A method and apparatus for configuring an electric-hydrogen hybrid energy storage system

Abstract

This invention relates to the field of hybrid energy storage system technology, and particularly to a method and apparatus for configuring an electric-hydrogen hybrid energy storage system. The method includes: constructing a multi-objective optimization model for the electric-hydrogen hybrid energy storage system, wherein the objective function of the multi-objective optimization model is to minimize the total life-cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system; iteratively calculating the Pareto solution set of the multi-objective optimization model using a political optimization algorithm until the iteration termination condition is met, and outputting the optimal Pareto solution set; and calculating the optimal compromise solution of the optimal Pareto solution set using an entropy weight method with a gray target decision-making approach, thereby obtaining the optimal configuration scheme of the electric-hydrogen hybrid energy storage system. The optimal configuration scheme includes optimal installation nodes, configuration capacity, and configuration power, used to reduce the cost of configuring the electric-hydrogen hybrid energy storage system in the distribution network and to improve the problems of power loss, load fluctuation, and voltage fluctuation in the system.

Description

Technical Field

[0001] This invention relates to the field of hybrid energy storage system technology, and in particular to a method and apparatus for configuring an electric-hydrogen hybrid energy storage system. Background Technology

[0002] With the continuous development and improvement of energy storage technology, the combined use of different types of energy storage systems has gradually become a research hotspot for researchers, such as hybrid battery energy storage systems and hydrogen energy storage systems, which are electric-hydrogen hybrid energy storage systems.

[0003] Electric-hydrogen hybrid energy storage systems store electrical energy using both battery and hydrogen storage devices. When the cost of a hydrogen storage system is fixed, the ability to flexibly connect to the grid can be avoided by rationally configuring the capacity of the hydrogen storage devices, mitigating the capacity limitations of battery storage systems. However, the energy conversion efficiency of hydrogen storage systems is lower than that of battery storage systems, and their cost is higher. Therefore, how to rationally configure electric-hydrogen hybrid energy storage systems to reduce costs while minimizing power fluctuations and energy losses has become a pressing issue. Summary of the Invention

[0004] This invention provides a method and apparatus for configuring an electric-hydrogen hybrid energy storage system, which reduces the cost of configuring an electric-hydrogen hybrid energy storage system in a power distribution network and improves the problems of power loss, load fluctuation and voltage fluctuation in the system.

[0005] This invention provides a method for configuring an electric-hydrogen hybrid energy storage system, comprising:

[0006] A multi-objective optimization model for an electric-hydrogen hybrid energy storage system is constructed, with the objective function being to minimize the total life-cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system.

[0007] The Pareto solution set of the multi-objective optimization model is calculated iteratively using a political optimization algorithm until the iteration termination condition is met, and the optimal Pareto solution set is output.

[0008] The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method with entropy weight, and the optimal configuration scheme of the electric-hydrogen hybrid energy storage system is obtained. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

[0009] Optionally, the multi-objective optimization model for constructing the electric-hydrogen hybrid energy storage system includes:

[0010] Obtain the distribution network parameters and construct an objective function based on the obtained distribution network parameters to minimize the total life cycle loss, power loss, load fluctuation and voltage fluctuation.

[0011] The objective function includes:

[0012]

[0013] In the formula, f(x) is the objective function; f1 is the total life cycle loss cost, f2 is the power loss, f3 is the voltage fluctuation, and f4 is the load fluctuation; x is the decision variable; h(x) is the constraint condition, wherein the decision variable includes the installation node, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system; the constraint condition includes node power balance constraint, node voltage constraint, grid connection point power constraint, electric-hydrogen hybrid system capacity and power constraint, battery energy storage system charge and discharge constraint, and hydrogen energy storage system charge and discharge constraint;

[0014] The total lifecycle loss cost includes:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Among them, Q BESSs Q represents the total lifecycle cost of a battery energy storage system. HESSs The total lifecycle cost of a hydrogen energy storage system; T B For the investment cost of battery energy storage systems, T H The investment cost of a hydrogen energy storage system; W B For the maintenance costs of battery energy storage systems, W H Maintenance costs for hydrogen energy storage systems; Y B For the operating cost of battery energy storage systems, Y H The operating cost of a hydrogen energy storage system; G B For battery energy storage systems, GH The replacement cost of the hydrogen energy storage system; C B For the disposal and recycling costs of battery energy storage systems, C H The cost of disposal and recycling of hydrogen energy storage systems; μ CRF,B N represents the capital recovery factor; BESS Indicates the number of battery energy storage systems installed in the distribution network; c battery The cost of a single battery; c EPCD,B This represents the engineering, procurement, and construction costs of the battery energy storage system, as well as the developer costs; I sub It is a government subsidy; E BESS,i c is the capacity of the i-th battery energy storage system; FMC,B P represents the annual fixed maintenance cost of a single battery energy storage system. BESS,i The power of the i-th battery energy storage system; T is 24 hours; c pu (t) and c sel (t) represents the purchase and sale price of electricity; P ch,Bi (t) and P dis,Bi (t) represents the charging and discharging power of the i-th battery energy storage system; n B t and α represent the battery's lifespan and replacement cycles, respectively; α is the annual cost depreciation rate of the battery; r represents the discount rate calculated using the weighted average cost of capital; γ B It is the recycling efficiency of battery energy storage systems; c FC and c E The costs of fuel cells and electrolyzers are respectively; c HT and Q HT,i For the cost and capacity of hydrogen storage tanks; P HESS,i C is the power output of the i-th hydrogen energy storage system; EPCD,H Indicates the EPC cost of a hydrogen energy storage system; c FC and c E The costs of fuel cells and electrolyzers are respectively; c FMC,H This represents the annual maintenance cost of the fuel cell; P HESS,i P represents the power of the i-th hydrogen energy storage system. ch,Hi and P dis,Hi Q represents the charging and discharging power of the i-th hydrogen energy storage system; H,i q represents the total hydrogen production of the hydrogen storage system in one day; H Hydrogen production per kilowatt-hour of electricity; This represents the profit generated per kilogram of hydrogen; p H The figure represents the power generation per kilogram of hydrogen; μ and v are the ratios of hydrogen delivered to power generated; n H β represents the number of HESSs replacements; β represents the annual cost loss rate of the hydrogen energy storage system; γ represents the number of HESSs replacements. H For the recycling benefits of fuel cells;

[0027] The power loss includes:

[0028]

[0029] L represents the total number of interconnects in the electric-hydrogen hybrid system; R j I represents the resistance on the j-th tie line; t represents time. j Let be the current on the j-th tie line;

[0030] The load fluctuations include:

[0031]

[0032] Among them, P load P pv (t) and P wind These represent the load of the electric-hydrogen hybrid system, and the output of photovoltaic and wind power during time period t.

