Multi-target capacity configuration method for optical storage charging station coupled with hydrogen energy

Abstract

The invention relates to a multi-target capacity configuration method for an optical storage charging station coupled with hydrogen energy, and the method comprises the steps: building a charging station multi-target capacity configuration model by taking the minimum annual construction cost, the minimum annual light abandoning and load losing capacity and the minimum annual total emission as target functions and taking energy balance and energy storage power as constraint conditions; solving the multi-target capacity configuration model of the charging station by adopting an improved non-dominated sorting genetic algorithm to obtain a Pareto solution set; and a Pareto solution set is decided through an interactive criterion, and a capacity configuration scheme with optimal comprehensive benefits is obtained. Compared with the prior art, the method can ensure the rationality of the capacity configuration of the charging station, improves the comprehensive benefits of the charging station, considers the economy, reliability and environmental protection of the capacity configuration scheme of the charging station at the same time, can obtain a compromise capacity configuration scheme with the optimal comprehensive benefits of the charging station, and globally ensures the rationality of the configuration capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity planning of photovoltaic storage charging stations, and in particular to a multi-objective capacity configuration method for photovoltaic storage charging stations coupled with hydrogen energy. Background Art

[0002] Driven by the background of clean energy transformation, new energy vehicles represented by electric vehicles and hydrogen fuel cell vehicles have achieved great development with their advantages of high energy efficiency and zero emissions. In order to adapt to the rapid development trend of new energy vehicles, it is urgent to accelerate the construction of charging stations and hydrogen refueling stations. Considering that the separate planning of charging stations and hydrogen refueling stations will cause excess land occupation and lead to high investment costs, the photovoltaic storage charging station coupled with hydrogen energy with integrated electricity and hydrogen load has become a hot research object at present. However, due to the uncertainty of electric vehicle charging load, hydrogen fuel cell vehicle charging load and photovoltaic energy output, it is necessary to configure the capacity of key equipment in the station. However, the current research on charging station capacity configuration is mostly limited to the economy of the system, lacking consideration of system reliability and environmental protection, which easily leads to unreasonable charging station capacity configuration results, resulting in too low comprehensive benefits of charging stations, which is not conducive to the actual promotion and application of charging stations. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a multi-objective capacity configuration method for a photovoltaic storage charging station coupled with hydrogen energy, which can ensure the rationality of the capacity configuration of the charging station and improve the comprehensive benefits of the charging station.

[0004] The purpose of the present invention can be achieved by the following technical solution: A multi-objective capacity configuration method for a photovoltaic storage charging station coupled with hydrogen energy, comprising the following steps:

[0005] S1. Taking the annual construction cost, annual abandoned solar power and lost load, and annual total emissions as the minimum objective function, and taking energy balance and energy storage power as constraints, a multi-objective capacity configuration model for charging stations is constructed;

[0006] S2. Using an improved non-dominated sorting genetic algorithm, the multi-objective capacity configuration model of charging stations is solved to obtain the Pareto solution set;

[0007] S3. Through interactive criteria, the Pareto solution set is determined to obtain the capacity configuration plan with the best comprehensive benefits.

[0008] Furthermore, the objective function in step S1 includes a first objective function, a second objective function and a third objective function, and the first objective function is:

[0009]

[0010]

[0011]

[0012]

[0013] Among them, f1 is the annual construction cost of the charging station, S is the number of typical daily scenarios, ω s is the weight corresponding to the sth scenario, C inv is the annualized investment cost, C op is the operation and maintenance cost, C grid,t is the electricity purchase cost, r is the expected life of the equipment; m is the actual discount rate, and are the configuration capacity and unit capacity investment cost of energy storage batteries, electrolyzers and hydrogen storage tanks, respectively; α1, β1 and γ1 are the maintenance cost ratio coefficients of energy storage batteries, electrolyzers and hydrogen storage tanks, respectively. is the purchased power interacting with the grid at time t, is the electricity price of the power grid at time t;

[0014] The second objective function is:

[0015]

[0016] in, and are the photovoltaic power reduction and hydrogen load loss respectively. and They are PV power reduction cost and hydrogen load missing penalty respectively;

[0017] The third objective function is:

[0018]

[0019] in, is the emission cost of 1kW·h electricity, are the emission densities of carbon oxides, nitrogen oxides and sulfur oxides, They are the emission costs of carbon oxides, nitrogen oxides and sulfur oxides respectively.

