A multi-objective optimization configuration method for network-constructed energy storage

Abstract

The application relates to the technical field of network-constructing energy storage, in particular to a network-constructing energy storage multi-target optimization configuration method. The method comprises the following steps: establishing multiple station nodes according to regional parameters; generating multiple initial allocation strategies according to a preset first-level processing model, and generating an initial population set according to all the initial allocation strategies; generating an optimal subset according to a preset multi-target function F and the initial population set, and generating a network-constructing energy storage configuration strategy according to a second-level processing model and the optimal subset. The voltage support effect of the network-constructing energy storage is quantified, the configuration efficiency of each station node is sorted, a multi-target function with the lowest construction cost, the highest new energy utilization rate and the best temporary stability effect is constructed, the optimal solution set is obtained through a genetic algorithm based on non-dominated sorting and individual crowding degree calculation, the optimal network-constructing energy storage configuration scheme is determined through a grey relational projection method, and the network-constructing energy storage capacity of each station is finely allocated.

Description

Technical Field

[0001] This application relates to the field of grid-type energy storage technology, and in particular to a multi-objective optimization configuration method for grid-type energy storage. Background Technology

[0002] In scenarios with a high proportion of renewable energy, the demand for energy flexibility has increased significantly. Electrochemical energy storage, as a high-quality flexibility resource, can address the randomness and volatility issues brought about by renewable energy installations, ensuring grid stability, reliable power supply, and safe operation. It can also significantly improve the power system's peak-shaving, frequency regulation, and voltage regulation capabilities. Adding grid-based energy storage systems with frequency regulation and voltage control capabilities similar to synchronous generators near renewable energy sources can comprehensively enhance the power system's regulation capabilities and flexibility.

[0003] In existing R&D technologies, energy storage planning generally takes network loss as the objective function and considers various characteristics to determine the location and capacity of distributed energy storage. However, current technologies do not simultaneously consider the economic and technical aspects of energy storage operation for joint optimization, neglect the various problems faced by high-proportion new energy systems, and have failed to discover a suitable multi-objective method for the fixed-capacity optimization configuration of grid-type energy storage power stations.

[0004] Application content

[0005] The purpose of this application is to provide a multi-objective optimization configuration method for grid-type energy storage in order to solve the above-mentioned technical problems, thereby optimizing the configuration scheme of grid-type energy storage and improving the operating benefits of grid-type energy storage systems.

[0006] In some embodiments of this application, the configuration efficiency of each site node is ranked by quantifying the voltage support effect of grid-connected energy storage, and a multi-objective function with the lowest cost, highest renewable energy utilization rate, and best transient stability effect is constructed. The optimal solution set is obtained by a genetic algorithm based on non-dominated sorting and individual congestion calculation, and the optimal grid-connected energy storage configuration scheme is determined by the grey relational projection method, thereby realizing the fine allocation of grid-connected energy storage capacity for each site.

[0007] In some embodiments of this application, a multi-objective optimization configuration method for grid-type energy storage is provided, including:

[0008] Establish multiple station nodes based on regional parameters;

[0009] Multiple initial allocation strategies are generated based on the preset first-level processing model, and an initial seed cluster is generated based on all the initial allocation strategies.

[0010] The optimal subset is generated based on the preset multi-objective function F and the initial population, and the grid-based energy storage configuration strategy is generated based on the secondary processing model and the optimal subset.

[0011] In some embodiments of this application, multiple initial allocation strategies are generated based on a preset first-level processing model, including:

[0012] Obtain the rated total power capacity P of grid-connected energy storage st.N ;

[0013] Set the first total power capacity threshold Second total power capacity threshold

[0014] Generate an initial configuration sequence {M} based on the primary processing model;

[0015]

[0016] Where N is the number of station nodes;

[0017] Extract the first N0 subsets of the sequence {M} to generate the first-level configuration sequence {H}, where {H} = {η} i}, i = 1, 2, ..., N0;

[0018] Preprocess the first-level configuration sequence;

[0019]

[0020] Set the initial P st.N For P st.min ;

[0021] Generate initial allocation strategy p st.N (i) 1 ;

[0022] p st.N (i) 1 =P st.min ·η i * ,i=1,2,…,N0.