[0033] The voltage fluctuations include:

[0034]

[0035] In the formula, N nodes V represents the total number of nodes in the system. j Let be the voltage at node j; Let J be the average voltage of node j during time period T;

[0036] The node power balance constraint is:

[0037]

[0038] In the formula, P i (t) represents the active power injected into node i at time t; Q i (t) represents the reactive power injected into node i at time t; θ ij (t) represents the voltage phase angle difference between nodes i and j at time t; V i (t) and V j (t) represent the voltages at node i and node j during time period t, respectively; G ij and B ij These are the line conductance and susceptance between nodes i and j, respectively;

[0039] The node voltage constraint is:

[0040]

[0041] In the formula, and These are the upper and lower voltage limits for node i, respectively;

[0042] The power constraint at the grid connection point is:

[0043]

[0044] In the formula, and These are the lower and upper limits of active and reactive power at the grid connection point, respectively.

[0045] The capacity and power constraints of the aforementioned electric-hydrogen hybrid system are:

[0046]

[0047] In the formula, and These are the upper and lower limits of the capacity of the battery energy storage system. and These are the upper and lower power limits for battery energy storage systems. and These are the upper and lower limits of the capacity of hydrogen energy storage systems. and These are the upper and lower power limits for hydrogen energy storage systems.

[0048] The charge / discharge constraints of the battery energy storage system are:

[0049]

[0050] In the formula, η ch_B and η dis_B These refer to the charging efficiency and discharging efficiency of the battery energy storage system, respectively.

[0051] The charge / discharge constraints of the hydrogen energy storage system are:

[0052] 0≤P ch,Hi (t)≤P HESS,i ·η ch_H

[0053] -P HESS,i ·η dis_H ≤P dis,Hi (t)≤0

[0054] In the formula, η ch_H and η dis_H These represent the charge and discharge efficiencies of the hydrogen energy storage system.

[0055] Optionally, the step of iteratively calculating the Pareto solution set of the multi-objective optimization model according to the political optimization algorithm until the iteration termination condition is met, and obtaining the optimal Pareto solution set, includes:

[0056] S1: Based on the obtained distribution network parameters and the objective function, initialize the algorithm parameters and store them in the storage pool; the algorithm parameters include the members of the population and the fitness function of the population; the members represent the installation nodes, configuration capacity and configuration power of a set of electric-hydrogen energy storage systems;

[0057] S2: Perform campaign activities, inter-party exchanges, elections and parliamentary affairs operations on the population in sequence, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set;

[0058] S3: Compare the Pareto solution set with the Pareto solution set of the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result;

[0059] S4: Iterate through S2-S3 until the number of iterations reaches the preset iteration threshold, and output the optimal Pareto solution set.

[0060] Optionally, the step of calculating the optimal compromise solution of the optimal Pareto solution set using the gray target decision method based on the entropy weight method to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system includes:

[0061] Establish a sample matrix based on the optimal Pareto solution set and the objective function;

[0062] The sample matrix is ​​dimensionless to obtain the operator;

[0063] A decision matrix is ​​constructed based on the operator and the sample matrix, and the bullseye of the decision matrix is ​​determined.

[0064] Calculate the first Euclidean distance between each solution in the decision sample matrix and the target center, and take the solution with the shortest first Euclidean distance as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

[0065] Optionally, constructing the sample matrix based on the optimal Pareto solution set and the objective function includes:

[0066] Obtain the non-dominated solutions of the optimal Pareto solution set, and normalize the objective function corresponding to the non-dominated solutions;

[0067] Calculate the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point;

[0068] The sample matrix is ​​constructed based on the normalized objective function and the second Euclidean distance.

[0069] The present invention also provides a configuration device for an electric-hydrogen hybrid energy storage system, characterized in that the device comprises:

[0070] A construction module is used to construct a multi-objective optimization model for an electric-hydrogen hybrid energy storage system. The multi-objective optimization model takes minimizing the total life cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system as the objective function.

[0071] The first calculation module is used to iteratively calculate the Pareto solution set of the multi-objective optimization model using a political optimization algorithm until the iteration termination condition is met, and output the optimal Pareto solution set.

[0072] The second calculation module is used to calculate the optimal compromise solution of the optimal Pareto solution set using the gray target decision method of entropy weight method, so as to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

[0073] Optionally, the building module includes:

[0074] Acquisition unit, used to acquire power distribution network parameters;

[0075] The construction unit is used to construct an objective function that minimizes the total life cycle loss, power loss, load fluctuation, and voltage fluctuation based on the acquired distribution network parameters.

[0076] The objective function includes:

[0077]

[0078] In the formula, f(x) is the objective function; f1 is the total life cycle loss cost, f2 is the power loss, f3 is the voltage fluctuation, and f4 is the load fluctuation; x is the decision variable; h(x) is the constraint condition, wherein the decision variable includes the installation node, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system; the constraint condition includes node power balance constraint, node voltage constraint, grid connection point power constraint, electric-hydrogen hybrid system capacity and power constraint, battery energy storage system charge and discharge constraint, and hydrogen energy storage system charge and discharge constraint;

[0079] The total lifecycle loss cost includes:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] Among them, Q BESSs Q represents the total lifecycle cost of a battery energy storage system. HESSs The total lifecycle cost of a hydrogen energy storage system; T B For the investment cost of battery energy storage systems, T H The investment cost of a hydrogen energy storage system; W B For the maintenance costs of battery energy storage systems, W H Maintenance costs for hydrogen energy storage systems; Y B For the operating cost of battery energy storage systems, Y H The operating cost of a hydrogen energy storage system; G B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; C B For the disposal and recycling costs of battery energy storage systems, C H The cost of disposal and recycling of hydrogen energy storage systems; μ CRF,B N represents the capital recovery factor; BESS Indicates the number of battery energy storage systems installed in the distribution network; c battery The cost of a single battery; c EPCD,B This represents the engineering, procurement, and construction costs of the battery energy storage system, as well as the developer costs; I sub It is a government subsidy; E BESS,i c is the capacity of the i-th battery energy storage system; FMC,B P represents the annual fixed maintenance cost of a single battery energy storage system. BESS,i The power of the i-th battery energy storage system; T is 24 hours; c pu (t) and c sel (t) represents the purchase and sale price of electricity; P ch,Bi (t) and P dis,Bi (t) represents the charging and discharging power of the i-th battery energy storage system; n B t and α represent the battery's lifespan and replacement cycles, respectively; α is the annual cost depreciation rate of the battery; r represents the discount rate calculated using the weighted average cost of capital; γ B It is the recycling efficiency of battery energy storage systems; c FC and c EThe costs of fuel cells and electrolyzers are respectively; c HT and Q HT,i For the cost and capacity of hydrogen storage tanks; P HESS,i C is the power output of the i-th hydrogen energy storage system; EPCD,H Indicates the EPC cost of a hydrogen energy storage system; c FC and c E The costs of fuel cells and electrolyzers are respectively; c FMC,H This represents the annual maintenance cost of the fuel cell; P HESS,i P represents the power of the i-th hydrogen energy storage system. ch,Hi and P dis,Hi Q represents the charging and discharging power of the i-th hydrogen energy storage system; H,i q represents the total hydrogen production of the hydrogen storage system in one day; H Hydrogen production per kilowatt-hour of electricity; I H2 This represents the profit generated per kilogram of hydrogen; p H The figure represents the power generation per kilogram of hydrogen; μ and v are the ratios of hydrogen delivered to power generated; n H β represents the number of HESSs replacements; β represents the annual cost loss rate of the hydrogen energy storage system; γ represents the number of HESSs replacements. H For the recycling benefits of fuel cells;

[0092] The power loss includes:

[0093]

[0094] L represents the total number of interconnects in the electric-hydrogen hybrid system; R j I represents the resistance on the j-th tie line; t represents time. j Let be the current on the j-th tie line;

[0095] The load fluctuations include:

[0096]

[0097] Among them, P load P pv (t) and P wind These represent the load of the electric-hydrogen hybrid system, and the output of photovoltaic and wind power during time period t.