[0020] Furthermore, the constraints in step S1 include electric power balance constraints, hydrogen energy balance constraints, charge and discharge power constraints and energy storage state constraints.

[0021] Furthermore, the electric power balance constraint is:

[0022]

[0023] in, and are the photovoltaic power generation and electrical load power at time t in the sth scenario, and are the charging power and discharging power of the energy storage battery at time t, is the electrolytic cell input power at time t, Cut power for photovoltaics;

[0024] The hydrogen energy balance constraint is:

[0025]

[0026] in, The amount of hydrogen produced by the electrolyzer consuming 1kW·h of electricity, is the hydrogen production efficiency of the electrolyzer, is the hydrogen load at time t in the sth scenario, ΔS HS,t is the change in hydrogen storage capacity in the hydrogen storage tank at time t.

[0027] Furthermore, the charge and discharge power constraints are:

[0028]

[0029]

[0030] in, and They are the upper limits of charging and discharging power of energy storage batteries;

[0031] The energy storage state constraint is:

[0032] 0.1≤SOE t / SOE max ≤0.9

[0033]

[0034] Among them, SOE t is the remaining capacity of the energy storage battery at time t, SOE max is the upper limit of the energy storage battery capacity, is the hydrogen storage capacity of the hydrogen storage tank at time t, It is the upper limit of hydrogen storage capacity of hydrogen storage tank.

[0035] Furthermore, the model solving process of the improved non-dominated sorting genetic algorithm in step S2 includes:

[0036] S21, input typical day data, basic parameters of each device and system constraints, and initialize them at the same time to generate reference points of the normalized hyperplane;

[0037] S22. For each subspace, a total number of N initial parent populations P are randomly generated through genetic operations. t ;

[0038] S23, initialize the population P t Perform crossover and mutation operations to obtain the offspring population Q t , and place the evolved individuals into a new population S t ;

[0039] S24, calculate the values ​​of each objective function according to the built charging station model, perform non-dominated sorting, and sort the population S t The non-dominated individuals with high medium priority, i.e. the optimized objective function values, are directly retained to the next generation;

[0040] S25, using the K-means algorithm to perform clustering operations, obtain the cluster center of the initial reference point and replace the initial reference point, calculate the shortest distance from each subspace individual to the cluster reference point, and record the number of dominant individuals associated with it;

[0041] S26, adding solutions with smaller penalty-based boundary intersection aggregation function values ​​to the next generation, applying a penalty function, and screening solutions with strong convergence;

[0042] S27, judging whether the number of iterations of the population meets the set maximum number of iterations. If the maximum number of iterations is reached, the optimized Pareto solution set is output, otherwise the S23-S27 loop is entered, where the Pareto solution set includes all non-dominated layer individuals, that is, multiple capacity configuration schemes corresponding to the lowest objective function value;

[0043] S28. The obtained optimized solution set is archived and stored externally, and the new non-dominated solution set is compared with the original solution set to update the external archive set to ensure that all solutions in the archive set do not dominate each other.

[0044] Furthermore, the aggregate function value of the boundary intersection based on the penalty in step S26 is:

[0045] d(x j ) = d i,1 (x j )+θd i,2 (x j )

[0046] d i,1 (x j )=∥(f n (x j )) T λ i ∥ / ∥λ i ∥

[0047] d i,2 (x j )=∥f n (x j )-d i,1 (xj )(λ i / ∥λ i ∥)∥

[0048] Among them, x j For the jth th The second solution, d i,1 (x j ) and d i,2 (x j ) are x j The projection distance and vertical distance in the direction of the i-th cluster center, θ is the penalty parameter, and is the direction vector from the reference point to the i-th cluster center.

