Hybrid energy storage system optimal configuration method and device based on IGA and medium

Abstract

The invention relates to the technical field of hybrid energy storage system configuration optimization, in particular to an IGA-based hybrid energy storage system optimization configuration method and device and a medium, and the method comprises the following steps: selecting a control variable, constructing a constraint condition and a target function, and establishing a hybrid energy storage system capacity configuration model; selecting chromosome codes, setting an initial population, and constructing a fitness function based on a target function; adopting an elite selection strategy, directly reserving the first K chromosomes with the highest fitness to filial generations, and selecting the remaining individuals by adopting a roulette method; constructing an individual adaptive strategy to calculate a crossover probability and a mutation probability, and performing crossover and mutation processing on chromosomes; and after population iteration is completed, solving to obtain an optimal configuration scheme of the hybrid energy storage system. According to the method, the IGA is used for optimizing system capacity configuration, the universality is high, the application range is wide, different requirements can be met only by replacing corresponding parameters, and cost reduction and efficiency improvement can be achieved in the project purchasing stage.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage system configuration optimization, and in particular to an IGA-based hybrid energy storage system optimization configuration method, device, and medium. Background Art

[0002] Compared to traditional single-energy storage technologies, hybrid energy storage combines multiple energy storage methods, leveraging the advantages of multiple technologies. This addresses the shortcomings of single-energy storage technologies while also overcoming issues such as low efficiency and high costs. To fully leverage the advantages of hybrid energy storage systems in terms of performance, service life, and system cost, the key lies in optimizing the capacity configuration and coordinated energy control of the energy and power storage units within the hybrid energy storage system.

[0003] Existing hybrid energy storage system configuration optimization methods fail to fully consider the balance between energy-type energy storage and power-type energy storage, making it difficult to fully utilize the performance of the hybrid energy storage system.

[0004] In summary, there is an urgent need for an IGA-based hybrid energy storage system optimization configuration method, equipment and medium to solve existing problems. Summary of the Invention

[0005] The present invention aims to provide a hybrid energy storage system optimization configuration method, device and medium based on IGA. The specific technical solutions are as follows:

[0006] An IGA-based hybrid energy storage system optimization configuration method includes the following steps:

[0007] S1: Select control variables, construct constraints and objective functions, and establish a capacity configuration model for the hybrid energy storage system;

[0008] S2: Select chromosome encoding, set the initial population, and construct the fitness function based on the objective function;

[0009] S3: Using the elite selection strategy, the top K chromosomes with the highest fitness are directly retained to the offspring, and the remaining individuals are selected using the roulette wheel method;

[0010] S4: Construct an individual adaptive strategy to calculate the crossover probability and mutation probability, and perform crossover and mutation processing on chromosomes;

[0011] S5: After completing the population iteration, the optimal configuration scheme of the hybrid energy storage system is obtained.

[0012] Optionally, in S1, the control variable is the number of series and parallel energy storage units in the hybrid energy storage system. The expression of the control variable X is as follows:

[0013] X=[NES ,N EP ,N PS ,N PP ];

[0014] Among them, N ES Indicates the number of energy storage units connected in series, N EP Indicates the number of energy storage units connected in parallel, N PS Indicates the number of power type energy storage units connected in series, N PP Indicates the number of power storage units connected in parallel.

[0015] Optionally, in S1, the constraints include power constraints, energy constraints, voltage constraints, and discharge rate constraints;

[0016] The power constraint expression is as follows:

[0017] P E >P B +P F ;

[0018] Among them, P E Represents the output power of the hybrid energy storage system, P B Indicates the system power loss, P F Indicates that the load requires power; the expression of energy constraint is as follows:

[0019] E E >E B +E F ;

[0020] Among them, E E Represents the single output energy of the hybrid energy storage system, E B The system loss energy is E F Indicates the energy required for a single load;

[0021] The expression of voltage constraint is as follows:

[0022] U E_l ≤U E ≤U E_u ;

[0023] Among them, U E Indicates the voltage of energy storage unit, U E_u and U E_l Respectively represent the upper and lower limits of the operating voltage of the energy storage unit;

[0024] The expression of discharge rate constraint is as follows:

[0025] 0<C E ≤C E_u ;

[0026] Among them, C E Indicates the discharge rate of energy storage unit, C E_u Indicates the upper limit of the discharge rate of the energy storage unit.