[0023] In some embodiments of this application, generating multiple initial allocation strategies based on a preset first-level processing model further includes:

[0024] set up 0≤η i * ≤1,

[0025] P st.N Divided into K levels;

[0026] from Start towards search;

[0027] Generate N0 random numbers between [0,1], and generate a random η based on the preprocessing results. i * sequence;

[0028] P st.N Multiply by η respectively i * Generate a single initial allocation strategy;

[0029] The process is repeated to generate multiple initial allocation strategies.

[0030] In some embodiments of this application, an initial seed cluster is generated according to all initial allocation strategies, including:

[0031] Generate and determine each initial allocation strategy based on a preset constraint model;

[0032] Multiple primary allocation strategies are generated based on the judgment results;

[0033] The correction parameters for each primary allocation strategy are generated based on the preset correction model;

[0034] An initial population cluster is generated based on the correction results.

[0035] In some embodiments of this application, a multi-objective function F is preset, including:

[0036] Establish the total cost function model f1;

[0037] Establish an energy utilization rate function model f2;

[0038] Establish a steady-state evaluation function model f3;

[0039] Based on the total cost function model f1, the energy utilization rate function model f2, and the steady-state evaluation function model f3, a multi-objective function F is generated.

[0040] Let F = min(f1, -f2, -f3).

[0041] In some embodiments of this application, the total cost function model f1 is established, including:

[0042] f1=k1·E st +k2·P st ;

[0043] Where k1 represents the cost per unit of energy storage capacity, k2 represents the cost per unit increase in energy storage power, and E st For the total capacity of grid-connected energy storage, P st The total power of the grid-connected energy storage.

[0044] In some embodiments of this application, the energy utilization rate function model f2 is established, including:

[0045] f2=1-ζ dump ;

[0046]

[0047] Where, p dump (i) represents the curtailed wind and solar power in time period i; Δt represents the duration of a single time period; T represents the number of time periods; Y(i) represents the selection coefficient; if P dump (i)-P st If (i)>0, Y(i)=1; if P dump (i)-P st (i)<0, Y(i)=0;E dump To determine the total amount of wind and solar power curtailment after configuring energy storage; ζ dump The curtailment rate of wind and solar power after configuring energy storage; p ideal (i) represents the total power generation in the i-th time period; p st (j) The energy storage power of the i-th station node in the i-th time period.

[0048] In some embodiments of this application, a steady-state evaluation function model f3 is established, including:

[0049]

[0050] ΔU p.u (i) represents the change in the per-unit value of the system voltage before and after the i-th configuration of grid-connected energy storage; U p.u 1 (i), U p.u 0 (i) represents the per-unit value of the system voltage before and after configuring grid-connected energy storage under the same transient stability fault, p st (i) represents the energy storage configuration power of the i-th station node.

[0051] In some embodiments of this application, an optimal subset is generated based on a preset multi-objective function F and an initial population, including:

[0052] Generate the objective function value set for each first-level allocation strategy;

[0053] Generate a dominance relationship mapping table between each first-level allocation strategy based on all objective function value sets;

[0054] Generate the first and second subpopulations based on the dominance relationship mapping table;

[0055] Search the first and second subpopulations according to the preset optimization model;

[0056] Generate the optimal subset based on the search results.

[0057] Compared with existing technologies, the multi-objective optimization configuration method for grid-type energy storage proposed in this application has the following advantages:

[0058] By quantifying the voltage support effect of grid-connected energy storage, the configuration efficiency of each power station node is ranked, and a multi-objective function is constructed that achieves the lowest cost, highest renewable energy utilization rate, and best transient stability effect. The optimal solution set is obtained through a genetic algorithm based on non-dominated sorting and individual congestion calculation, and the optimal grid-connected energy storage configuration scheme is determined using the grey relational projection method. This achieves the optimal configuration of each power station node.

