Green hydrogen energy storage capacity configuration method based on knee region multi-objective optimization algorithm

Abstract

The invention discloses a green hydrogen energy storage capacity configuration method based on a knee region multi-objective optimization algorithm, solves the problem of how to balance economy, safety and wind power utilization rate in green hydrogen energy storage capacity configuration, and belongs to the field of hydrogen storage capacity configuration. The method comprises the following steps: constructing a multi-objective optimization model of energy storage capacity configuration by taking the lowest annual investment cost and the lowest annual operation cost of green hydrogen energy storage equipment and the lowest annual wind curtailment penalty cost of each wind power plant as objectives; according to the characteristics of each subsystem in the green hydrogen energy storage system, establishing an operation constraint condition; and under an operation constraint condition, solving an optimal solution in the multi-objective optimization model by adopting a multi-objective optimization algorithm based on the knee region. According to the method, algorithm convergence can be guided under the condition of lacking priori knowledge, and solutions with good performance in economy, safety and wind power utilization rate can be found in a multi-system coupled wind-hydrogen combined system.

Description

Technical Field

[0001] The present invention relates to a green hydrogen energy storage capacity configuration method based on a knee-region multi-objective optimization algorithm, and belongs to the field of hydrogen storage capacity configuration. Background Art

[0002] As one of the first choices for clean and pollution-free energy, hydrogen has the characteristics of high energy density, large capacity, easy storage and transportation, and has become one of the common solutions for the development, utilization and storage of renewable energy such as wind power and photovoltaics. The electricity generated by wind power and photovoltaics can be stored. The current common method is to produce hydrogen through water electrolysis and store it in the form of chemical energy. In this way, hydrogen has multiple utilization channels. It can be used as a clean and efficient fuel energy, and can also be used directly. With the rapid development of efficient and clean utilization technologies such as fuel cells, hydrogen energy has been more fully developed and utilized. Hydrogen produced by wind power and photovoltaic power generation can be provided to fuel cell vehicles as a power source, so that vehicles can achieve the same travel effect using only hydrogen.

[0003] Hydrogen energy is gaining more and more attention, and hydrogen storage technology has also developed rapidly, which has also driven the progress of distributed power stations. The two major limitations of the development of distributed new energy are energy consumption and power fluctuations. Hydrogen energy, as a new type of energy storage, combined with wind power and photovoltaics will effectively improve the impact of large-scale wind power and photovoltaic volatility and randomness on the power grid. As a controllable load, the electrolyzer can cooperate with the power grid to jointly control the restrictions on large-scale wind power and photovoltaic power generation connected to the grid, which can effectively alleviate the problems of wind and solar power abandonment and improve the quality of wind and solar power grid-connected electricity.

[0004] Adding hydrogen storage to existing distributed power generation can effectively alleviate the contradiction between the excess wind and photovoltaic power generation and the fragile grid structure. Producing hydrogen from wind and photovoltaic power generation and supplementing the grid through fuel cells and other means is more economical and feasible than adjusting the imbalance factors of grid power.

[0005] The wind-hydrogen combined system includes clean wind power, water electrolysis hydrogen production equipment, pressure hydrogen storage equipment, green hydrogen fuel cells, and hydrogen transportation and application. In the absence of prior knowledge, the existing green hydrogen storage capacity configuration method cannot balance the economy, safety and wind power utilization rate well. Summary of the invention

[0006] In order to solve the problem of how to balance the economy, safety and wind power utilization rate in the configuration of green hydrogen energy storage capacity, the present invention provides a green hydrogen energy storage capacity configuration method based on a knee-region multi-objective optimization algorithm.

[0007] A green hydrogen energy storage capacity configuration method based on a knee-region multi-objective optimization algorithm of the present invention comprises:

[0008] S1. A multi-objective optimization model for energy storage capacity configuration is constructed with the goal of minimizing the annual investment cost and annual operating cost of green hydrogen energy storage equipment and minimizing the annual wind abandonment penalty cost of each wind farm;

[0009] S2. Establish operating constraints based on the characteristics of each subsystem in the green hydrogen energy storage system;

[0010] S3. Under the operating constraints, a multi-objective optimization algorithm based on the knee region is used to solve the optimal solution in the multi-objective optimization model.