[0049] Furthermore, the step S3 specifically includes the following steps:

[0050] S31. Determine the evaluation matrix and assign the Pareto solution set A i (i=1,2,3,…,n) in the objective function f j The evaluation matrix P under (j=1,2,3) is [x ij ] n×3 ;

[0051] S32, determine the objective function f j Relative to the reference function f r The relative weight of

[0052] S33, calculate A i and A k Relative to the objective function f j The relative advantage of Φ j (A i ,A k )(i,k=1,2,…,n; i≠k,j=1,2,3);

[0053] S34, Standardized comprehensive superiority The comprehensive prospect values ​​of each capacity configuration scheme of the charging station are calculated and ranked. The scheme with the highest ranking is the capacity configuration scheme with the best comprehensive benefits.

[0054] Furthermore, the relative advantage degree Φ in step S33 j (A i ,A k )for:

[0055]

[0056]

[0057]

[0058] Among them, d ij For x ij With x kj The distance between It is the attenuation factor, which indicates the sensitivity to the annual construction cost, annual abandoned light and lost load, and annual total emissions. The larger the value, the lower the degree of loss avoidance.

[0059] Furthermore, the standardized comprehensive advantage degree in step S34 for:

[0060]

[0061]

[0062] Among them, δ(A i ,A k ) is configuration scheme A i Compared with configuration plan A k overall relative advantage.

[0063] Compared with the prior art, the present invention has the following advantages:

[0064] The present invention takes the annual construction cost, the annual abandoned light and lost load, and the annual total emission as the minimum objective function, and takes the energy balance and energy storage power as the constraint conditions to construct a multi-objective capacity configuration model for charging stations; then the non-dominated sorting genetic algorithm is used to solve the multi-objective capacity configuration model for charging stations to obtain the Pareto solution set; finally, the Pareto solution set is decided by the interactive criterion to obtain the capacity configuration plan with the best comprehensive benefit. In this way, the economy, reliability, and environmental protection of the charging station capacity configuration plan are taken into consideration at the same time, and a compromise capacity configuration plan with the best comprehensive benefit of the charging station can be obtained, which ensures the rationality of the configuration capacity from a global perspective.

[0065] The present invention solves the constructed multi-objective capacity configuration model of charging stations by improving the non-dominated sorting genetic algorithm. In the improved non-dominated sorting genetic algorithm, solutions with smaller penalty-based boundary intersection aggregation function values ​​are added to the next generation, and a penalty function is applied to screen solutions with strong convergence, which can improve the convergence speed of the solution and ensure the stability of the solution process, thereby ensuring that a reliable Pareto solution set is obtained.

[0066] The present invention obtains the Pareto solution set through interactive criterion decision-making, searches for the optimal solution in the Pareto solution set through decision-making, and calculates the relative advantage degree and the standardized comprehensive advantage degree in the decision-making process, so as to ensure that the optimal capacity configuration plan is decided. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0068] Figure 2 This is a schematic diagram of the structure of a photovoltaic storage charging station coupled with hydrogen energy in an embodiment;

[0069] Figure 3 Schematic diagram of photovoltaic output curve in the embodiment;

[0070] Figure 4 This is a schematic diagram of the electricity-hydrogen load curve in the embodiment;

[0071] Figure 5 is the Pareto solution set obtained in the embodiment. DETAILED DESCRIPTION

[0072] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0073] Example

[0074] like Figure 1 As shown, a method for configuring the capacity of a photovoltaic storage charging station coupled with hydrogen energy comprises the following steps:

[0075] S1. Taking the annual construction cost, annual abandoned solar power and lost load, and annual total emissions as the minimum objective function, and taking energy balance and energy storage power as constraints, a multi-objective capacity configuration model for charging stations is constructed;

[0076] S2. Using an improved non-dominated sorting genetic algorithm, the multi-objective capacity configuration model of charging stations is solved to obtain the Pareto solution set;

[0077] S3. Obtain the Pareto solution set through interactive criterion decision-making and obtain the capacity configuration plan with the best comprehensive benefits.