[0027] Optionally, in S1, an objective function is constructed based on the system weight, service life, and annual average cost. The objective function is expressed as follows:

[0028]

[0029] Among them, k1 and k2 are the weight coefficients of annual average cost and system weight respectively; R max ,R min are the maximum and minimum values ​​of the annual average cost of a single iteration respectively; m max ,m min are the maximum and minimum values ​​of the system weight in a single iteration, respectively; α and β are adjustment values ​​used to prevent the denominator from being zero; R represents the average annual cost, and m represents the system weight.

[0030] Optionally, in S1, the expressions for system weight and average annual cost are as follows:

[0031] m=m E ·N ES ·N EP +m P ·N PS ·N PP +m Q ;

[0032]

[0033] Among them, m E Represents the mass of a single energy storage unit module, m P Represents the mass of a single power storage unit module, m Q Indicates the weight of the control system and the box; R E Indicates the unit price of energy storage unit module, Y E Indicates the service life of the energy storage unit module, R P Indicates the unit price of power storage unit module, Y P Indicates the service life of the power type energy storage unit module, M E represents the cost of energy storage unit management system, M P represents the cost of the power-type energy storage unit management system, and C represents the average annual maintenance fee of the system.

[0034] Optionally, in S2, the fitness function is expressed as follows:

[0035] F = 1-G;

[0036] Where F represents the fitness function.

[0037] Optionally, in S4, crossover is to randomly select 5 positions in the two chromosomes and exchange the genes at the corresponding positions; mutation is to randomly select 3 genes on the parent chromosome and change 0 to 1 and 1 to 0.

[0038] Optionally, in S5, the crossover probability P c and mutation probability P m The calculation expression is as follows:

[0039]

[0040] Among them, P c1 For a larger crossover probability, P c2 is a smaller crossover probability, f′ is the maximum fitness of the two individuals to be crossed, is the average fitness of individuals in the population, f max is the maximum value of the individual fitness of the population, P m1 For a larger mutation probability, P m2 is a smaller mutation probability, and f is the fitness of the individual to be mutated.

[0041] Additionally, the present invention also includes a computer device comprising a memory and a processor;

[0042] The memory is used to store a computer program that can be executed on the processor;

[0043] The processor is used to implement the steps of the hybrid energy storage system optimization configuration method as described above when executing the computer program.

[0044] In addition, the present invention also includes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the hybrid energy storage system optimization configuration method as described above are implemented.

[0045] The application of the technical solution of the present invention has the following beneficial effects:

[0046] The hybrid energy storage system optimization configuration method proposed in this invention comprehensively considers factors such as output power, stored energy, system weight, service life, and cost, and utilizes IGA to optimize system capacity configuration. It is highly versatile and has a wide range of applications. Simply replacing the corresponding parameters can meet different needs, enabling cost reduction and efficiency improvement during the project procurement phase. The method uses the number of energy-type and power-type energy storage units connected in series and parallel as control variables, performs binary conversion on these units, and constructs chromosomes for an improved genetic algorithm. This method fully considers the synergy between energy-type and power-type energy storage units, fully leveraging the performance of the hybrid energy storage system.

[0047] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 It is a flowchart of the steps of the hybrid energy storage system optimization configuration method in the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0051] This embodiment addresses the capacity optimization configuration problem of energy-type energy storage units and power-type energy storage units in a hybrid energy storage system. A hybrid energy storage system optimization configuration method based on IGA is proposed. This method comprehensively considers output power, stored energy, system weight, service life, and cost, selects appropriate control variables, and uses output power, stored energy, voltage, and current as constraints to construct an objective function that integrates system weight, service life, and cost. An improved genetic algorithm (IGA) is then used to find the optimal ratio of different energy storage types, thereby reducing costs and increasing efficiency.

[0052] like Figure 1 As shown, this embodiment provides an IGA-based hybrid energy storage system optimization configuration method, including the following steps:

[0053] S1: Select control variables, construct constraints and objective functions, and establish a capacity configuration model for the hybrid energy storage system;

[0054] S2: Select chromosome encoding, set the initial population, and construct a fitness function based on the objective function; in this embodiment, the chromosome encoding preferably adopts binary encoding;

[0055] S3: Using the elite selection strategy, the top K chromosomes with the highest fitness are directly retained to the offspring, and the remaining individuals are selected using the roulette wheel method;

[0056] S4: Construct an individual adaptive strategy to calculate the crossover probability and mutation probability, and perform crossover and mutation processing on chromosomes;

[0057] S5: After completing the population iteration, the optimal configuration scheme of the hybrid energy storage system is obtained.

[0058] Optionally, in S1, the control variable is the number of series and parallel energy storage units in the hybrid energy storage system. The expression of the control variable X is as follows:

[0059] X=[N ES ,N EP ,N PS ,N PP ];

[0060] Among them, N ES Indicates the number of energy storage units connected in series, N EP Indicates the number of energy storage units connected in parallel, N PS Indicates the number of power type energy storage units connected in series, N PP Indicates the number of power storage units connected in parallel.