[0011] As a preferred embodiment, the multi-objective optimization model is:

[0012] The sum of the annual investment cost and annual operating cost of green hydrogen energy storage equipment for:

[0013]

[0014] Where: are the annual investment cost and annual operating cost of green hydrogen energy storage equipment, ε k , Φ are the scenes and scene sets generated by GAN respectively;

[0015] Annual wind curtailment penalty cost for each wind farm:

[0016]

[0017] Where: is the annual wind curtailment penalty cost of wind farm w, w = 1, ..., W, W is the number of wind farms; is the power that can be generated by wind farm w at time t; P w,t is the grid-connected power of wind farm w at time t; ω is the penalty price for wind abandonment.

[0018] As a preferred option, the annualized investment cost of green hydrogen energy storage equipment is:

[0019]

[0020] Among them, the investment cost of the electrolyzer

[0021] Investment cost of gas tank

[0022] Investment cost of hydrogen-fired unit

[0023]

[0024] π ET are the power of the electrolyzer and the unit power investment price respectively;

[0025] π H2 are the volume of hydrogen storage tank and the price per unit volume respectively;

[0026] π O2 They are the volume of oxygen storage tank and price per unit volume;

[0027] π HF are the power of hydrogen-fired units and the investment price per unit power respectively;

[0028] n ET 、n H2 、n O2 、n HF They are the numbers of the electrolyzer, hydrogen storage tank, oxygen storage tank and hydrogen-fired unit to be installed in the corresponding wind farm;

[0029] N ET 、N H2 、N O2 、N HF They are the number of electrolyzers, hydrogen storage tanks, oxygen storage tanks and hydrogen-fired units planned to be invested in;

[0030] δ is the discounted value;

[0031] η ET , η O2 , η H2 , η HF It is the annualized conversion factor for electrolyzers, hydrogen storage tanks, oxygen storage tanks and hydrogen-fired units;

[0032] t ET ,t O2 ,t H2 ,t HF They are the estimated service lives of the planned investment electrolyzer, hydrogen storage tank, oxygen storage tank and hydrogen-fired unit respectively.

[0033] As a preferred option, the annual operating cost of green hydrogen energy storage equipment for:

[0034]

[0035] Among them, the annual operating cost of the electrolyzer is

[0036] Annual operating cost of gas tank

[0037] Annual operating cost of hydrogen-fired unit

[0038] They are the annual operating costs per unit power / volume for the electrolyzer, oxygen storage tank, hydrogen storage tank and hydrogen-fired unit;

[0039] d is the number of typical days.

[0040] Preferably, the operation constraints include load balance constraints, green hydrogen production constraints, green hydrogen combustion constraints and green hydrogen storage state constraints;

[0041] Preferably, the load balancing constraint is:

[0042]

[0043] Among them, P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively;

[0044] P g,t , P w,t They are hydrogen-fired unit n at time t. HF , the output of conventional thermal power units g and wind farm w;

[0045] w and W are the number and quantity of wind farms, respectively;

[0046] g and G are the number and quantity of conventional thermal power groups respectively.

[0047] Preferably, green hydrogen production is constrained to:

[0048]

[0049] n ET ∈w

[0050] in, They are the electrolyzer n in the hydrogen production process ET The hydrogen production and input power;

[0051] The wind farm w at time t has an effect on the electrolytic cell n ET The supply power;

[0052] μ ET ,φ ET They are the hydrogen production conversion rate and electrolyzer efficiency of the electrolyzer respectively.

[0053] Preferably, the green hydrogen combustion constraints are:

[0054]

[0055] in, They are respectively the electrolyzer n for hydrogen combustion process ET The hydrogen production and input power;

[0056] γ and ν are the hydrogen consumption per unit of power generation and the power generation efficiency of the hydrogen-fired unit, respectively;

[0057] Indicates hydrogen-fired unit n HF The corresponding electrolytic cell {n ET,1 …n ET,k}.