[0098] The voltage fluctuations include:

[0099]

[0100] In the formula, N nodes V represents the total number of nodes in the system. j Let be the voltage at node j; Let J be the average voltage of node j during time period T;

[0101] The node power balance constraint is:

[0102]

[0103] In the formula, P i (t) represents the active power injected into node i at time t; Q i (t) represents the reactive power injected into node i at time t; θ ij (t) represents the voltage phase angle difference between nodes i and j at time t; V i (t) and V j (t) represent the voltages at node i and node j during time period t, respectively; G ij and B ij These are the line conductance and susceptance between nodes i and j, respectively;

[0104] The node voltage constraint is:

[0105]

[0106] In the formula, and These are the upper and lower voltage limits for node i, respectively;

[0107] The power constraint at the grid connection point is:

[0108]

[0109] In the formula, and These are the lower and upper limits of active and reactive power at the grid connection point, respectively.

[0110] The capacity and power constraints of the aforementioned electric-hydrogen hybrid system are:

[0111]

[0112] In the formula, and These are the upper and lower limits of the capacity of the battery energy storage system. and These are the upper and lower power limits for battery energy storage systems. and These are the upper and lower limits of the capacity of hydrogen energy storage systems. and These are the upper and lower power limits for hydrogen energy storage systems.

[0113] The charge / discharge constraints of the battery energy storage system are:

[0114]

[0115] In the formula, η ch_B and η dis_BThese refer to the charging efficiency and discharging efficiency of the battery energy storage system, respectively.

[0116] The charge / discharge constraints of the hydrogen energy storage system are:

[0117] 0≤P ch,Hi (t)≤P HESS,i ·η ch_H

[0118] -P HESS,i ·η dis_H ≤P dis,Hi (t)≤0

[0119] In the formula, η ch_H and η dis_H These represent the charge and discharge efficiencies of the hydrogen energy storage system.

[0120] Optionally, the first computing module includes:

[0121] An initialization unit is used to initialize algorithm parameters based on the acquired distribution network parameters and the objective function, and store the algorithm parameters in a storage pool; the algorithm parameters include the members of the population and the fitness function of the population; the members represent the optimal installation nodes, configuration capacity and configuration power of a set of electric-hydrogen energy storage systems;

[0122] The update unit is used to sequentially perform campaign activities, inter-party exchanges, elections and parliamentary affairs operations on the population, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set;

[0123] The replacement unit is used to compare the Pareto solution set with the Pareto solution set of the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result;

[0124] The output unit is used to repeatedly trigger the update unit and the replacement in sequence until the termination triggering condition is met, and output the optimal Pareto solution set.

[0125] Optionally, the second computing module includes:

[0126] A sample matrix is ​​established based on the optimal Pareto solution set and the objective function.

[0127] The first computational subunit is used to perform dimensionless transformation on the sample matrix to obtain the operator;

[0128] A determining unit is configured to construct a decision matrix based on the operator and the sample matrix, and determine the bullseye of the decision matrix;

[0129] The second calculation subunit is used to calculate the first Euclidean distance between each solution in the decision sample matrix and the target center, and take the solution with the shortest first Euclidean distance as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

[0130] Optionally, the establishment unit includes:

[0131] The normalization subunit is used to obtain the non-dominated solution of the optimal Pareto solution set and normalize the objective function corresponding to the non-dominated solution.

[0132] The third calculation subunit is used to calculate the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point;

[0133] Sub-units are established to construct sample matrices based on the normalized objective function and the second Euclidean distance.

[0134] As can be seen from the above technical solutions, the present invention has the following advantages:

[0135] This invention provides a method for an electric-hydrogen hybrid energy storage system, comprising constructing a multi-objective optimization model for the electric-hydrogen hybrid energy storage system. The multi-objective optimization model takes minimizing the total life-cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system as its objective function. A political optimization algorithm with strong global search capability and fast convergence speed is used to iteratively calculate the Pareto solution set of the multi-objective optimization model until the iteration termination condition is met, outputting the optimal Pareto solution set. This method can more quickly search for a uniformly distributed Pareto solution set with good convergence performance, achieving good optimization results. The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method with entropy weight, which can fairly balance the four optimization objectives of total life-cycle loss cost, power loss, voltage fluctuation, and load fluctuation, obtaining the optimal installation node, configuration capacity, and configuration power scheme for the electric-hydrogen hybrid energy storage system. This reduces the cost of configuring the electric-hydrogen hybrid energy storage system in the distribution network and improves the problems of power loss, load fluctuation, and voltage fluctuation in the system. Attached Figure Description

[0136] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0137] Figure 1 This is a schematic diagram of a configuration method for an electric-hydrogen hybrid energy storage system provided in Embodiment 1 of the present invention;

[0138] Figure 2 This is a schematic diagram of a configuration method for an electric-hydrogen hybrid energy storage system provided in Embodiment 2 of the present invention;

[0139] Figure 3 This is a structural diagram of an electric-hydrogen hybrid energy storage system configuration device provided in Embodiment 3 of the present invention. Detailed Implementation

[0140] This invention provides a method and apparatus for configuring an electric-hydrogen hybrid energy storage system, which reduces the cost of configuring an electric-hydrogen hybrid energy storage system in a power distribution network and improves the problems of power loss, load fluctuation and voltage fluctuation in the system.

[0141] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0142] Please see Figure 1 , Figure 1 This is a flowchart illustrating a configuration method for an electric-hydrogen hybrid energy storage system provided in Embodiment 1 of the present invention.

[0143] This embodiment provides a method for configuring an electric-hydrogen hybrid energy storage system, including:

[0144] 101. Construct a multi-objective optimization model for an electric-hydrogen hybrid energy storage system. The objective function of the multi-objective optimization model is to minimize the total life cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system.

[0145] In this embodiment, the multi-objective optimization model of the electric-hydrogen hybrid energy storage system is established based on the acquired distribution network data. This distribution network data includes load data, generator data, branch data, and wind and solar power data for both battery and hydrogen energy storage systems within the entire distribution network. The multi-objective optimization model uses the minimization of total lifecycle losses, power losses, load fluctuations, and voltage fluctuations as its objective function.

[0146] The factors influencing the total lifecycle loss include the total lifecycle loss of battery energy storage systems and hydrogen energy storage systems. The total lifecycle loss of battery energy storage systems includes the total investment cost, operating cost, replacement cost, and disposal and recycling cost. The total lifecycle loss of hydrogen energy storage systems includes the total investment cost, operating cost, replacement cost, and disposal and recycling cost.

[0147] 102. Use the political optimization algorithm to iteratively calculate the Pareto solution set of the multi-objective optimization model until the iteration termination condition is met, and output the optimal Pareto solution set.