[0078] In this embodiment, the structure of a photovoltaic storage charging station coupled with hydrogen energy is taken as the research object, such as Figure 2 As shown, it mainly includes photovoltaic power generation equipment, battery energy storage system, electrolyzer and hydrogen storage tank, and is connected to the upper power grid. The charging station uses the energy management system as the control center to update the photovoltaic power generation power and load demand information in real time. By coordinating the control of energy storage battery charging and discharging and electrolyzer energy conversion, it can cope with the uncertainty of charging and hydrogen refueling behaviors.

[0079] Taking a photovoltaic charging station as the optimization object, according to the selected geographical location factors and load type characteristics, the source and load sequence of the whole year is generated with reference to the local historical weather and operation data, and segmented clustering is performed to obtain three typical days in summer, transition season and winter. The source-load curve is as follows: Figure 3 and Figure 4Combined with the source-load timing scenario, the improved non-dominated sorting genetic algorithm (NSGA-III) is used to simulate and solve the multi-objective capacity configuration model of charging stations and analyze the results. The algorithm parameters are set as follows: the initial population size N is 100, the maximum number of iterations is 50, the crossover probability is 0.9, and the mutation probability is 0.1.

[0080] The method of the present invention is applied to this embodiment, and the specific process is as follows:

[0081] 1. Initialize system parameters. Input the basic parameters of the improved NSGA-Ⅲ algorithm, equipment parameters, photovoltaic output and typical daily data of electric-hydrogen load, set the electric power balance constraints, hydrogen energy balance constraints, charging and discharging power constraints and energy storage state constraints, and initialize them at the same time to generate the reference point of the normalized hyperplane;

[0082] 2. Generate parent population P t For the integrated station system, according to the genetic operation, the initial parent population P is randomly generated t , the total number is N, the parent population is P t The individual i in represents the configuration capacity of the decision variables (energy storage battery, electrolyzer and hydrogen storage tank);

[0083] 3. Crossover evolution. The DE population individual selection strategy is used to operate the initial population or the iterative parent population, and then the dominant individual P t Perform crossover and mutation operations to obtain the offspring population Q t , put the crossover mutation evolutionary individuals into the new population S t , until the number of individuals is greater than the maximum population number N;

[0084] 4. Fast non-dominated sort. Combine the parent and child populations and perform fast non-dominated sorting to sort the population S t The individuals in the non-dominated layer with high medium priority are directly retained to the next generation. The minimum value set of each dimension in each objective function is established as the ideal point, and adaptively normalized with the objective function value;

[0085] 5. Cluster the initial reference points. Use the K-means algorithm to perform clustering operations, obtain the cluster center of the initial reference point and replace the initial reference point, and divide the target space into k subspaces {S1, S2, …, S k}, calculate the shortest distance from each subspace individual to the cluster reference point, and record the number of associated dominant individuals;

[0086] 6. Use NP (Niche-Preservation) aggregation based on penalty boundary intersection. Add solutions with smaller penalty boundary intersection aggregation function values ​​to the next generation, apply penalty function, and screen solutions with strong convergence;

[0087] 7. Determine whether the number of iterations of the population meets the set maximum number of iterations. If the maximum number of iterations is reached, output the optimized Pareto solution set, otherwise enter the 3-7 loop;

[0088] 8. The optimized solution set obtained in the previous step is stored in an external archive. The new non-dominated solution set needs to be compared with the original solution set to update the external archive set. In order to increase the diversity of solutions, the roulette method needs to be used to eliminate redundant solution sets, while ensuring that all solutions in the archive set do not dominate each other.