[0061] In S1, this embodiment constructs a constraint function based on constraints such as output power, stored energy, voltage, and current. Specifically, the constraints in this embodiment include power constraints, energy constraints, voltage constraints, and discharge rate constraints.

[0062] The power constraint expression is as follows:

[0063] P E >P B +P F ;

[0064] Among them, P E Represents the output power of the hybrid energy storage system, P B Indicates the system power loss, P F Indicates that the load requires power; the expression of energy constraint is as follows:

[0065] E E >E B +E F ;

[0066] Among them, E E Represents the single output energy of the hybrid energy storage system, E B The system loss energy is E F Indicates the energy required for a single load;

[0067] The expression of voltage constraint is as follows:

[0068] U E_l ≤U E≤U E_u ;

[0069] Among them, U E Indicates the voltage of energy storage unit, U E_u and U E_l Respectively represent the upper and lower limits of the operating voltage of the energy storage unit;

[0070] The expression of discharge rate constraint is as follows:

[0071] 0<C E ≤C E_u ;

[0072] Among them, C E Indicates the discharge rate of energy storage unit, C E_u Indicates the upper limit of the discharge rate of the energy storage unit.

[0073] In S1, this embodiment comprehensively considers the system weight, service life and annual average cost to construct an objective function. Specifically, the objective function is constructed based on the system weight, service life and annual average cost. The expression of the objective function is as follows:

[0074]

[0075] Among them, k1 and k2 are the weight coefficients of annual average cost and system weight respectively; R max ,R min are the maximum and minimum values ​​of the annual average cost of a single iteration respectively; m max ,m min are the maximum and minimum values ​​of the system weight in a single iteration, respectively; α and β are adjustment values ​​used to prevent the denominator from being zero; R represents the average annual cost, and m represents the system weight.

[0076] Furthermore, in S1, the expressions for system weight and annual average cost are as follows:

[0077] m=m E ·N ES ·N EP +m P ·N PS ·N PP +m Q ;

[0078]

[0079] Among them, m E Represents the mass of a single energy storage unit module, m P Represents the mass of a single power storage unit module, m Q Indicates the weight of the control system and the box; R E Indicates the unit price of energy storage unit module, YE Indicates the service life of the energy storage unit module, R P Indicates the unit price of power storage unit module, Y P Indicates the service life of the power type energy storage unit module, M E represents the cost of energy storage unit management system, M P represents the cost of the power-type energy storage unit management system, and C represents the average annual maintenance fee of the system.

[0080] Optionally, in S2, the fitness function is expressed as follows:

[0081] F = 1-G;

[0082] Where F represents the fitness function.

[0083] In S3, the roulette wheel method works as follows: the probability of each individual in the population being selected is proportional to the value of its fitness function. That is, individuals with higher fitness have a higher probability of being selected, but individuals with lower fitness also have a smaller probability of being selected and will not be directly eliminated. The probability of an individual being selected in the roulette wheel method is as follows:

[0084]

[0085] Among them, x i represents the i-th individual, F is the fitness function, and N is the population size.

[0086] Optionally, in S4, crossover is to randomly select 5 positions in the two chromosomes and exchange the genes at the corresponding positions; mutation is to randomly select 3 genes on the parent chromosome and change 0 to 1 and 1 to 0.

[0087] Optionally, in S4, the crossover probability P c and mutation probability P m The calculation expression is as follows:

[0088]

[0089] Among them, P c1 For a larger crossover probability, P c2 is a smaller crossover probability, f′ is the maximum fitness of the two individuals to be crossed, is the average fitness of individuals in the population, f max is the maximum value of the individual fitness of the population, P m1 For a larger mutation probability, P m2 is a smaller mutation probability, and f is the fitness of the individual to be mutated.

[0090] In S5, after each population iteration, the population is decoded and the fitness function is recalculated. The decoding method is: the chromosome is separated according to each control variable, and the control variable is converted from binary form to real number.

[0091] The method of this embodiment uses an improved genetic algorithm to solve the capacity configuration model of the hybrid energy storage system, and takes the number of series and parallel connections of energy-type energy storage units and power-type energy storage units as control variables, which are converted into binary and then constructed into chromosomes of the improved genetic algorithm. Output power, stored energy, service life, voltage, etc. are added as constraints to the iterative process of the improved genetic algorithm. The objective function is constructed by weighting the normalized annual average cost and system weight. The population size, maximum evolutionary generations, number of elite individuals, crossover probability, mutation probability, lithium battery management system cost, supercapacitor management system cost, and average annual maintenance fee of the system are set, and the population is initialized to obtain an initial population. The fitness of the population is then calculated, and crossover and mutation operations are performed on the population until the maximum number of iterations is reached to obtain the optimal configuration solution.

