New energy station energy storage configuration method and system considering energy storage life

Abstract

The invention discloses a new energy station energy storage configuration method and system considering energy storage life, and the method comprises the steps: building an energy storage economic evaluation framework through obtaining the performance parameters of an energy storage system; based on an energy storage economic evaluation framework, constructing a target function with maximum benefit as a target; solving the target function by using an improved grey wolf algorithm, and judging whether an iteration termination condition is met or not to obtain an energy storage optimal configuration scheme; according to the invention, through energy storage configuration, energy storage full life cycle income maximization is realized, and a new guidance method is provided for energy storage configuration of a new energy station.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis and control, and in particular to a new energy station energy storage configuration method and system taking energy storage life into consideration. Background Art

[0002] Excessive collection of traditional fossil fuels poses new challenges to energy utilization. Renewable clean energy generation, mainly wind power and solar power, has become a hot topic for many scholars and a key step in building a new power system. However, the volatility and uncertainty of renewable energy generation bring new technical challenges to the safe operation of the power grid. When large-scale renewable energy is connected to the power grid, it causes system interference to the voltage, frequency and power control of the power grid. As a grid-connected carrier for energy such as wind power and photovoltaics, new energy stations provide an important support platform for the consumption of new energy.

[0003] As a medium for power regulation in the power system, energy storage technology can effectively promote the consumption of new energy, provide new support for the normal operation of the power grid and reduce energy waste. Reasonable configuration of energy storage is the key to achieving the economic benefits of new energy power stations. Excessive configuration of energy storage can easily increase costs and cause waste of resources; insufficient energy storage configuration is not conducive to the consumption of new energy and affects the normal operation of the power grid. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a new energy station energy storage configuration method and system taking into account the energy storage life to solve the problem of unreasonable energy storage configuration of existing new energy power stations.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for configuring energy storage at a new energy station taking into account energy storage life, comprising:

[0009] Establish an energy storage economic evaluation framework by obtaining the performance parameters of the energy storage system;

[0010] Based on the energy storage economic evaluation framework, an objective function is constructed to maximize the benefits;

[0011] The improved grey wolf algorithm is used to solve the objective function, determine whether the iteration termination conditions are met, and obtain the optimal energy storage configuration plan.

[0012] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0013] The energy storage economic evaluation framework includes energy storage benefit evaluation and cost evaluation;

[0014] The energy storage benefit assessment includes an energy storage operation benefit model, a policy subsidy benefit model and a carbon trading benefit model;

[0015] The cost assessment includes an investment cost model, an operation and maintenance cost model, and a penalty cost model;

[0016] The energy storage operation revenue model, policy subsidy revenue model and carbon trading revenue model are respectively expressed as:

[0017]

[0018] Among them, f cx is the energy storage operation profit model, f zc is the policy subsidy benefit model, f tj is the carbon trading revenue model, r t,d and r t,c are the energy storage discharge price and charging price in period t, respectively, t,d and P t,c are the energy storage discharge power and charging power respectively; Δt is the time interval, T is the scheduling period, r t,b is the subsidy price for the tth period of a day, ΔP t is the system surplus power, P t,w , P t,v , P t,g and P t,l are wind power output, photovoltaic output, motor output and load demand power in period t, respectively, t,t is the carbon trading price in period t, Q j,t,z To reduce the amount of electricity purchased from the grid.

[0019] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0020] The investment cost model, operation and maintenance cost model and penalty cost model are respectively expressed as:

[0021]

[0022] C cf =C cfg Q cfg +C cfqQ cfq

[0023] Among them, C tz is the investment cost model, C yw is the operation and maintenance cost model, C cf is the penalty cost model, C tr and Q n are the unit capacity cost and rated capacity of energy storage, E tr is the discount rate, n is the energy storage operation life, C t,ywd and Q t,ywn are the unit capacity operation and maintenance cost and operation and maintenance capacity of energy storage, C cfg and Q cfg are the unit electricity purchase cost and the amount of electricity purchased from the power grid, C cfq and Q cfq They are the unit cost of wind and solar power curtailment and the total amount of wind and solar power curtailment respectively.