[0058] Preferably, the green hydrogen storage state constraints include:

[0059] The status of the hydrogen storage tank for green hydrogen energy storage is:

[0060]

[0061] Where, κ is the storage efficiency of the hydrogen tank, is the amount of hydrogen in the hydrogen storage tank at time t, for t-Δt The amount of hydrogen electrolyzed in the electrolyzer at the moment; is the amount of hydrogen electrolyzed by the electrolyzer at the time interval of Δt; γ and ν are the hydrogen consumption per unit power generation and the power generation efficiency of the hydrogen-fired unit, respectively;

[0062] The status of the oxygen tank is:

[0063]

[0064] in, is the amount of oxygen in the gas tank at time t; is the amount of electrolyzed oxygen in the electrolytic cell at a time interval of Δt; The amount of oxygen delivered for the time interval Δt; is the amount of electrolyzed oxygen in the electrolytic cell at time t-Δt;

[0065] Hydrogen and oxygen are also subject to the volume of the gas tank:

[0066]

[0067] in, They are the upper and lower limits of the hydrogen volume that can be stored in the hydrogen tank;

[0068] They are respectively the upper and lower limits of the volume of oxygen that can be stored in an oxygen tank.

[0069] Preferably, S3 includes:

[0070] S31, initializing the population;

[0071] S32, input wind power scenario data and load data, and use the hypervolume HV index to obtain the approximate virtual Pareto frontier PF of the annualized investment cost and annual operating cost of green hydrogen energy storage equipment and the annual wind abandonment penalty cost of each wind farm in combination with the input wind power scenario data and load data;

[0072] S33, calculate the extreme point and ideal point z according to the virtual Pareto frontier PF I The annual investment cost and annual operating cost of green hydrogen energy storage equipment and the knee point z of the annual wind abandonment penalty cost of each wind farm are normalized and calculated. * ;

[0073] S34, set the knee radius r, determine the knee area Θ (z * ,r) i

[0074] S35, starting the iterative evolution part for the current population, including operations such as selection, crossover, mutation, etc., and merging the population;

[0075] S36, using fast dominance sorting to select all levels of dominance layers to enter the next generation population;

[0076] S37, judging whether the current iteration has converged, if it has, outputting the result corresponding to the knee area, if not, going to S32.

[0077] The beneficial effects of the present invention are as follows: the present invention adjusts the proportion of wind power grid-connected to hydrogen production through a control system, absorbs wind power abandonment to the maximum extent, alleviates the "bottleneck" problem of large-scale wind power grid-connected, utilizes abandoned wind power to electrolyze water to produce hydrogen and secondary oxygen, and simultaneously increases the storage density of hydrogen through pressure hydrogen storage. The knee region represents a good balance in the optimization objectives in the multi-objective optimization theory, can guide the algorithm to converge in the absence of prior knowledge, and is conducive to finding a solution with good performance in economy, safety and wind power utilization in a multi-system coupled wind-hydrogen joint system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A schematic diagram of a flow chart of a capacity configuration method of the present invention;

[0079] Figure 2 This is a typical scenario obtained after scenario reduction;

[0080] Figure 3 This is the distribution diagram of the frequency regulation reserve capacity in each frequency band of different wind farms. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0083] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0084] like Figure 1 As shown, this embodiment provides a green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm, which includes three steps:

[0085] Step 1: A multi-objective optimization model for energy storage capacity configuration is constructed with the goal of minimizing the annual investment and annual operating cost of green hydrogen energy storage equipment and the annual wind abandonment penalty cost of each wind farm;

[0086] Step 2: Establish operating constraints based on the characteristics of each subsystem in the green hydrogen energy storage system;

[0087] Step 3 uses a knee-region-based multi-objective optimization algorithm to solve the optimal solution of the multi-objective optimization model for green hydrogen energy storage capacity configuration;

[0088] With the goals of minimizing the annual investment and annual operating cost of green hydrogen energy storage equipment and minimizing the annual wind abandonment penalty cost of each wind farm, a multi-objective optimization model for energy storage capacity configuration is constructed as follows:

[0089] Annual investment and annual operating costs of green hydrogen energy storage equipment as follows:

[0090]

[0091] Where: They are the annual investment cost and annual operating cost of green hydrogen energy storage equipment respectively. k , Φ are the scenarios and scenario sets generated by GAN respectively. Another type of objective function, namely the annual wind abandonment penalty cost of each wind farm, is expressed as follows:

[0092]

[0093] Where: is the annual wind curtailment penalty cost of wind farm w; is the power that can be generated by wind farm w at time t; P w,tis the grid-connected power of wind farm w at time t; ω is the penalty price for wind abandonment.