[0092] This embodiment is used to optimize the configuration of a hybrid energy storage system, as follows:

[0093] The energy storage portion of this hybrid energy storage system consists of a power storage unit connected in series with a DC / DC converter, then connected in parallel with an energy storage unit. These two components jointly power the load. The power storage unit is a supercapacitor, and the energy storage unit is a lithium iron phosphate battery. Their parameters are shown in Table 1. The hybrid energy storage system parameters are shown in Table 2.

[0094] Table 1 Energy storage unit parameters

[0095]

[0096]

[0097] Table 2 System parameters

[0098] project parameter Output power ≥2MW Duration 20s Energy Storage ≥1200MJ Nominal voltage 800V Weight limit 10000kg Service life of lithium iron phosphate batteries 3 years Excessive service life 10 years

[0099] The method of this embodiment is applied to optimize the configuration of the hybrid energy storage system. The number of lithium battery strings and parallel connections, as well as the number of supercapacitor strings and parallel connections, are used as control variables. These are converted into binary to construct chromosomes of the improved genetic algorithm (IGA). The maximum number of series connections is set to 10 bits of binary, and the maximum number of parallel connections is set to 8 bits of binary. These are connected in a string and encoded into a chromosome of length 36. Voltage constraints, current constraints, power constraints, and energy constraints are added as constraints to the algorithm. The population size is set to 300, the maximum number of evolution generations is 100, the number of elite individuals is 30, and the crossover probability P is 0. c1 =0.9, P c2 =0.6, mutation probability Pm1 =0.1, P m2 =0.01, lithium battery management system cost M E = 50,000 yuan, supercapacitor management system cost M P = 50,000 yuan, the average annual system maintenance cost C = 10,000 yuan, the control system and cabinet weigh 1000 kg, and during the iteration process, the upper limit of the lithium battery voltage is 1500V and the lower limit is 800V. The upper limit of the supercapacitor voltage is 1600V and the lower limit is 1200V. The available capacity of the lithium battery is calculated as 80%, and the supercapacitor provides energy at half the voltage drop. The population is initialized to obtain the initial population. The fitness of the population is then calculated, and crossover and mutation operations are performed on the population until the maximum number of iterations is reached to obtain the optimal configuration solution.

[0100] The optimal configuration is shown in Table 3: 317 lithium-ion battery cells are connected in series and 8 in parallel, with a unit cost of 608,640 yuan and a weight of 2,789.6 kg. 493 supercapacitors are connected in series and 9 in parallel, with a unit cost of 798,660 yuan and a weight of 2,218.5 kg. The average annual cost is 314,412.67 yuan, and the total weight is 6,008.1 kg.

[0101] Table 3 Optimal configuration of hybrid energy storage system

[0102] project lithium battery supercapacitors Number of series connections 317 493 Number of parallel connections 8 9 Total number 2536 4437 Cost / yuan 608640 798660 Weight / kg 2789.6 2218.5

[0103] In addition, this embodiment also includes a computer device, including a memory and a processor;

[0104] The memory is used to store a computer program that can be executed on the processor;

[0105] The processor is used to implement the steps of the hybrid energy storage system optimization configuration method as described above when executing the computer program.

[0106] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0107] The computer device may be a mobile phone, desktop computer, laptop, PDA, cloud server, or other computing device. The computer device may include, but is not limited to, a processor and memory. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0108] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device and connects various parts of the entire computer device using various interfaces and lines.

[0109] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0110] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0111] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned hybrid energy storage system optimization configuration method are implemented.

[0112] This embodiment provides an optimized configuration method for hybrid energy storage systems based on an integrated genetic algorithm (IGA). This method comprehensively considers factors such as output power, stored energy, system weight, service life, and cost. It selects appropriate control variables, uses output power, stored energy, voltage, and current as constraints, and constructs an objective function that integrates system weight, service life, and cost. It then uses an improved genetic algorithm (IGA) to find the optimal ratio of different energy storage types, achieving cost reduction and efficiency improvement. This method is highly versatile and widely applicable; simply by substituting appropriate parameters, it can meet different needs, enabling cost reduction and efficiency improvement during the project procurement phase.