[0089] In step 6, the penalty-based boundary intersection aggregation function is specifically:

[0090] d(x j ) = d i,1 (x j )+θd i,2 (x j )

[0091] d i,1 (x j )=∥(f n (x j )) T λ i ∥ / ∥λ i ∥

[0092] d i,2 (x j )=∥f n (x j )-d i,1 (x j )(λ i / ∥λ i ∥)∥

[0093] Where: x j For the jth th The second solution, d i,1 (x j ) and d i,2 (x j ) are x j The projection distance and vertical distance in the direction of the center of the i-th cluster, θ is the penalty parameter, which is the direction vector from the ideal point to the center of the i-th cluster;

[0094] The objective function of the charging station includes:

[0095]

[0096]

[0097]

[0098]

[0099] Where: f1 is the annual construction cost of the charging station, S is the number of typical daily scenarios; ω s is the weight corresponding to the sth scenario; C inv is the annualized investment cost; C op C is the operation and maintenance cost; grid,t is the electricity purchase cost; r is the expected life of the equipment; m is the actual discount rate; and are the configuration capacity and unit capacity investment cost of energy storage batteries, electrolyzers and hydrogen storage tanks respectively; α1, β1 and γ1 are the maintenance cost ratio coefficients of energy storage batteries, electrolyzers and hydrogen storage tanks respectively; is the purchased power interacting with the grid at time t; is the electricity price of the power grid at time t.

[0100]

[0101] Where: and They are the photovoltaic power reduction and hydrogen load loss respectively; and They are PV power reduction cost and hydrogen load loss penalty respectively.

[0102]

[0103] Where: is the emission cost of 1kW·h of electricity; are the emission densities of carbon oxides, nitrogen oxides and sulfur oxides, respectively; They are the emission costs of carbon oxides, nitrogen oxides and sulfur oxides respectively.

[0104] The constraints include:

[0105] Electric power balance constraints:

[0106]

[0107] Where: and are the photovoltaic power generation and electrical load power at time t in the sth scenario respectively; and are the charging power and discharging power of the energy storage battery at time t respectively; is the electrolytic cell input power at time t; Cut power for photovoltaics.

[0108] Hydrogen energy balance constraints:

[0109]

[0110] Where: The amount of hydrogen produced by the electrolyzer consuming 1 kW·h of electricity; is the hydrogen load at time t in the sth scenario; ΔS HS,t is the change in hydrogen storage capacity in the hydrogen storage tank at time t.

[0111] Charge and discharge power constraints:

[0112]

[0113]

[0114] Where: and They are the upper limits of charging and discharging power of energy storage batteries respectively.

[0115] Hydrogen energy balance constraints:

[0116] 0.1≤SOE t / SOE max ≤0.9

[0117]

[0118] Where: SOE t is the remaining capacity of the energy storage battery at time t; SOE max The upper limit of the energy storage battery capacity; is the hydrogen storage capacity of the hydrogen storage tank at time t; It is the upper limit of hydrogen storage capacity of hydrogen storage tank.

[0119] After obtaining the Pareto solution set, continue to make decisions on the optimal capacity allocation solution:

[0120] First determine the evaluation matrix and assign the Pareto solution set A i (i=1,2,3,…,n) in the objective function f j The evaluation matrix P under (j=1,2,3) is [x ij ] n×3 ;

[0121] Then determine the objective function f j Relative to the reference function f r The relative weight of

[0122] Calculate A i and A kRelative to the objective function f j The relative advantage of Φ j (A i ,A k )(i,k=1,2,…,n;i≠k,j=1,2,3):

[0123]

[0124]

[0125] Among them, d ij For x ij With x kj The distance between is the attenuation factor, which indicates the sensitivity to the annual construction cost, annual abandoned solar power and lost load, and annual total emissions. The larger the value, the lower the degree of loss avoidance.