[0148] It should be noted that political optimization algorithms seek individual optimization by simulating a multi-stage election process, including the election of party leaders. The algorithm divides the population into political parties and constituencies, and updates party leaders and constituency winners through elections by members from these parties and constituencies, aiming to find the optimal solution. The roles of party leaders and constituency winners can be interchanged. The election can be viewed as an evaluation objective function (i.e., a fitness function), and the number of votes a member receives in the election can be understood as the fitness of that member within the fitness function. The entire algorithm includes processes such as party and parliamentary formation, campaign activities, inter-party exchanges, elections, and parliamentary affairs.

[0149] In this embodiment, members represent the installation nodes, configured capacity, and configured power of a group of electric-hydrogen hybrid energy storage systems. A political optimization algorithm is used to update the installation nodes, configured capacity, and configured power of each hybrid energy storage system in the population in each iteration. The overall performance of the electric-hydrogen hybrid energy storage system is evaluated using four fitness functions (lifetime cost loss, power loss, voltage fluctuation, and load fluctuation) until the iteration terminates. The best member with the optimal fitness function is selected, and the optimal planning scheme is output. The best member represents the optimal feasible solution with the installation nodes, configured capacity, and configured power that satisfy the fitness function, and the optimal planning scheme is the set of optimal feasible solutions, i.e., the optimal Pareto solution set.

[0150] In this embodiment, by employing a political optimization algorithm to iteratively compute the solution set of the multi-objective optimization model, the Pareto front can be robustly searched, thereby finding a uniformly distributed Pareto solution set with good convergence performance, and obtaining the optimal Pareto solution set more accurately and comprehensively. Furthermore, by employing a political optimization algorithm and dividing the candidate solutions into two parts, a mechanism for interaction between candidate solutions is added, enabling the algorithm to quickly explore new candidate solutions.

[0151] 103. The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method with entropy weight, and the optimal configuration scheme of the electric-hydrogen hybrid energy storage system is obtained. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

[0152] It should be noted that by employing the gray target decision-making method based on entropy weight, the optimal compromise solution is obtained from the optimal Pareto solution set. This method can fairly balance the four optimization objectives of life-cycle loss cost, power loss, voltage fluctuation, and load fluctuation, thereby obtaining the optimal configuration scheme of the electric-hydrogen hybrid energy storage system that minimizes life-cycle loss cost, power loss, voltage fluctuation, and load fluctuation. That is, the optimal installation node, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system. Configuring the electric-hydrogen hybrid energy storage system according to the optimal configuration scheme reduces configuration costs and improves the existing problems of power loss, voltage fluctuation, and load fluctuation in the system.

[0153] This embodiment provides a configuration method for an electric-hydrogen hybrid energy storage system. The method includes constructing a multi-objective optimization model for the electric-hydrogen hybrid energy storage system. The objective function of this model is to minimize the system's lifecycle losses, power losses, load fluctuations, and voltage fluctuations. A political optimization algorithm with strong global search capabilities and fast convergence speed is used to iteratively calculate the Pareto solution set of the multi-objective optimization model until the iteration termination condition is met, outputting the optimal Pareto solution set. This method can more quickly find a uniformly distributed Pareto solution set with good convergence performance, achieving good optimization results. The optimal compromise solution of the optimal Pareto solution set is calculated using the entropy weight method's gray target decision method. This method fairly considers the four optimization objectives of lifecycle losses, power losses, voltage fluctuations, and load fluctuations, obtaining the optimal installation nodes, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system. This reduces the cost of configuring electric-hydrogen hybrid energy storage systems in distribution networks and improves the problems of power losses, load fluctuations, and voltage fluctuations in the system, providing strong technical support for the stable operation of distribution networks.

[0154] Please see Figure 2 , Figure 2 This is a schematic flowchart of a configuration method for an electric-hydrogen hybrid energy storage system provided in Embodiment 2 of the present invention. The configuration method specifically includes:

[0155] 201. Obtain the distribution network parameters and construct an objective function based on the obtained distribution network parameters, which minimizes the total life cycle loss, power loss, load fluctuation and voltage fluctuation.

[0156] It should be noted that the objective function is as follows:

[0157]

[0158] In the formula, f(x) is the objective function; f1 is the total life cycle loss cost, f2 is the power loss, f3 is the load fluctuation, and f4 is the voltage fluctuation; x is the decision variable; h(x) is the constraint condition, where the decision variable includes the installation node, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system; the constraint condition includes node power balance constraint, node voltage constraint, grid connection point power constraint, electric-hydrogen hybrid system capacity and power constraint, battery energy storage system charge and discharge constraint, and hydrogen energy storage system charge and discharge constraint.

[0159] The formulas for calculating the total life-cycle loss cost are shown in (2)-(12):

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171] Among them, Q BESSs Q represents the total lifecycle cost of a battery energy storage system. HESSs The total lifecycle cost of a hydrogen energy storage system; T B For the investment cost of battery energy storage systems, T H The investment cost of a hydrogen energy storage system; W B For the maintenance costs of battery energy storage systems, W H Maintenance costs for hydrogen energy storage systems; Y B For the operating cost of battery energy storage systems, Y H The operating cost of a hydrogen energy storage system; G B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; C B For the disposal and recycling costs of battery energy storage systems, C HThe cost of disposal and recycling of hydrogen energy storage systems; μ CRF,B N represents the capital recovery factor; BESS Indicates the number of battery energy storage systems installed in the distribution network; c battery The cost of a single battery; c EPCD,B This represents the engineering, procurement, and construction costs of the battery energy storage system, as well as the developer costs; I sub It is a government subsidy; E BESS,i c is the capacity of the i-th battery energy storage system; FMC,B P represents the annual fixed maintenance cost of a single battery energy storage system. BESS,i The power of the i-th battery energy storage system; T is 24 hours; c pu (t) and c sel (t) represents the purchase and sale price of electricity; P ch,Bi (t) and P dis,Bi (t) represents the charging and discharging power of the i-th battery energy storage system; n B t and α represent the battery's lifespan and replacement cycles, respectively; α is the annual cost depreciation rate of the battery; r represents the discount rate calculated using the weighted average cost of capital; γ B It is the recycling efficiency of battery energy storage systems; c FC and c E The costs of fuel cells and electrolyzers are respectively; c HT and Q HT,i For the cost and capacity of hydrogen storage tanks; P HESS,i C is the power output of the i-th hydrogen energy storage system; EPCD,H Indicates the EPC cost of a hydrogen energy storage system; c FC and c E The costs of fuel cells and electrolyzers are respectively; c FMC,H This represents the annual maintenance cost of the fuel cell; P HESS,i P represents the power of the i-th hydrogen energy storage system. ch,Hi and P dis,Hi Q represents the charging and discharging power of the i-th hydrogen energy storage system; H,i q represents the total hydrogen production of the hydrogen storage system in one day; H Hydrogen production per kilowatt-hour of electricity; This represents the profit generated per kilogram of hydrogen; p H The figure represents the power generation per kilogram of hydrogen; μ and v are the ratios of hydrogen delivered to power generated; n H β represents the number of HESSs replacements; β represents the annual cost loss rate of the hydrogen energy storage system; γ represents the number of HESSs replacements. H For the recycling benefits of fuel cells;

[0172] It should be noted that the power loss of the electric-hydrogen hybrid energy storage system is calculated using the following formula:

[0173]

[0174] L represents the total number of interconnects in the electric-hydrogen hybrid system; R j I represents the resistance on the j-th tie line; t represents time. j Let be the current on the j-th tie line;

[0175] It should be noted that the electric-hydrogen hybrid energy storage system can smooth out load fluctuations. Load fluctuations can be represented by the daily power fluctuation at the grid connection point, which can be specifically calculated using formula (14):

[0176]

[0177] Among them, P load P pv (t) and P wind These represent the load of the electric-hydrogen hybrid system, and the output of photovoltaic and wind power during time period t.