[0113] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0114] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A hybrid energy storage system optimization configuration method based on IGA, characterized in that: The following steps are involved: S1: Select control variables, construct constraints and objective functions, and establish a capacity configuration model for the hybrid energy storage system; S2: Select chromosome encoding, set the initial population, and construct the fitness function based on the objective function; S3: Using the elite selection strategy, the top K chromosomes with the highest fitness are directly retained to the offspring, and the remaining individuals are selected using the roulette wheel method; S4: Construct an individual adaptive strategy to calculate the crossover probability and mutation probability, and perform crossover and mutation processing on chromosomes; S5: After completing the population iteration, the optimal configuration scheme of the hybrid energy storage system is obtained.

2. The IGA-based hybrid energy storage system optimization configuration method according to claim 1, characterized in that: In S1, the control variable is the number of series and parallel energy storage units in the hybrid energy storage system. The expression of the control variable X is as follows: X=[N ES ,N EP ,N PS ,N PP ]; Among them, N ES Indicates the number of energy storage units connected in series, N EP Indicates the number of energy storage units connected in parallel, N PS Indicates the number of power type energy storage units connected in series, N PP Indicates the number of power storage units connected in parallel.

3. The IGA-based hybrid energy storage system optimization configuration method according to claim 2, characterized in that: In S1, the constraints include power constraint, energy constraint, voltage constraint and discharge rate constraint; The power constraint expression is as follows: P E >P B +P F ; Among them, P E Represents the output power of the hybrid energy storage system, P B Indicates the system power loss, P F Indicates the power required by the load; The expression of energy constraint is as follows: AND E >And B +E F ; Among them, E E Represents the single output energy of the hybrid energy storage system, E B The system loss energy is E F Indicates the energy required for a single load; The expression of voltage constraint is as follows: IN E_l ≤U E ≤U E_u ; Among them, U E Indicates the voltage of energy storage unit, U E_u and U E_l Respectively represent the upper and lower limits of the operating voltage of the energy storage unit; The expression of discharge rate constraint is as follows: 0<C E ≤C E_u ; Among them, C E Indicates the discharge rate of energy storage unit, C E_u Indicates the upper limit of the discharge rate of the energy storage unit.

4. The IGA-based hybrid energy storage system optimization configuration method according to claim 3, characterized in that: In S1, the objective function is constructed based on the system weight, service life and annual average cost. The expression of the objective function is as follows: Among them, k1 and k2 are the weight coefficients of annual average cost and system weight respectively; R max ,R min are the maximum and minimum values ​​of the annual average cost of a single iteration respectively; m max ,m min are the maximum and minimum values ​​of the system weight in a single iteration, α and β are adjustment values ​​used to prevent the denominator from being zero; R represents the average annual cost, and m represents the system weight.

5. The IGA-based hybrid energy storage system optimization configuration method according to claim 4, characterized in that: In S1, the expressions for system weight and average annual cost are as follows: m=m E ·N ES ·N EP +m P ·N PS ·N PP +m Q ; Among them, m E Represents the mass of a single energy storage unit module, m P Represents the mass of a single power storage unit module, m Q Indicates the weight of the control system and the box; R E Indicates the unit price of energy storage unit module, Y E Indicates the service life of the energy storage unit module, R P Indicates the unit price of power type energy storage unit module, Y P Indicates the service life of the power type energy storage unit module, M E represents the cost of energy storage unit management system, M P represents the cost of the power-type energy storage unit management system, and C represents the average annual maintenance fee of the system.

6. The IGA-based hybrid energy storage system optimization configuration method according to claim 5, characterized in that: In S2, the fitness function is expressed as follows: F = 1-G; Where F represents the fitness function.

7. The IGA-based hybrid energy storage system optimization configuration method according to claim 6, characterized in that: In S4, crossover is to randomly select 5 positions in two chromosomes and exchange the genes at the corresponding positions; mutation is to randomly select 3 genes on the parent chromosome and change 0 to 1 and 1 to 0.

8. The IGA-based hybrid energy storage system optimization configuration method according to claim 7, characterized in that: In S4, the crossover probability P c and mutation probability P m The calculation expression is as follows: Among them, P c1 For a larger crossover probability, P c2 is a smaller crossover probability, f′ is the maximum fitness of the two individuals to be crossed, is the average fitness of individuals in the population, f max is the maximum value of the individual fitness of the population, P m1 For a larger mutation probability, P m2 is a smaller mutation probability, and f is the fitness of the individual to be mutated.

9. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program that can be executed on the processor; The processor is configured to implement the steps of the IGA-based hybrid energy storage system optimization configuration method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the IGA-based hybrid energy storage system optimization configuration method according to any one of claims 1 to 8.

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