[0094] The first term in formula (1) can also be specifically expressed as follows:

[0095]

[0096] Annual operating costs of green hydrogen energy storage It is expressed as follows:

[0097]

[0098]

[0099] Where: are the annual operating costs of the electrolyzer, gas storage tank and hydrogen-fired unit respectively; are the annual operating costs per unit power / volume of the electrolyzer, oxygen storage tank, hydrogen storage tank and hydrogen-fired unit respectively; d is the number of typical days.

[0100] (1) In the hydrogen energy system, load balance constraints:

[0101]

[0102] Where: P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively; P g,t , P w,t They are the hydrogen-fired unit n at time t. HF , the output of conventional thermal power unit g and wind farm w; w and W are the number and group of wind farms respectively; g and G are the number and group of conventional thermal power respectively.

[0103] (2) Constraints on green hydrogen production

[0104] There is energy loss in the hydrogen production process, which can be expressed as the following constraints:

[0105]

[0106] Where: Electrolytic cell n ET Hydrogen production and input power; μ ET ,φ ET are the hydrogen production conversion rate and efficiency of the electrolyzer, respectively. The green hydrogen manufacturing process must satisfy that the energy supply comes from renewable energy. Therefore, on the basis of satisfying the load balance formula (12) of the entire system, the power supply of the electrolyzer must meet the following constraints:

[0107]

[0108] Where: The wind farm w at time t has an effect on the electrolytic cell n ET The supply power.

[0109] (3) Green hydrogen combustion constraints

[0110] The hydrogen combustion process also has energy loss and needs to meet the following constraints:

[0111]

[0112] In the formula: γ and ν are the hydrogen consumption per unit power generation and the power generation efficiency of the hydrogen-fired unit respectively; Indicates hydrogen-fired unit n HF The corresponding electrolytic cell {n ET,1 …n ET,k}.

[0113] (4) Green hydrogen storage status constraints

[0114] The status of the hydrogen storage tank for green hydrogen energy storage can be expressed as follows:

[0115]

[0116] Where: κ is the storage efficiency of the hydrogen tank. is the amount of hydrogen in the hydrogen storage tank at time t; is the amount of hydrogen electrolyzed by the electrolyzer at the time interval Δt. The state of the oxygen storage tank can be expressed as follows:

[0117]

[0118] Where: is the amount of oxygen in the gas tank at time t; is the amount of electrolyzed oxygen in the electrolytic cell at a time interval of Δt; is the amount of oxygen delivered in the Δt time interval. Hydrogen and oxygen are also affected by the volume of the gas tank and must meet the following constraints:

[0119]

[0120] Where: They are the upper and lower limits of the hydrogen volume that can be stored in the hydrogen tank; They are respectively the upper and lower limits of the volume of oxygen that can be stored in an oxygen tank.

[0121] The problem is optimized using a multi-objective optimization algorithm based on the knee region.

[0122] Definition of Chebyshev distance (CD): Assume that there are two vectors A and B, both of which have m dimensions, then the CD between them can be represented by the infinite norm. The specific representation is as follows:

[0123]

[0124] Knee point: Assume that the ideal point on the Pareto front (PF) is z I , the performance of the solution vector in the normalized objective function is different. If the performance can be minimized and the maximum value of the solution vector can be improved, it can be defined as the knee point based on CD, denoted as z * The compromise of the knee point is as follows: compared to the ideal point z I , z * The change in performance after normalization of the objective function does not favor any objective and can be expressed in the following mathematical form:

[0125]

[0126] Where: L i is the maximum difference of each component of solution vector i; f i are the upper and lower limits of the target value of the vector respectively. are normalized vector points respectively.

[0127] Definition of knee region based on CD: For example, a circle is used to represent the knee region Θ(z * ), then the area can be expressed as follows:

[0128]

[0129] Where: r is the radius of the knee area.