[0178] Voltage fluctuations can be calculated using formula (15):

[0179]

[0180] In the formula, N nodes V represents the total number of nodes in the system. j Let be the voltage at node j; Let J be the average voltage of node j during time period T;

[0181] It should be noted that the constraints include point power balance constraints, node voltage constraints, grid connection point power constraints, capacity and power constraints of the electric-hydrogen hybrid system, charge and discharge constraints of the battery energy storage system, and charge and discharge constraints of the hydrogen energy storage system.

[0182] 1) The node power balance constraint is:

[0183]

[0184] In the formula, P i (t) represents the active power injected into node i at time t; Q i (t) represents the reactive power injected into node i at time t; θ ij (t) represents the voltage phase angle difference between nodes i and j at time t; V i (t) and V j (t) represent the voltages at node i and node j during time period t, respectively; G ij and B ij These are the line conductance and susceptance between nodes i and j, respectively;

[0185] 2) Node voltage constraints are:

[0186]

[0187] In the formula, and These are the upper and lower voltage limits for node i, respectively;

[0188] 3) The power constraint at the grid connection point is:

[0189]

[0190] In the formula, and These are the lower and upper limits of active and reactive power at the grid connection point, respectively.

[0191] 4) The capacity and power constraints of the electric-hydrogen hybrid system are:

[0192]

[0193] In the formula, and These are the upper and lower limits of the capacity of the battery energy storage system. and These are the upper and lower power limits for battery energy storage systems. and These are the upper and lower limits of the capacity of hydrogen energy storage systems. and These are the upper and lower power limits for hydrogen energy storage systems.

[0194] 5) The charge and discharge constraints of the battery energy storage system are:

[0195]

[0196] In the formula, η ch_B and η dis_B These refer to the charging efficiency and discharging efficiency of the battery energy storage system, respectively.

[0197] 6) The charge and discharge constraints of the hydrogen energy storage system are:

[0198]

[0199] In the formula, η ch_H and η dis_H These represent the charging and discharging efficiencies of the hydrogen energy storage system, respectively.

[0200] 202. Use the political optimization algorithm to iteratively calculate the Pareto solution set of the multi-objective optimization model until the iteration termination condition is met, and output the optimal Pareto solution set.

[0201] It should be noted that step 202 includes the following sub-steps:

[0202] S1: Based on the obtained distribution network parameters and objective function, initialize the algorithm parameters and store them in the storage pool; the algorithm parameters include the members of the population and the fitness function of the population; the members represent the installation nodes, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems.

[0203] It should be noted that the algorithm population is stored in a storage pool. The purpose of step S1 is to initialize the algorithm population, i.e., to form political parties and parliaments. In the formation of political parties and parliaments, the population is divided into n political parties and n constituencies. The members of the population are assigned to the n political parties, and each political party Zi consists of n members. The j-th member of each political party... As a candidate solution, each candidate solution This is a d-dimensional vector representing the installation nodes, configured capacity, and configured power of a set of electric-hydrogen hybrid energy storage systems. In this embodiment, d = 3. See below:

[0204] Z = {Z1, Z2, Z3, ..., Z} n} (twenty two)

[0205]

[0206]

[0207] In the formula, Z represents the set of political parties. i Let i be the i-th political party. When setting the population parameters, the number of political parties, the number of constituencies, and the number of members in each political party are all set to the same value. The j-th member in each political party... Participating in the j-th constituency X j For the election, please refer to the following formulas (25) and (26):

[0208] X = {x1, x2, x3, ..., x} n} (25)

[0209]

[0210] X represents a constituency, where there are n constituencies. Each constituency has n members participating in the election, and the member with the most votes is the winner of the constituency.

[0211] In a political party, the member with the highest fitness rate is elected as the party leader, as shown below:

[0212]

[0213] In the formula, f() is the objective function that includes total lifecycle loss cost, power loss, voltage fluctuation, and load fluctuation. This represents the leader of the i-th political party.

[0214] After the election, all party leaders Form a set as shown in the following formula:

[0215]

[0216] All constituency winners The members of parliament are composed as shown in the following formula:

[0217]

[0218] In this embodiment, the formation of political parties and parliaments in the population is completed according to formulas (22)-(29). In this embodiment, the algorithm parameters of the political optimization algorithm are initialized by the distribution network parameters and the objective function. The objective function of the multi-objective optimization model is used as the fitness function of the population. The members are regarded as the installation nodes, configuration capacity and configuration power in a set of electric-hydrogen hybrid energy storage systems. The fitness of the members is the objective function value corresponding to the installation nodes, configuration capacity and configuration power in a set of electric-hydrogen hybrid energy storage systems. The party leaders and constituency winners are the best members in the population with the best fitness. The initialized population will be stored in the storage pool.

[0219] S2: Perform campaign activities, inter-party exchanges, elections and parliamentary affairs operations on the population in sequence, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set.

[0220] In this embodiment, the election activity updates the member's position using formulas (30) and (31). This is achieved by comparing the member's current fitness with its past fitness, and then selecting the appropriate formula based on the comparison result. If the member's current fitness... Compared to previous fitness If the position is high, use equation (30) to update the position; otherwise, use equation (31) to update the position.

[0221] It should be noted that both formulas (30) and (31) are applicable to updating the positions of party leaders and constituency winners. When updating the position of party leaders, m * For political party leadership At position k in the k-th dimension, when updating the position of the winner of the selection, m * For constituency winner Position in the k-th dimension:

[0222]

[0223]

[0224] In the formula, t represents the number of iterations, and r is a random number in the interval [0,1].

[0225] In this embodiment, inter-party exchange refers to each member A political party Z is randomly selected with probability λ. i and the least adapted individuals within that political party Perform position swapping. Specifically, the individual with the worst fitness can be determined according to formula (32).

[0226]

[0227] Where λ is an adaptive parameter, from λ max Initially, it decreases linearly to 0 during the iteration process.

[0228] In this embodiment, the election activity is carried out according to formula (33).

[0229]

[0230] In this embodiment, parliamentary affairs refer to the winners of each constituency. Another winner from a randomly selected constituency The winner is then updated using formula (34). Adaptability, after update The position is Formula (34) is as follows

[0231]

[0232] In this embodiment, the positions of members are updated by sequentially performing campaign activities, inter-party exchanges, elections, and parliamentary affairs on the above population. Based on the updated positions, the member with the highest fitness is selected as the Pareto solution set of the multi-objective optimization model.