[0059] The energy storage capacity of each power station is allocated in a refined manner. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a multi-objective optimization configuration method for grid-type energy storage in a preferred embodiment of the present invention. Detailed Implementation

[0061] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0062] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0063] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0064] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0065] like Figure 1 As shown in the preferred embodiment of this application, a multi-objective optimization configuration method for grid-type energy storage includes:

[0066] S101: Establish multiple site nodes based on regional parameters;

[0067] S102: Generate multiple initial allocation strategies based on the preset first-level processing model, and generate an initial seed cluster based on all initial allocation strategies;

[0068] S103: Generate the optimal subset based on the preset multi-objective function F and the initial seed cluster, and generate the grid-based energy storage configuration strategy based on the secondary processing model and the optimal subset.

[0069] Specifically, multiple power station nodes are established based on the parameters of new energy power stations within the planned power grid area, with each power station node representing a new energy power station.

[0070] Specifically, multiple initial allocation strategies are generated based on a preset primary processing model, including:

[0071] Obtain the rated total power capacity P of grid-connected energy storage st.N ;

[0072] Set the first total power capacity threshold Second total power capacity threshold

[0073] Generate an initial configuration sequence {M} based on the primary processing model;

[0074]

[0075] Where N is the number of station nodes;

[0076] Extract the first N0 subsets of the sequence {M} to generate the first-level configuration sequence {H}, where {H} = {η} i}, i = 1, 2, ..., N0;

[0077] Preprocess the first-level configuration sequence;

[0078]

[0079] Set the initial P st.N For P st.min ;

[0080] Generate initial allocation strategy p st.N (i) 1 ;

[0081] p st.N (i) 1 =P st.min ·η i* ,i=1,2,…,N0.

[0082] Specifically, based on the voltage support effect of grid-connected energy storage quantified by the first-level processing model, the configuration efficiency of each site node is ranked to generate the most efficient configuration sequence {M} for grid-connected energy storage.

[0083] Specifically, the output power p corresponding to the grid-connected energy storage can be further obtained based on a single initial allocation strategy. st (i), e st (i) and the solution of the multi-objective function.

[0084] Specifically, multiple initial allocation strategies are generated based on a preset primary processing model, including:

[0085] set up 0≤η i * ≤1,

[0086] P st.N Divided into K levels;

[0087] from Start towards search;

[0088] Generate N0 random numbers between [0,1], and generate a random η based on the preprocessing results. i * sequence;

[0089] P st.N Multiply by η respectively i * Generate a single initial allocation strategy;

[0090] The process is repeated to generate multiple initial allocation strategies.

[0091] Specifically, the initial sample p st.N (i) 1 It is only a potential better solution and cannot characterize the properties of the entire feasible solution set. Therefore, it is necessary to expand the initial calculation sample and take values ​​uniformly in the feasible solution set to achieve the goal of approximating the optimal solution as closely as possible.

[0092] The feasible solution set matrix is ​​an (N0)-dimensional matrix, represented as follows:

[0093] [P st.N η1 * η2 * …η N0 * ]→[p st.N (1) p st.N (2) … pst.N (N0)]

[0094] in, 0≤η i * ≤1, Furthermore, the smaller the serial number, the more efficient the energy storage configuration.

[0095] P st.N Divided into K levels;

[0096] from Start towards The search simultaneously generates N0 random numbers between [0,1], normalizes them, and then rearranges them in descending order to generate a random η. i * Sequence, P st.N Multiply by η respectively i * This generates a desired feasible solution matrix, thus enabling the construction of an initial allocation strategy.

[0097] In a preferred embodiment of this application, generating an initial seed cluster based on all initial allocation strategies includes:

[0098] Generate and determine each initial allocation strategy based on a preset constraint model;

[0099] Multiple primary allocation strategies are generated based on the judgment results;

[0100] The correction parameters for each primary allocation strategy are generated based on the preset correction model;

[0101] An initial population cluster is generated based on the correction results.

[0102] Specifically, a single initial allocation strategy includes the rated power capacity of each site node.

[0103] Specifically, the constraint models include:

[0104] 1) Economic constraints

[0105] The economic constraint on the total cost of grid-connected energy storage can be expressed as a constraint on P. st.max The constraints are expressed as follows:

[0106] f1=k1·E st +k2·P st ≤Ψ

[0107] 2) Actual energy storage power and capacity constraints

[0108] The actual power of the grid-connected energy storage at each site does not exceed the rated power. If the abandoned power is less than the rated energy storage power, then the abandoned power will be used to store renewable energy. i represents the site number.