[0024] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0025] The energy storage economic evaluation framework includes corresponding constraints of the established model; the corresponding constraints include power balance constraints, wind power and photovoltaic output constraints, unit output constraints, and energy storage charge state constraints;

[0026] The power balance constraint is expressed as:

[0027] P t,w +P t,v +P t,g =P t,l +λ c P t,c -λ d P t,d

[0028] Among them, λ c and λ d They are 0-1 variables for energy storage charging and discharging;

[0029] The wind power and photovoltaic output constraints are expressed as:

[0030]

[0031] Among them, P t,wmin and P t,wmax are the lower and upper limits of wind power output in period t, P t,vmin and P t,vmax They are the lower and upper limits of photovoltaic output respectively;

[0032] The unit output constraint is expressed as:

[0033] Pt,gmin ≤P i,g ≤P t,gmax

[0034] Among them, P t,gmin and P t,gmax They are the lower and upper limits of the motor output during period t respectively;

[0035] The energy storage state of charge constraint is expressed as:

[0036]

[0037] Among them, S t is the energy state of energy storage during period t in a year, η s is the energy storage loss coefficient, η c and η d are the charging efficiency and discharging efficiency respectively, S t,min and S t,max They are the lower and upper limits of the energy storage state respectively.

[0038] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0039] The objective function of constructing the maximum benefit is expressed as:

[0040]

[0041] Among them, E1 is the expected rate of return and n is the operating life of energy storage.

[0042] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0043] The improved grey wolf algorithm is used to solve the objective function as follows:

[0044]

[0045] Among them, x(t+1) is the current position of the gray wolf, X p (t) is the prey position, a is the convergence factor, which takes the value [0, 2], r1 and r2 are random vectors between [0, 1], t is the current iteration number, T is the maximum iteration number, a min and a max are the initial and final values ​​of a respectively; k is the adjustment factor, taking the value [1, 5].

[0046] As a preferred solution of the energy storage configuration method of a new energy station considering the energy storage life described in the present invention, wherein:

[0047] The step of determining whether the iteration termination condition is met comprises the following steps:

[0048] If t > T, then end the operation;

[0049] If t < T, then perform the next iteration until the set iteration value is reached, and then end the operation.

[0050] In a second aspect, the present invention provides a new energy power station energy storage configuration system considering the energy storage life, including:

[0051] An energy storage system performance evaluation module, which is used to establish an energy storage economic evaluation framework by obtaining the performance parameters of the energy storage system;

[0052] A target function construction module, which is used to construct a target function aiming at maximizing the benefit based on the energy storage economic evaluation framework;

[0053] An algorithm solving module, which is used to solve the target function by using an improved grey wolf algorithm, judge whether the iteration termination condition is satisfied, and obtain the optimal energy storage configuration scheme.

[0054] In a third aspect, the present invention provides a computing device, including:

[0055] A memory, which is used to store programs;

[0056] A processor, which is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the new energy power station energy storage configuration method considering the energy storage life are realized.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium, including: when the program is executed by the processor, the steps of the new energy power station energy storage configuration method considering the energy storage life are realized.

[0058] The beneficial effects of the present invention: By establishing an energy storage benefit model and a cost model, constructing a target function including the energy storage benefit model, the cost model and the corresponding constraint conditions, and using an improved grey wolf algorithm to solve the target function, the optimal energy storage configuration scheme is obtained. By configuring energy storage for the new energy power station, the power fluctuation of the power grid is effectively alleviated, the consumption of new energy is promoted, the reliability of the power grid is improved, and at the same time, the maximum benefit is realized during the whole life process of the energy storage, providing a new guiding method for the energy storage configuration of the new energy power station. Description of the Drawings

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts. Among them:

[0060] Figure 1 A basic flow chart of a method for configuring energy storage at a new energy station taking energy storage life into consideration provided by an embodiment of the present invention;

[0061] Figure 2 A flow model diagram of a new energy station energy storage configuration method considering energy storage life provided by an embodiment of the present invention;

[0062] Figure 3 A comparison diagram of wind and solar power abandonment before and after optimization of a new energy station energy storage configuration method considering energy storage life provided by an embodiment of the present invention;

[0063] Figure 4 An energy storage net benefit diagram of a new energy station energy storage configuration method taking energy storage life into consideration is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0068] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0069] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] Example 1

[0071] Reference Figure 1-2 , which is an embodiment of the present invention, provides a new energy station energy storage configuration method considering energy storage life, including:

[0072] S1: Establish an energy storage economic evaluation framework by obtaining the performance parameters of the energy storage system;

[0073] In the embodiment of the present application, by obtaining the performance parameters of the energy storage system (such as charging and discharging efficiency, maximum charging and discharging power, capacity, etc.), as well as relevant information such as market prices, policy subsidies, carbon trading, etc., an energy storage economic evaluation framework is established to output a complete energy storage benefit and cost model.