[0233] S3: Compare the Pareto solution set with the Pareto solution set in the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result;

[0234] It should be noted that during the iteration process, the Pareto solution sets obtained in step S2 are all stored in the storage pool. In order to obtain the optimal Pareto solution set, the solution sets stored in the storage pool for each Pareto solution set obtained in S2 are compared. If there is a Pareto solution in the storage pool that is better than the solution in the Pareto solution set obtained in S2, the solution in the storage pool will replace the solution in the Pareto solution set obtained in S2. Specifically, the replacement can be performed according to formula (35).

[0235]

[0236] In the formula, f k() represents the k-th objective function, which is one of the following: total lifecycle loss cost, power loss, voltage fluctuation, and load fluctuation; x i For the i-th electric-hydrogen hybrid energy storage system in the population; PT k The Pareto front distance limit for the k-th objective function value; and Let N be the maximum and minimum values ​​of the k-th objective function, respectively; r This represents the upper limit of the number of Pareto optimal solutions in the storage pool.

[0237] It is understandable that when performing the first comparison and replacement, the solution in the storage pool is calculated by the algorithm initialization parameters. At this time, it is not necessary to replace the Pareto solution set obtained in S2 for the first time.

[0238] S4: Iterate through S2-S3 until the number of iterations reaches the preset iteration threshold, and output the optimal Pareto solution set.

[0239] It should be noted that the threshold for the number of iterations can be determined based on the actual situation.

[0240] 203. The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method of entropy weight method, and the optimal configuration scheme of the electric-hydrogen hybrid energy storage system is obtained. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

[0241] It should be noted that step 203 also includes the following sub-steps:

[0242] C1. Establish the sample matrix based on the optimal Pareto solution set and the objective function;

[0243] It should be noted that in step C1, the non-dominated solutions of the optimal Pareto solution set are first obtained, the objective function corresponding to the non-dominated solutions is normalized, and then the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point is calculated. After that, a sample matrix is ​​established based on the normalized objective function and the second Euclidean distance. For details, please refer to formulas (36)-(38).

[0244]

[0245]

[0246]

[0247] In the formula, δ is the sample matrix; Let be the element in the i-th row and j-th column of the sample matrix; n is the number of objective functions; m is the dimension of the objective function. Let ED be the i-th dimension of the j-th objective function; iO is the Euclidean distance of the objective function in the i-th dimension; j Let be the objective point after normalization of the j-th objective function.

[0248] In this embodiment, by establishing a sample matrix, the objective functions f1, f2, f3, and f4 are normalized, and the Euclidean distance between each solution in the optimal Pareto solution set and the ideal point is taken into account in order to determine the optimal compromise solution.

[0249] C2. Dimensionless transformation of the sample matrix yields the operator.

[0250] It should be noted that the operator q can be calculated using formula (39). j

[0251]

[0252] C3. Construct a decision matrix based on the operators and the sample matrix, and determine the bullseye of the decision matrix;

[0253] It should be noted that the decision matrix V can be constructed according to formula (40).

[0254]

[0255] The bullseye of the decision matrix is:

[0256]

[0257] C4. Calculate the first Euclidean distance between each solution in the decision sample matrix and the target center. Take the solution with the shortest first Euclidean distance as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

[0258] It should be noted that, in this embodiment, based on the first Euclidean distance between each non-dominated solution and the target center in the decision sample matrix, each non-dominated solution is sorted, and the solution closest to the target center is selected as the optimal compromise solution. The optimal compromise solution is the optimal installation node, configuration capacity, and configuration power of the electric-hydrogen hybrid energy storage system. The configuration scheme can be obtained by configuring the installation node, capacity, and power of the electric-hydrogen hybrid energy storage system according to the optimal compromise solution.

[0259] This embodiment provides a method for configuring an electric-hydrogen hybrid energy storage system. The method includes acquiring distribution network parameters and constructing an objective function based on these parameters, minimizing lifecycle losses, power losses, load fluctuations, and voltage fluctuations. A political optimization algorithm with strong global search capabilities and fast convergence speed is used to iteratively calculate the Pareto solution set of the objective function until the iteration termination condition is met, outputting the optimal Pareto solution set. This method can more quickly find a uniformly distributed Pareto solution set with good convergence performance, achieving good optimization results. The optimal compromise solution of the optimal Pareto solution set is calculated using the entropy weight method's gray target decision method, which fairly considers the four optimization objectives of lifecycle losses, power losses, voltage fluctuations, and load fluctuations. This yields the optimal installation node, configuration capacity, and configuration power scheme for the electric-hydrogen hybrid energy storage system that satisfies the objective function, reducing the cost of configuring the electric-hydrogen hybrid energy storage system in the distribution network and improving the problems of power losses, load fluctuations, and voltage fluctuations in the system.

[0260] Please see Figure 3 , Figure 3 This is a structural diagram of an electric-hydrogen hybrid energy storage system configuration device provided in Embodiment 3 of the present invention. The device includes:

[0261] Module 301 is used to construct a multi-objective optimization model for the electric-hydrogen hybrid energy storage system. The multi-objective optimization model takes minimizing the total life cycle loss, power loss, load fluctuation and voltage fluctuation of the electric-hydrogen hybrid energy storage system as the objective function.

[0262] The first calculation module 302 is used to iteratively calculate the Pareto solution set of the multi-objective optimization model using a political optimization algorithm until the iteration termination condition is met, and output the optimal Pareto solution set.

[0263] The second calculation module 303 is used to calculate the optimal compromise solution of the optimal Pareto solution set using the gray target decision method of entropy weight method, so as to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

[0264] Furthermore, the building module 301 includes:

[0265] Acquisition unit, used to acquire power distribution network parameters;

[0266] The construction unit is used to construct an objective function that minimizes the total life cycle loss, power loss, load fluctuation, and voltage fluctuation based on the acquired distribution network parameters.

[0267] It should be noted that the calculation formulas for the objective function, total life cycle loss, power loss, load fluctuation and voltage fluctuation can be found in Embodiment 2 of this invention, and will not be repeated here.

[0268] Furthermore, the first computing module 302 includes:

[0269] The initialization unit is used to initialize the algorithm parameters based on the obtained distribution network parameters and objective function, and store the algorithm parameters in the storage pool. The algorithm parameters include the members of the population and the fitness function of the population. The members represent the optimal installation nodes, configuration capacity and configuration power of a set of electric-hydrogen energy storage systems.

[0270] The update unit is used to sequentially perform campaign activities, inter-party exchanges, elections, and parliamentary affairs operations on the population, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set;

[0271] The replacement unit is used to compare the Pareto solution set with the Pareto solution set in the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result.

[0272] The output unit is used to repeatedly trigger the update unit and replacement until the termination triggering condition is met, and output the optimal Pareto solution set.

[0273] Furthermore, the second computing module 303 includes:

[0274] Establishment unit, used to build sample matrix based on optimal Pareto solution set and objective function;

[0275] The first computational subunit is used to dimensionlessly transform the sample matrix to obtain the operator;

[0276] The determination unit is used to construct the decision matrix based on the operator and the sample matrix, and to determine the bullseye of the decision matrix;

[0277] The second calculation subunit is used to calculate the first Euclidean distance between each solution in the decision sample matrix and the target center. The solution with the shortest first Euclidean distance is taken as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

[0278] Furthermore, the establishment of units includes:

[0279] The normalized sub-unit is used to obtain the non-dominated solution of the optimal Pareto solution set, and the objective function corresponding to the normalized non-dominated solution is used.