[0109]

[0110] The actual storage capacity of the grid-connected energy storage at each site shall not exceed the rated capacity, and the daily charging power shall be based on the given charging power p. st (i) Charge until the rated energy storage capacity is reached or the abandoned power is cut off to zero:

[0111]

[0112] 3) Constraints on curtailment rates of new energy sources (wind and solar)

[0113] The power grid imposes certain constraints on the curtailment rate of renewable energy sources, generally requiring it not to exceed 5% or 10%. Considering that the curtailment rate is included in the multi-objective optimization function, a value of 10% can be preferred to prevent the loss of potential excellent solutions during the optimization process.

[0114] ζ dump ≤10%

[0115] At the same time, considering reasonable economics, it is unnecessary to completely store restricted new energy sources through energy storage. Therefore, the wind and solar curtailment rate must be greater than 0, with a lower limit of 2%.

[0116] ζ dump ≥2%

[0117] According to ζ dump The upper and lower limits of the grid-connected energy storage power can be obtained from the upper and lower limits of the grid-connected energy storage power, i.e., P. stmin and P stmax .

[0118] 4) Rated energy storage power and capacity constraints

[0119] The sum of the rated power of the grid-connected energy storage at each site must meet the power capacity constraint:

[0120]

[0121] At the same time, the sum of the rated capacity of the grid-connected energy storage at each site must meet the energy capacity constraint:

[0122]

[0123] Specifically, the rated power sequence corresponding to each initial allocation strategy must satisfy all the above constraints. If not, the corresponding initial allocation strategy must be eliminated.

[0124] Specifically, since the initial population is randomly generated, some solutions may be too dense or too sparse in the solution space. Uniformly distributed solutions are more effective at representing the characteristics of the overall solution space; therefore, it is necessary to define the distances between points.

[0125]

[0126] Search for the nearest point of each point and calculate the distance between the two points as a representation of the density of that point.

[0127] Points with unreasonable density in the initial population are filtered out, some points that are too close together are removed, and points that are too far apart are supplemented by taking the median value:

[0128] p st.N (i) xy =(p st.N (i) x +p st.N (i) y ) / 2;

[0129] Reorganize the initial population according to this principle to ensure that the initial population is distributed as evenly as possible in the solution space.

[0130] In a preferred embodiment of this application, a multi-objective function F is preset, including:

[0131] Establish the total cost function model f1;

[0132] Establish an energy utilization rate function model f2;

[0133] Establish a steady-state evaluation function model f3;

[0134] Based on the total cost function model f1, the energy utilization rate function model f2, and the steady-state evaluation function model f3, a multi-objective function F is generated.

[0135] Let F = min(f1, -f2, -f3).

[0136] Specifically, the total cost function model f1 is established, including:

[0137] f1=k1·E st +k2·P st ;

[0138] Where k1 represents the cost per unit of energy storage capacity, k2 represents the cost per unit increase in energy storage power, and E st For the total capacity of grid-connected energy storage, P st The total power of the grid-connected energy storage.

[0139] Specifically, the energy utilization rate function model f2 is established, including:

[0140] f2=1-ζ dump ;

[0141]

[0142] Where, p dump(i) represents the curtailed wind and solar power in time period i; Δt represents the duration of a single time period; T represents the number of time periods; Y(i) represents the selection coefficient; if P dump (i)-P st If (i)>0, Y(i)=1; if P dump (i)-P st (i)<0, Y(i)=0;E dump To determine the total amount of wind and solar power curtailment after configuring energy storage; ζ dump The curtailment rate of wind and solar power after configuring energy storage; p ideal (i) represents the total power generation in the i-th time period; p st (j) The energy storage power of the i-th station node in the i-th time period.

[0143] Specifically, the steady-state evaluation function model f3 is established, including:

[0144]

[0145] ΔU p.u (i) represents the change in the per-unit value of the system voltage before and after the i-th configuration of grid-connected energy storage; U p.u 1 (i), U p.u 0 (i) represents the per-unit value of the system voltage before and after configuring grid-connected energy storage under the same transient stability fault, p st (i) represents the energy storage configuration power of the i-th station node.