[0074] In the embodiment of the present application, the energy storage economic evaluation framework includes energy storage benefit evaluation and cost evaluation;

[0075] In the embodiment of the present application, the energy storage revenue assessment includes an energy storage operation revenue model, a policy subsidy revenue model, and a carbon trading revenue model;

[0076] In the embodiment of the present application, the cost assessment includes an investment cost model, an operation and maintenance cost model, and a penalty cost model;

[0077] In the embodiment of the present application, the energy storage operation revenue model, the policy subsidy revenue model and the carbon trading revenue model are respectively expressed as:

[0078]

[0079] Among them, f cx is the energy storage operation profit model, f zc is the policy subsidy benefit model, f tjis the carbon trading revenue model, r t,d and r t,c are the energy storage discharge price and charging price in period t, respectively, t,d and P t,c are the energy storage discharge power and charging power respectively; Δt is the time interval, T is the scheduling period, r t,b is the subsidy price for the tth period of a day, ΔP t is the system surplus power, P t,w , P t,v , P t,g and P t,l are wind power output, photovoltaic output, motor output and load demand power in period t, respectively, t,t is the carbon trading price in period t, Q j,t,z To reduce the amount of electricity purchased from the grid.

[0080] In the embodiment of the present application, the investment cost model, the operation and maintenance cost model and the penalty cost model are respectively expressed as:

[0081]

[0082] C cf =C cfg Q cfg +C cfq Q cfq

[0083] Among them, C tz is the investment cost model, C yw is the operation and maintenance cost model, C cf is the penalty cost model, C tr and Q n are the unit capacity cost and rated capacity of energy storage, E tr is the discount rate, n is the energy storage operation life, C t,ywd and Q t,ywn are the unit capacity operation and maintenance cost and operation and maintenance capacity of energy storage, C cfg and Q cfg are the unit electricity purchase cost and the amount of electricity purchased from the grid, C cfq and Q cfq They are the unit cost of wind and solar power curtailment and the total amount of wind and solar power curtailment respectively.

[0084] In the embodiment of the present application, the energy storage economic evaluation framework includes corresponding constraints of the established model; the corresponding constraints include power balance constraints, wind power and photovoltaic output constraints, unit output constraints, and energy storage charge state constraints;

[0085] In the embodiment of the present application, the power balance constraint is expressed as:

[0086] P t,w +Pt,v +P t,g =P t,l +λ c P t,c -λ d P t,d

[0087] Among them, λ c and λ d They are 0-1 variables for energy storage charging and discharging;

[0088] In the embodiment of the present application, the wind power and photovoltaic output constraints are expressed as:

[0089]

[0090] Among them, P t,wmin and P t,wmax are the lower and upper limits of wind power output in period t, respectively, t,vmin and P t,vmax They are the lower and upper limits of photovoltaic output respectively;

[0091] In the embodiment of the present application, the unit output constraint is expressed as:

[0092] P t,gmin ≤P t,g ≤P t,gmax

[0093] Among them, P t,gmin and P t,gmax They are the lower and upper limits of the motor output during period t respectively;

[0094] In the embodiment of the present application, the energy storage charge state constraint is expressed as:

[0095]

[0096] Among them, S t is the energy state of energy storage during period t in a year, η s is the energy storage loss coefficient, η c and η d are the charging efficiency and discharging efficiency respectively, S t,min and S t,max They are the lower and upper limits of the energy storage state respectively.

[0097] S2: Based on the energy storage economic evaluation framework, an objective function is constructed to maximize the benefits;

[0098] In the embodiment of the present application, based on the energy storage economic evaluation framework, the energy storage operation income, policy subsidy income, carbon trading income, investment cost, operation and maintenance cost and penalty cost are comprehensively considered to construct an objective function with the goal of maximizing the income, and output the objective function expression to be optimized.

[0099] In the embodiment of the present application, the objective function constructed with the goal of maximizing the benefit is expressed as:

[0100]

[0101] Among them, E1 is the expected rate of return, and n is the operation years of the energy storage.