[0280] The third computational subunit is used to calculate the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point;

[0281] Sub-units are established to construct the sample matrix based on the normalized objective function and the second Euclidean distance.

[0282] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0283] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0284] The units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0285] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0286] If the integrated unit is implemented as 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0287] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring an electric-hydrogen hybrid energy storage system, characterized in that, include: A multi-objective optimization model for an electric-hydrogen hybrid energy storage system is constructed, with the objective function being to minimize the total life-cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system. The Pareto solution set of the multi-objective optimization model is calculated iteratively using a political optimization algorithm until the iteration termination condition is met, and the optimal Pareto solution set is output. The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method with entropy weight, and the optimal configuration scheme of the electric-hydrogen hybrid energy storage system is obtained. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

2. The method according to claim 1, characterized in that, The multi-objective optimization model for constructing the electric-hydrogen hybrid energy storage system includes: Obtain the distribution network parameters and construct an objective function based on the obtained distribution network parameters to minimize the total life cycle loss, power loss, load fluctuation and voltage fluctuation. The objective function includes: In the formula, The objective function is... For the total life cycle loss cost, For power loss, For voltage fluctuations, For load fluctuations; h(x) represents the decision variables; h(x) represents the constraints, where the decision variables include the installation nodes, configured capacity, and configured power of the electric-hydrogen hybrid energy storage system; the constraints include node power balance constraints, node voltage constraints, grid connection point power constraints, electric-hydrogen hybrid system capacity and power constraints, battery energy storage system charge and discharge constraints, and hydrogen energy storage system charge and discharge constraints. The total lifecycle loss cost includes: in, The total lifecycle cost of a battery energy storage system. The total lifecycle cost of a hydrogen energy storage system; The investment cost of battery energy storage systems, The investment cost of a hydrogen energy storage system; For the maintenance costs of battery energy storage systems, Maintenance costs for hydrogen energy storage systems; For the operating cost of battery energy storage systems, The operating cost of a hydrogen energy storage system; The replacement cost of battery energy storage systems, The replacement cost of hydrogen energy storage systems; The disposal and recycling costs of battery energy storage systems, The cost of disposal and recycling of hydrogen energy storage systems; Indicates the capital recovery factor; This indicates the number of battery energy storage systems installed in the power distribution network. Cost per battery; This represents the engineering, procurement, construction, and developer costs of a battery energy storage system. It is a government subsidy; It is the capacity of the i-th battery energy storage system; This represents the annual fixed maintenance cost of a single battery energy storage system; is the power of the i-th battery energy storage system; T is 24 hours; and These are the purchase and sale prices of electricity; and These are the charging and discharging power of the i-th battery energy storage system, respectively. t and α represent the battery life and replacement cycles, respectively; α is the annual cost depreciation rate of the battery; r represents the discount rate calculated using the weighted average cost of capital. It is the recycling benefit of battery energy storage systems; and The costs of fuel cells and electrolyzers are respectively; and The cost and capacity of hydrogen storage tanks; It is the power output of the i-th hydrogen energy storage system; This indicates the EPC cost of a hydrogen energy storage system; and The costs of fuel cells and electrolyzers are respectively; This indicates the annual maintenance cost of the fuel cell; Let be the power of the i-th hydrogen energy storage system; and This represents the charging and discharging power of the i-th hydrogen energy storage system; This indicates the total hydrogen production of the hydrogen energy storage system in one day. Hydrogen production per kilowatt-hour of electricity; This represents the profit generated per kilogram of hydrogen; μ is the ratio of hydrogen delivered to electricity generated. The number of times HESSs are replaced; The annual cost loss rate of the hydrogen energy storage system; For the recycling benefits of fuel cells; The power loss includes: L represents the total number of interconnects in the electric-hydrogen hybrid system; Represents the resistance of the j-th tie line; t represents time. Let be the current on the j-th tie line; The load fluctuations include: in, , and These represent the load of the electric-hydrogen hybrid system, and the output of photovoltaic and wind power during time period t. The voltage fluctuations include: In the formula, This represents the total number of nodes in the system. Let be the voltage at node j; Let J be the average voltage of node j during time period T; The node power balance constraint is: In the formula, The active power injected into node i at time t; The reactive power injected into node i at time t; Let be the voltage phase angle difference between nodes i and j at time t; and Let represent the voltages at node i and node j during time period t, respectively; and These are the line conductance and susceptance between nodes i and j, respectively; The node voltage constraint is: In the formula, and They are nodes The upper and lower limits of voltage; The power constraint at the grid connection point is: In the formula, , , and These are the lower and upper limits of active and reactive power at the grid connection point, respectively. The capacity and power constraints of the aforementioned electric-hydrogen hybrid system are: In the formula, and These are the upper and lower limits of the capacity of the battery energy storage system. and These are the upper and lower power limits for battery energy storage systems. and These are the upper and lower limits of the capacity of hydrogen energy storage systems. and These are the upper and lower power limits for hydrogen energy storage systems. The charge and discharge constraints of the battery energy storage system are: In the formula, and These refer to the charging efficiency and discharging efficiency of the battery energy storage system, respectively. The charge / discharge constraints of the hydrogen energy storage system are: In the formula, and These represent the charge and discharge efficiencies of the hydrogen energy storage system.

3. The method according to claim 2, characterized in that, The step of iteratively calculating the Pareto solution set of the multi-objective optimization model using the political optimization algorithm until the iteration termination condition is met, and obtaining the optimal Pareto solution set, includes: S1: Based on the obtained distribution network parameters and the objective function, initialize the algorithm parameters and store them in the storage pool; the algorithm parameters include the members of the population and the fitness function of the population; the members represent the installation nodes, configuration capacity and configuration power of a set of electric-hydrogen energy storage systems; S2: Perform campaign activities, inter-party exchanges, elections and parliamentary affairs operations on the population in sequence, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set; S3: Compare the Pareto solution set with the Pareto solution set of the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result; S4: Iterate through S2-S3 until the number of iterations reaches the preset iteration threshold, and output the optimal Pareto solution set.

4. The method according to claim 3, characterized in that, The optimal compromise solution of the optimal Pareto solution set is calculated using the gray target decision method based on the entropy weight method, resulting in the optimal configuration scheme of the electric-hydrogen hybrid energy storage system, including: Establish a sample matrix based on the optimal Pareto solution set and the objective function; The sample matrix is ​​dimensionless to obtain the operator; A decision matrix is ​​constructed based on the operator and the sample matrix, and the bullseye of the decision matrix is ​​determined. Calculate the first Euclidean distance between each solution in the decision sample matrix and the target center, and take the solution with the shortest first Euclidean distance as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

5. The method according to claim 4, characterized in that, The step of constructing the sample matrix based on the optimal Pareto solution set and the objective function includes: Obtain the non-dominated solutions of the optimal Pareto solution set, and normalize the objective function corresponding to the non-dominated solutions; Calculate the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point; The sample matrix is ​​constructed based on the normalized objective function and the second Euclidean distance.

