sources S×D is equal to the number of employed bees and follower bees. Initializing the nectar source is to assign a random value within the value range to all dimensions of each nectar source through a formula, thereby randomly generating S×D initial nectar sources. The employed bee corresponding to the i-th nectar source searches for a new nectar source according to the following nectar source formula: x′ id =x id +rand[0,1](x id -x kd ),k≠i Among them, x id is the solution of the i-th honey source, i∈{1,2,...,SD}, x′ id As a new possible solution, the newly generated solution X′ i ={x′ i1 ,x′ i2 ,...,x′ iD } and the previous solution X i ={x i1 ,x i2 ,...,x iD } for comparison, a greedy strategy is used to retain the better solution; the fitness of the nectar source is calculated as follows: Among them, S is the number of employed bees.

8. A power distribution method for a lithium battery-flow battery hybrid energy storage system according to claim 7, characterized in that: In the optimization power allocation based on the artificial bee colony algorithm, if the observer bees find that the local solution is better than the current solution during the process of searching for a local solution near the current solution, a replacement process is performed. Each observer bee selects a nectar source based on probability. The probability formula is: For the selected nectar source, the observer bees search for new possible solutions according to the above probability formula. When all employed bees and observer bees have searched the entire search space, the fitness value of a nectar source is within a given step (i.e., the control parameter t L ) is not improved, the nectar source is discarded, and the hired bees corresponding to the nectar source become scout bees, which search for new solutions using the following formula: in, and are the upper and lower bounds of the dth dimension respectively.

9. The power distribution method of a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: In the dynamic adjustment and optimization of the power distribution mode, when the load demand suddenly increases, the current mode is to prioritize the liquid flow battery, but the power output of the liquid flow battery cannot meet the demand. The system will automatically switch to the lithium battery priority distribution mode to take advantage of the rapid response capability of the lithium battery. At the same time, based on the analysis of long-term operating data, the system will optimize the power allocation mode. If the system efficiency of the capacity ratio allocation mode is lower than expected within a certain operating cycle, the system will improve the overall performance of the system by adjusting the allocation ratio.

10. The power distribution method of a lithium battery-flow battery hybrid energy storage system according to claim 1, characterized in that: In the analysis and prediction of the system operation data, the advantages and disadvantages of each mode are evaluated, and a basis is provided for future system design and optimization. In the scenario application, the optimization allocation mode based on the artificial bee colony algorithm shows significant performance advantages, and the system will give priority to recommending this mode; In addition, by analyzing historical and real-time data, the system predicts future power demand and load change trends. If the system detects that load demand increases regularly during specific periods of the day, the system will adjust the power distribution mode in advance to ensure that sufficient power support can be provided during peak load periods.

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