[0102] S3: Use the improved grey wolf algorithm to solve the objective function, judge whether the iteration termination condition is satisfied, and obtain the optimal energy storage configuration plan.

[0103] In the embodiment of the present application, use the improved grey wolf algorithm to solve the constructed objective function, judge whether the termination condition is satisfied through iterative calculation, finally obtain the optimal configuration plan of the energy storage system, and output the optimal configuration plan of the energy storage system, including the best energy storage capacity, charge and discharge strategy, etc.

[0104] In the embodiment of the present application, using the improved grey wolf algorithm to solve the objective function is expressed as:

[0105]

[0106] Among them, x(t + 1) is the current grey wolf position, X p (t) is the prey position, a is the convergence factor, with a value range of [0, 2], r1 and r2 are random vectors between [0, 1], t is the current iteration number, T is the maximum iteration number, a min and a max are respectively the initial value and the final value of a, and k is the adjustment factor, with a value range of [1, 5].

[0107] In the embodiment of the present application, judging whether the iteration termination condition is satisfied includes the following steps:

[0108] If t > T, then end the operation;

[0109] If t < T, then perform the next iteration until the set iteration value is reached, and then end the operation.

[0110] This embodiment also provides a new energy power station energy storage configuration system considering the energy storage life, including:

[0111] An energy storage system performance evaluation module, which is used to establish an energy storage economic evaluation framework by obtaining the performance parameters of the energy storage system;

[0112] An objective function construction module, which is used to construct an objective function with the goal of maximizing the benefit based on the energy storage economic evaluation framework;

[0113] An algorithm solving module, which is used to use the improved grey wolf algorithm to solve the objective function, judge whether the iteration termination condition is satisfied, and obtain the optimal energy storage configuration plan.

[0114] Furthermore, it also includes:

[0115] Memory, used to store programs;

[0116] A processor is used to load the program to execute the new energy station energy storage configuration method considering the energy storage life.

[0117] This embodiment also provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the method for configuring energy storage in a new energy station taking into account the energy storage life.

[0118] The storage medium proposed in this embodiment and the new energy station energy storage configuration method considering the energy storage life proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0119] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0120] Example 2

[0121] Reference Figure 3-4 , which is an embodiment of the present invention, provides a new energy station energy storage configuration method taking into account the energy storage life. In order to verify its beneficial effects, it is described below with reference to specific examples.

[0122] In order to verify the effectiveness of the method proposed in the present invention, in the simulation process, the energy storage charging power is configured to be 10MW, the charging and discharging efficiency is 0.95, the initial energy storage energy state is 0.5, and the expected rate of return is 8.5%;

[0123] Using the method proposed in the present invention, the comparison of wind and light power abandonment before and after optimization is obtained. Figure 2 As shown, it can be seen that after the optimization method proposed in the present invention, the phenomenon of wind and solar power abandonment in the system is significantly improved;

[0124] Figure 3 This is the net benefit chart of energy storage. It can be seen that after the energy storage is configured, the benefits are negative in the first five years. From the sixth year, the benefits turn positive. The net benefit reaches the maximum in the tenth year, and the operating life of the energy storage is 14 years.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for configuring energy storage at a new energy station taking into account energy storage life, characterized in that: Including: By obtaining the performance parameters of the energy storage system, an energy storage economic evaluation framework is established; Based on the energy storage economic evaluation framework, an objective function aiming at maximizing the revenue is constructed; The improved grey wolf algorithm is used to solve the objective function, and it is judged whether the iteration termination condition is satisfied to obtain the optimal energy storage configuration scheme.

2. The method for configuring energy storage in a new energy station taking energy storage life into consideration according to claim 1, characterized in that: The energy storage economic evaluation framework includes energy storage revenue evaluation and cost evaluation; The energy storage revenue evaluation includes an energy storage operation revenue model, a policy subsidy revenue model, and a carbon trading revenue model; The cost evaluation includes an investment cost model, an operation and maintenance cost model, and a penalty cost model; The energy storage operation revenue model, the policy subsidy revenue model, and the carbon trading revenue model are respectively expressed as: Among them, f cx is the energy storage operation profit model, f zc is the policy subsidy benefit model, f tj is the carbon trading revenue model, r t,d and r t,c are the energy storage discharge price and charging price in period t, respectively, t,d and P t,c are the energy storage discharge power and charging power respectively; Δt is the time interval, T is the scheduling period, r t,b is the subsidy price for the tth period of a day, ΔP t is the system surplus power, P t,w , P t,v , P t,g and P t,l are wind power output, photovoltaic output, motor output and load demand power in period t, respectively, t,t is the carbon trading price in period t, Q j,t,z To reduce the amount of electricity purchased from the grid.