6. A configuration device for an electric-hydrogen hybrid energy storage system, characterized in that, The device includes: A construction module is used to construct a multi-objective optimization model for an electric-hydrogen hybrid energy storage system. The multi-objective optimization model takes minimizing the total life cycle loss, power loss, load fluctuation, and voltage fluctuation of the electric-hydrogen hybrid energy storage system as the objective function. The first calculation module is used to iteratively calculate the Pareto solution set of the multi-objective optimization model using a political optimization algorithm until the iteration termination condition is met, and output the optimal Pareto solution set. The second calculation module is used to calculate the optimal compromise solution of the optimal Pareto solution set using the gray target decision method of entropy weight method, so as to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system. The optimal configuration scheme includes the optimal installation node, configuration capacity and configuration power.

7. The apparatus according to claim 6, characterized in that, The building module includes: Acquisition unit, used to acquire power distribution network parameters; The construction unit is used to construct an objective function that minimizes the total life cycle loss, power loss, load fluctuation, and voltage fluctuation based on the acquired distribution network parameters. The objective function includes: In the formula, The objective function is... For the total life cycle loss cost, For power loss, For voltage fluctuations, For load fluctuations; h(x) represents the decision variables; h(x) represents the constraints, where the decision variables include the installation nodes, configured capacity, and configured power of the electric-hydrogen hybrid energy storage system; the constraints include node power balance constraints, node voltage constraints, grid connection point power constraints, electric-hydrogen hybrid system capacity and power constraints, battery energy storage system charge and discharge constraints, and hydrogen energy storage system charge and discharge constraints. The total lifecycle loss cost includes: in, The total lifecycle cost of a battery energy storage system. The total lifecycle cost of a hydrogen energy storage system; The investment cost of battery energy storage systems, The investment cost of a hydrogen energy storage system; For the maintenance costs of battery energy storage systems, Maintenance costs for hydrogen energy storage systems; For the operating cost of battery energy storage systems, The operating cost of a hydrogen energy storage system; The replacement cost of battery energy storage systems, The replacement cost of hydrogen energy storage systems; The disposal and recycling costs of battery energy storage systems, The cost of disposal and recycling of hydrogen energy storage systems; Indicates the capital recovery factor; This indicates the number of battery energy storage systems installed in the power distribution network. Cost per battery; This represents the engineering, procurement, construction, and developer costs of a battery energy storage system. It is a government subsidy; It is the capacity of the i-th battery energy storage system; This represents the annual fixed maintenance cost of a single battery energy storage system; is the power of the i-th battery energy storage system; T is 24 hours; and These are the purchase and sale prices of electricity; and These are the charging and discharging power of the i-th battery energy storage system, respectively. t and α represent the battery life and replacement cycles, respectively; α is the annual cost depreciation rate of the battery; r represents the discount rate calculated using the weighted average cost of capital. It is the recycling benefit of battery energy storage systems; and The costs of fuel cells and electrolyzers are respectively; and The cost and capacity of hydrogen storage tanks; It is the power output of the i-th hydrogen energy storage system; This indicates the EPC cost of a hydrogen energy storage system; and The costs of fuel cells and electrolyzers are respectively; This indicates the annual maintenance cost of the fuel cell; Let be the power of the i-th hydrogen energy storage system; and This represents the charging and discharging power of the i-th hydrogen energy storage system; This indicates the total hydrogen production of the hydrogen energy storage system in one day. Hydrogen production per kilowatt-hour of electricity; This represents the profit generated per kilogram of hydrogen; μ is the ratio of hydrogen delivered to electricity generated. The number of times HESSs are replaced; The annual cost loss rate of the hydrogen energy storage system; For the recycling benefits of fuel cells; The power loss includes: L represents the total number of interconnects in the electric-hydrogen hybrid system; Represents the resistance of the j-th tie line; t represents time. Let be the current on the j-th tie line; The load fluctuations include: in, , and These represent the load of the electric-hydrogen hybrid system, and the output of photovoltaic and wind power during time period t. The voltage fluctuations include: In the formula, This represents the total number of nodes in the system. Let be the voltage at node j; Let J be the average voltage of node j during time period T; The node power balance constraint is: In the formula, The active power injected into node i at time t; The reactive power injected into node i at time t; Let be the voltage phase angle difference between nodes i and j at time t; and Let represent the voltages at node i and node j during time period t, respectively; and These are the line conductance and susceptance between nodes i and j, respectively; The node voltage constraint is: In the formula, and They are nodes The upper and lower limits of voltage; The power constraint at the grid connection point is: In the formula, , , and These are the lower and upper limits of active and reactive power at the grid connection point, respectively. The capacity and power constraints of the aforementioned electric-hydrogen hybrid system are: In the formula, and These are the upper and lower limits of the capacity of the battery energy storage system. and These are the upper and lower power limits for battery energy storage systems. and These are the upper and lower limits of the capacity of hydrogen energy storage systems. and These are the upper and lower power limits for hydrogen energy storage systems. The charge and discharge constraints of the battery energy storage system are: In the formula, and These refer to the charging efficiency and discharging efficiency of the battery energy storage system, respectively. The charge / discharge constraints of the hydrogen energy storage system are: In the formula, and These represent the charge and discharge efficiencies of the hydrogen energy storage system.

8. The apparatus according to claim 6, characterized in that, The first computing module includes: An initialization unit is used to initialize algorithm parameters based on the acquired distribution network parameters and the objective function, and store the algorithm parameters in a storage pool; the algorithm parameters include the members of the population and the fitness function of the population; the members represent the optimal installation nodes, configuration capacity and configuration power of a set of electric-hydrogen energy storage systems; The update unit is used to sequentially perform campaign activities, inter-party exchanges, elections and parliamentary affairs operations on the population, update the members in the storage pool and their fitness, and select the member with the highest fitness as the Pareto solution set; The replacement unit is used to compare the Pareto solution set with the Pareto solution set of the storage pool, and replace the dominant solution in the Pareto solution set according to the comparison result; The output unit is used to repeatedly trigger the update unit and the replacement in sequence until the termination triggering condition is met, and output the optimal Pareto solution set.

9. The apparatus according to claim 6, characterized in that, The second calculation module includes: A sample matrix is ​​established based on the optimal Pareto solution set and the objective function. The first computational subunit is used to perform dimensionless transformation on the sample matrix to obtain the operator; A determining unit is configured to construct a decision matrix based on the operator and the sample matrix, and determine the bullseye of the decision matrix; The second calculation subunit is used to calculate the first Euclidean distance between each solution in the decision sample matrix and the target center, and take the solution with the shortest first Euclidean distance as the optimal compromise solution to obtain the optimal configuration scheme of the electric-hydrogen hybrid energy storage system.

10. The apparatus according to claim 9, characterized in that, The establishment unit includes: The normalization subunit is used to obtain the non-dominated solution of the optimal Pareto solution set and normalize the objective function corresponding to the non-dominated solution. The third calculation subunit is used to calculate the second Euclidean distance between each solution in the optimal Pareto solution set and the ideal point; Sub-units are established to construct sample matrices based on the normalized objective function and the second Euclidean distance.

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