[0126] Standardized comprehensive superiority Calculate the comprehensive prospect value of each capacity configuration scheme of the charging station and rank them. The highest ranked scheme is the capacity configuration scheme with the best comprehensive benefit. The standardized comprehensive advantage for:

[0127]

[0128]

[0129] Among them, δ(A i ,A k ) is configuration scheme A i Compared with configuration plan A k overall relative advantage.

[0130] Finally, the capacity configuration scheme of this embodiment is obtained as follows: Figure 5 As shown in the figure, through interactive multi-criteria decision-making, the minimum annual construction cost is 336,248.59 yuan, the annual abandoned light and lost load cost is 392.34 yuan, and the annual total emission cost is 52,793.68 yuan. The battery energy storage capacity is 550 kW h, the electrolyzer is 110 kW, and the hydrogen storage tank is 386 Nm 3 , can achieve the best comprehensive benefits.

[0131] In summary, the present invention provides a multi-objective optimization configuration method for photovoltaic storage charging stations coupled with hydrogen energy, which takes into account the economy, reliability and environmental protection of the charging station capacity configuration plan to achieve the best comprehensive benefits. This method can globally ensure the rationality of the charging station capacity configuration plan, and can ensure that the capacity configuration plan with the best comprehensive benefits is obtained quickly and accurately.

Claims

1. A multi-objective capacity configuration method for a photovoltaic storage charging station coupled with hydrogen energy, characterized in that: The following steps are involved: S1. Taking the annual construction cost, annual abandoned solar power and lost load, and annual total emissions as the minimum objective function, and taking energy balance and energy storage power as constraints, a multi-objective capacity configuration model for charging stations is constructed; S2. Using an improved non-dominated sorting genetic algorithm, the multi-objective capacity configuration model of charging stations is solved to obtain the Pareto solution set; S3. Through interactive criteria, the Pareto solution set is determined to obtain the capacity configuration plan with the best comprehensive benefits.

2. According to the multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 1, it is characterized in that: The objective function in step S1 includes a first objective function, a second objective function and a third objective function, wherein the first objective function is: Among them, f1 is the annual construction cost of the charging station, S is the number of typical daily scenarios, ω s is the weight corresponding to the sth scenario, C inv is the annualized investment cost, C op is the operation and maintenance cost, C grid,t is the electricity purchase cost, r is the expected life of the equipment; m is the actual discount rate, and are the configuration capacity and unit capacity investment cost of energy storage batteries, electrolyzers and hydrogen storage tanks, respectively; α1, β1 and γ1 are the maintenance cost ratio coefficients of energy storage batteries, electrolyzers and hydrogen storage tanks, respectively. is the purchased power interacting with the grid at time t, is the electricity price of the power grid at time t; The second objective function is: in, and are the photovoltaic power reduction and hydrogen load loss respectively. and They are PV power reduction cost and hydrogen load missing penalty respectively; The third objective function is: in, is the emission cost of 1kW·h electricity, are the emission densities of carbon oxides, nitrogen oxides and sulfur oxides, They are the emission costs of carbon oxides, nitrogen oxides and sulfur oxides respectively.

3. According to the multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 1, it is characterized in that: The constraints in step S1 include electric power balance constraints, hydrogen energy balance constraints, charge and discharge power constraints, and energy storage state constraints.

4. According to claim 3, a multi-objective capacity configuration method for a photovoltaic storage charging station coupled with hydrogen energy is characterized in that: The electric power balance constraint is: in, and are the photovoltaic power generation and electrical load power at time t in the sth scenario, and are the charging power and discharging power of the energy storage battery at time t, is the electrolytic cell input power at time t, Cut power for photovoltaics; The hydrogen energy balance constraint is: in, The amount of hydrogen produced by the electrolyzer consuming 1kW·h of electricity, is the hydrogen production efficiency of the electrolyzer, is the hydrogen load at time t in the sth scenario, ΔS HS,t is the change in hydrogen storage capacity in the hydrogen storage tank at time t.