[0146] It is understandable that in the above embodiments, with the optimization goal of maximizing the overall new energy absorption capacity through grid-based energy storage, a multi-objective function is constructed that has the lowest cost, the highest new energy utilization rate, and the best temporary stability effect. This function is used to judge all initial allocation strategies and achieve refined allocation of grid-based energy storage capacity for each site.

[0147] In a preferred embodiment of this application, generating an optimal subset based on a preset multi-objective function F and an initial population includes:

[0148] Generate the objective function value set for each first-level allocation strategy;

[0149] Generate a dominance relationship mapping table between each first-level allocation strategy based on all objective function value sets;

[0150] Generate the first and second subpopulations based on the dominance relationship mapping table;

[0151] Search the first and second subpopulations according to the preset optimization model;

[0152] Generate the optimal subset based on the search results.

[0153] The initial population pst.N (i) x Substituting these values ​​into the calculation of the multi-objective function F yields a set of objective function values ​​(f1, -f2, -f3) of equal population size. These function values ​​are mutually dominant, defined as follows: 1) f1 x <f1 y -f2 x <-f2 y And -f3 x <-f3 y In the case of F x Completely superior to F y ;2)f1 x >f1 y -f2 x >-f2 y And -f3 x >-f3 y In the case of F x Completely inferior to F y 3) If neither of the above two relationships is satisfied, then F x With F y There is no dominant relationship.

[0154] Based on the above definition, the objective function value F is stratified and processed in a cyclical manner. In the first round, all function values ​​are filtered to select the subpopulation with the highest dominance order. No individual in this subpopulation can be dominated by any other individual, and there are no mutual dominance relationships within the subpopulation. Then, this subpopulation is removed from the population, and another subpopulation that cannot be dominated by other individuals is selected as the second dominance order subpopulation. This step is repeated until all individuals in the population have been traversed.

[0155] Following the non-dominated sorting method described above, the subpopulations with the first and second dominant orders in the objective function value F are selected as the optimal objective function value set corresponding to the initial population. At the same time, the initial population subset corresponding to this subpopulation is used as the elite subset and enters the initial population of the next round of fine search.

[0156] Specifically, the subpopulation with the first non-dominated order is set as the first subpopulation, the subpopulation with the second non-dominated order is set as the second subpopulation, and the first and second subpopulations are set as elite subsets, which are considered to be close to the optimal grid-based energy storage configuration in the solution space. Therefore, a second round of fine search is carried out in the vicinity of this subset.

[0157] Crossover operator: Select an individual from the elite subset, average it with the nearest individual, and use the average of the two as a new feasible solution;

[0158] Mutation operator: Select any individual in the elite subset, mutate any bit of its value, and use the mutated value as a new feasible solution;

[0159] Perform crossover and mutation operations on the elite subsets, and incorporate the newly generated subsets into the elite subsets. Repeat the above steps until the subset with the first non-dominated order remains unchanged or the loop reaches the limit number of times. Output the subset with the first non-dominated order as the final optimal subset.

[0160] Specifically, in the optimal subset, no two individuals can dominate each other, meaning that it is not possible to simply determine which individual is optimal. Therefore, a two-stage processing model is established using the grey relational projection method to determine the optimal individual. Based on the allocation strategy corresponding to the optimal individual, a grid-based energy storage configuration strategy is generated.

[0161] Based on the first concept of this application, the configuration efficiency of each site node is ranked by quantifying the voltage support effect of grid-connected energy storage, and a multi-objective function with the lowest cost, highest renewable energy utilization rate, and best transient stability effect is constructed. The optimal solution set is obtained by a genetic algorithm based on non-dominated sorting and individual congestion calculation, and the optimal grid-connected energy storage configuration scheme is determined by the grey relational projection method.