[0130] The specific steps of designing a multi-objective optimization algorithm based on the knee area are as follows:

[0131] (1) Initialize the population;

[0132] (2) Input wind power scenario data and load data, and use the hypervolume HV index to approximate the annual investment cost and annual operating cost of green hydrogen energy storage equipment and the annual wind abandonment penalty cost of each wind farm;

[0133] (3) Calculate the extreme point and ideal point z based on the virtual Pareto frontier PF IThe annual investment cost and annual operating cost of green hydrogen energy storage equipment and the knee point z of the annual wind abandonment penalty cost of each wind farm are normalized and calculated. * ;

[0134] (4) Set the knee radius r and determine the knee area Θ(z * ,r) i

[0135] (5) Start the iterative evolution part of the current population, including operations such as selection, crossover, and mutation, and merge the population;

[0136] (6) Use fast dominance sorting to select all levels of dominance to enter the next generation population;

[0137] (7) Determine whether the termination condition is met. If so, output the result corresponding to the knee area. If not, go to (2) and continue iterating to solve.

[0138] Figure 2 Flowchart of the knee region-based multi-objective optimization algorithm. Figure 3 Schematic diagram of the generated Pareto front.

[0139] In summary, the beneficial effects of the present invention are as follows: the wind-hydrogen combined system includes clean wind power, water electrolysis hydrogen production equipment, pressure hydrogen storage equipment, green hydrogen fuel cells, and hydrogen transportation and application, etc. The control system adjusts the ratio of wind power access to the grid and hydrogen production electricity to maximize the absorption of wind power abandonment, alleviate the "bottleneck" problem of large-scale wind power access to the grid, and utilizes the abandoned wind power to electrolyze water to produce hydrogen and secondary oxygen, while increasing the storage density of hydrogen through pressure hydrogen storage. In the multi-objective optimization theory, the knee region represents that the solution in this region has a good balance in the optimization objectives, and can guide the algorithm to converge in the absence of prior knowledge, which is conducive to finding a solution with good performance in economy, safety and wind power utilization in the wind-hydrogen combined system with multi-system coupling.

[0140] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A green hydrogen energy storage capacity configuration method based on a knee-region multi-objective optimization algorithm, characterized in that: The method comprises: S1. A multi-objective optimization model for energy storage capacity configuration is constructed with the goal of minimizing the annual investment cost and annual operating cost of green hydrogen energy storage equipment and minimizing the annual wind abandonment penalty cost of each wind farm; S2. Establish operating constraints based on the characteristics of each subsystem in the green hydrogen energy storage system; S3. Under the operating constraints, a multi-objective optimization algorithm based on the knee region is used to solve the optimal solution in the multi-objective optimization model.

2. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 1 is characterized in that: The multi-objective optimization model is: The sum of the annual investment cost and annual operating cost of green hydrogen energy storage equipment for: Where: are the annual investment cost and annual operating cost of green hydrogen energy storage equipment, ε k , Φ are the scenes and scene sets generated by GAN respectively; Annual wind curtailment penalty cost for each wind farm: Where: is the annual wind curtailment penalty cost of wind farm w, w = 1, ..., W, W is the number of wind farms; is the power that can be generated by wind farm w at time t; P w,t is the grid-connected power of wind farm w at time t; ω is the penalty price for wind abandonment.

3. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 1 is characterized in that: The annualized investment cost of green hydrogen energy storage equipment is: Among them, the investment cost of the electrolyzer Investment cost of gas tank Investment cost of hydrogen-fired unit π ET are the power of the electrolyzer and the unit power investment price respectively; π H2 are the volume of hydrogen storage tank and the price per unit volume respectively; π O2 They are the volume of oxygen storage tank and price per unit volume; π HF are the power of hydrogen-fired units and the investment price per unit power respectively; n ET 、n H2 、n O2 、n HF They are the numbers of the electrolyzer, hydrogen storage tank, oxygen storage tank and hydrogen-fired unit to be installed in the corresponding wind farm; N ET 、N H2 、N O2 、N HF They are the number of electrolyzers, hydrogen storage tanks, oxygen storage tanks and hydrogen-fired units planned to be invested in; δ is the discounted value; η ET , η O2 , η H2 , η HF It is the annualized conversion factor for electrolyzers, hydrogen storage tanks, oxygen storage tanks and hydrogen-fired units; t ET ,t O2 ,t H2 ,t HF They are the estimated service lives of the planned investment electrolyzer, hydrogen storage tank, oxygen storage tank and hydrogen-fired unit respectively.

4. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 2 is characterized in that: Annual operating cost of green hydrogen energy storage equipment for: Among them, the annual operating cost of the electrolyzer is Annual operating cost of gas tank Annual operating cost of hydrogen-fired unit They are the annual operating costs per unit power / volume for the electrolyzer, oxygen storage tank, hydrogen storage tank and hydrogen-fired unit; d is the number of typical days.

5. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 1 is characterized in that: The operating constraints include load balance constraints, green hydrogen production constraints, green hydrogen combustion constraints and green hydrogen storage status constraints.

6. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 5 is characterized in that: The load balancing constraints are: Among them, P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively; P g,t , P w,t They are hydrogen-fired unit n at time t. HF , the output of conventional thermal power units g and wind farm w; w and W are the number and quantity of wind farms, respectively; g and G are the number and quantity of conventional thermal power groups respectively.

7. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 5 is characterized in that: Green hydrogen production constraints are: in, They are the electrolyzer n in the hydrogen production process ET The hydrogen production and input power; The wind farm w at time t has an effect on the electrolytic cell n ET The supply power; μ ET ,φ ET They are the hydrogen production conversion rate and electrolyzer efficiency of the electrolyzer respectively.

8. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 5 is characterized in that: Green hydrogen combustion constraints are: in, They are respectively the electrolyzer n for hydrogen combustion process ET The hydrogen production and input power; γ and ν are the hydrogen consumption per unit of power generation and the power generation efficiency of the hydrogen-fired unit, respectively; Indicates hydrogen-fired unit n HF The corresponding electrolytic cell {n ET,1 …n ET,k }.

9. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 5 is characterized in that: Green hydrogen storage state constraints include: The status of the hydrogen storage tank for green hydrogen energy storage is: Where, κ is the storage efficiency of the hydrogen tank, is the amount of hydrogen in the hydrogen storage tank at time t, is the amount of electrolyzed hydrogen in the electrolyzer at time t-Δt; for Δt The amount of electrolytic hydrogen in the electrolyzer at each time interval; γ and ν are the hydrogen consumption per unit of power generation and the power generation efficiency of the hydrogen-fired unit, respectively; The status of the oxygen tank is: in, is the amount of oxygen in the gas tank at time t; is the amount of electrolyzed oxygen in the electrolytic cell at a time interval of Δt; The amount of oxygen delivered for the time interval Δt; is the amount of electrolyzed oxygen in the electrolytic cell at time t-Δt; Hydrogen and oxygen are also subject to the volume of the gas tank: in, They are the upper and lower limits of the hydrogen volume that can be stored in the hydrogen tank; They are respectively the upper and lower limits of the volume of oxygen that can be stored in an oxygen tank.

10. The green hydrogen energy storage capacity configuration method based on the knee area multi-objective optimization algorithm according to claim 1 is characterized in that: The S3 includes: S31, initializing the population; S32, input wind power scenario data and load data, and use the hypervolume HV index to obtain the approximate virtual Pareto frontier PF of the annualized investment cost and annual operating cost of green hydrogen energy storage equipment and the annual wind abandonment penalty cost of each wind farm in combination with the input wind power scenario data and load data; S33, calculate the extreme point and ideal point z according to the virtual Pareto frontier PF I The annual investment cost and annual operating cost of green hydrogen energy storage equipment and the knee point z of the annual wind abandonment penalty cost of each wind farm are normalized and calculated. * ; S34, set the knee radius r, determine the knee area Θ (z * ,r) i S35, starting the iterative evolution part for the current population, including operations such as selection, crossover, mutation, etc., and merging the population; S36, using fast dominance sorting to select all levels of dominance layers to enter the next generation population; S37, judging whether the current iteration has converged, if it has, outputting the result corresponding to the knee area, if not, going to S32.

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