5. The multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 3 is characterized in that: The charge and discharge power constraints are: in, and They are the upper limits of charging and discharging power of energy storage batteries; The energy storage state constraint is: 0.1≤SOE t / SOO max ≤0.9 Among them, SOE t is the remaining capacity of the energy storage battery at time t, SOE max is the upper limit of the energy storage battery capacity, is the hydrogen storage capacity of the hydrogen storage tank at time t, It is the upper limit of hydrogen storage capacity of hydrogen storage tank.

6. The multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 1 is characterized in that: The model solving process of the improved non-dominated sorting genetic algorithm in step S2 includes: S21, input typical day data, basic parameters of each device and system constraints, and initialize them at the same time to generate reference points of the normalized hyperplane; S22. For each subspace, a total number of N initial parent populations P are randomly generated through genetic operations. t ; S23, initialize the population P t Perform crossover and mutation operations to obtain the offspring population Q t , and place the evolved individuals into a new population S t ; S24, calculate the values ​​of each objective function according to the built charging station model, perform non-dominated sorting, and sort the population S t The non-dominated individuals with high medium priority, i.e. the optimized objective function values, are directly retained to the next generation; S25, using the K-means algorithm to perform clustering operations, obtain the cluster center of the initial reference point and replace the initial reference point, calculate the shortest distance from each subspace individual to the cluster reference point, and record the number of dominant individuals associated with it; S26, adding solutions with smaller penalty-based boundary intersection aggregation function values ​​to the next generation, applying a penalty function, and screening solutions with strong convergence; S27, judging whether the number of iterations of the population meets the set maximum number of iterations. If the maximum number of iterations is reached, the optimized Pareto solution set is output, otherwise the S23-S27 loop is entered, where the Pareto solution set includes all non-dominated layer individuals, that is, multiple capacity configuration schemes corresponding to the lowest objective function value; S28. The obtained optimized solution set is archived and stored externally, and the new non-dominated solution set is compared with the original solution set to update the external archive set to ensure that all solutions in the archive set do not dominate each other.

7. A multi-objective capacity configuration method for a photovoltaic storage charging station coupled with hydrogen energy according to claim 6, characterized in that: The aggregate function value of the boundary intersection based on the penalty in step S26 is: d(x j )=d i,1 (x j )+θd i,2 (x j ) d i,1 (x j )=∥(f n (x j )) T λ i ∥ / ∥λ i ∥ d i,2 (x j )=∥f n (x j )-d i,1 (x j )(λ i / ∥λ i ∥)∥ Among them, x j For the jth th The second solution, d i,1 (x j ) and d i,2 (x j ) are x j The projection distance and vertical distance in the direction of the i-th cluster center, θ is the penalty parameter, and is the direction vector from the reference point to the i-th cluster center.

8. The multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: S31. Determine the evaluation matrix and assign the Pareto solution set A i (i=1,2,3,…,n) in the objective function f j The evaluation matrix P under (j=1,2,3) is [x ij ] n×3 ; S32, determine the objective function f j Relative to the reference function f r The relative weight of S33, calculate A i and A k Relative to the objective function f j The relative advantage of Φ j (A i ,A k )(i,k=1,2,…,n; i≠k,j=1,2,3); S34, Standardized comprehensive superiority The comprehensive prospect values ​​of each capacity configuration scheme of the charging station are calculated and ranked. The scheme with the highest ranking is the capacity configuration scheme with the best comprehensive benefits.

9. The multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 8 is characterized in that: The relative advantage degree Φ in step S33 j (A i ,A k )for: Among them, d ij For x ij With x kj The distance between It is the attenuation factor, which indicates the sensitivity to the annual construction cost, annual abandoned light and lost load, and annual total emissions. The larger the value, the lower the degree of loss avoidance.

10. The multi-objective capacity configuration method of a photovoltaic storage charging station coupled with hydrogen energy according to claim 9, characterized in that: The standardized comprehensive advantage in step S34 for: Among them, δ(A i ,A k ) is configuration scheme A i Compared with configuration plan A k overall relative advantage.

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