[0162] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A multi-objective optimization configuration method for grid-type energy storage, characterized in that, include: Establish multiple station nodes based on regional parameters; Multiple initial allocation strategies are generated based on the preset first-level processing model, and an initial seed cluster is generated based on all the initial allocation strategies. The optimal subset is generated based on the preset multi-objective function F and the initial seed cluster, and the grid-based energy storage configuration strategy is generated based on the secondary processing model and the optimal subset. Preset multi-objective function F, including: Establish the total cost function model f1; Establish an energy utilization rate function model f2; Establish a steady-state evaluation function model f3; Based on the total cost function model f1, the energy utilization rate function model f2, and the steady-state evaluation function model f3, a multi-objective function F is generated. Let F = min(f1, -f2, -f3); Establish the total cost function model f1, including: f1=k1·E st +k2·P st ; Where k1 represents the cost per unit of energy storage capacity, k2 represents the cost per unit increase in energy storage power, and E st For the total capacity of grid-connected energy storage, P st The total power of the grid-connected energy storage; Establish the energy utilization rate function model f2, including: f2=1-ζ dump ; Where, p dump (i) represents the curtailed wind and solar power in time period i; Δt represents the duration of a single time period; T represents the number of time periods; Y(i) represents the selection coefficient; if P dump (i)-P st If (i)>0, Y(i)=1; if P dump (i)-P st (i)<0, Y(i)=0; E dump To determine the total amount of wind and solar power curtailment after configuring energy storage; ζ dump The curtailment rate of wind and solar power after configuring energy storage; p ideal (i) represents the total power generation in the i-th time period; p st (j) The energy storage power of the i-th power station node in the i-th time period; Establish a steady-state evaluation function model f3, including: ΔU p.u (i) represents the change in the per-unit value of the system voltage before and after the i-th configuration of grid-connected energy storage; U p.u 1 (i), U p.u 0 (i) represents the per-unit value of the system voltage before and after configuring grid-connected energy storage under the same transient stability fault, p st (i) represents the energy storage configuration power of the i-th station node; The optimal subset is generated based on the preset multi-objective function F and the initial population, including: Generate the objective function value set for each first-level allocation strategy; Generate a dominance relationship mapping table between each first-level allocation strategy based on all objective function value sets; Generate the first and second subpopulations based on the dominance relationship mapping table; Search the first and second subpopulations according to the preset optimization model; Generate the optimal subset based on the search results.

2. The multi-objective optimization configuration method for grid-type energy storage as described in claim 1, characterized in that, Multiple initial allocation strategies are generated based on a preset primary processing model, including: Obtain the rated total power capacity P of grid-connected energy storage st.N ; Set the first total power capacity threshold Second total power capacity threshold Generate an initial configuration sequence {M} based on the primary processing model; Where N is the number of station nodes; ΔU p.u This indicates the change in the per-unit voltage value of the system before and after configuring grid-connected energy storage; It is an ascending function; Extract the first N0 subsets of the sequence {M} to generate the first-level configuration sequence {H}, where {H} = {η} i }, i = 1, 2, ..., N0; Preprocess the first-level configuration sequence; Set the initial P st.N For P st.min ; Generate initial allocation strategy p st.N (i) 1 ; p st.N (i) 1 =P st.min ·η i ,i=1,2,...,N0; Where, p st.N (i) 1 It is a potential better solution in the initial configuration strategy; p st (i) represents the real-time power capacity.

3. The multi-objective optimization configuration method for grid-type energy storage as described in claim 2, characterized in that, multiple initial allocation strategies are generated based on a preset primary processing model, and further includes: set up 0≤η i * ≤1, P st.N Divided into K levels; from Start towards search; Generate N0 random numbers between [0, 1], and generate a random η based on the preprocessing results. i * sequence; P st.N Multiply by η respectively i * Generate a single initial allocation strategy; The process is repeated to generate multiple initial allocation strategies.

4. The multi-objective optimization configuration method for grid-type energy storage as described in claim 3, characterized in that, An initial seed cluster is generated based on all initial allocation strategies, including: Generate and determine each initial allocation strategy based on a preset constraint model; Multiple primary allocation strategies are generated based on the judgment results; The correction parameters for each primary allocation strategy are generated based on the preset correction model; An initial population cluster is generated based on the correction results.

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