3. The method for configuring energy storage in a new energy station taking into account energy storage life as claimed in claim 2, characterized in that: The investment cost model, the operation and maintenance cost model, and the penalty cost model are respectively expressed as: C cf =C cfg Q cfg +C cfq Q cfq Among them, C tz is the investment cost model, C yw is the operation and maintenance cost model, C cf is the penalty cost model, C tr and Q n are the unit capacity cost and rated capacity of energy storage, E tr is the discount rate, n is the energy storage operation life, C t,ywd and Q t,ywn are the unit capacity operation and maintenance cost and operation and maintenance capacity of energy storage, C cfg and Q cfg are the unit electricity purchase cost and the amount of electricity purchased from the grid, C cfq and Q cfq They are the unit cost of wind and solar power curtailment and the total amount of wind and solar power curtailment respectively.

4. The method for configuring energy storage in a new energy station taking into account energy storage life as claimed in claim 3, characterized in that: The energy storage economic evaluation framework includes the corresponding constraint conditions of the established models; the corresponding constraint conditions include power balance constraint, wind power and photovoltaic output constraint, unit output constraint, and energy storage state of charge constraint; The power balance constraint is expressed as: P t,w +P t,v +P t,g =P t,l +λ c P t,c -λ d P t,d Among them, λ c and λ d They are 0-1 variables for energy storage charging and discharging; The wind power and photovoltaic output constraint is expressed as: Among them, P t,wmin and P t,wmax are the lower and upper limits of wind power output in period t, P t,vmin and P t,vmax They are the lower and upper limits of photovoltaic output respectively; The unit output constraint is expressed as: P t,gmin ≤P t,g ≤P t,gmax Among them, P t,gmin and P t,gmax They are the lower and upper limits of the motor output during period t respectively; The energy storage state of charge constraint is expressed as: Among them, S t is the energy state of energy storage during period t in a year, η s is the energy storage loss coefficient, η c and η d are the charging efficiency and discharging efficiency respectively, S t,min and S t,max They are the lower and upper limits of the energy storage state respectively.

5. The method for configuring energy storage in a new energy station taking energy storage life into consideration according to claim 4, characterized in that: The construction of the objective function aiming at maximizing the revenue is expressed as: Wherein, E1 is the expected rate of return, and n is the operation years of the energy storage.

6. The method for configuring energy storage in a new energy station taking into account energy storage life as claimed in claim 5, characterized in that: The use of the improved grey wolf algorithm to solve the objective function is expressed as: Among them, x(t+1) is the current position of the gray wolf, X p (t) is the prey position, a is the convergence factor, which takes the value [0, 2], r1 and r2 are random vectors between [0, 1], t is the current iteration number, T is the maximum iteration number, a min and a max are the initial and final values ​​of a respectively; k is the adjustment factor, taking the value [1, 5].

7. The method for configuring energy storage in a new energy station taking into account energy storage life as claimed in claim 6, characterized in that: The judgment of whether the iteration termination condition is satisfied includes the following steps: If t>T, the operation is terminated; If t<T, the next iteration is carried out until the set iteration value is reached, and the operation is terminated.

8. A system for the energy storage configuration method of a new energy power station considering the energy storage life according to claim 1, characterized in that: An energy storage system performance evaluation module, which is used to establish an energy storage economic evaluation framework by obtaining the performance parameters of the energy storage system; An objective function construction module, which is used to construct an objective function aiming at maximizing the revenue based on the energy storage economic evaluation framework; An algorithm solving module, which is used to use the improved grey wolf algorithm to solve the objective function, judge whether the iteration termination condition is satisfied, and obtain the optimal energy storage configuration scheme.

9. A computing device, characterized in that Including: A memory for storing programs; A processor for loading the program to execute the steps of the energy storage configuration method of the new energy power station considering the energy storage life according to any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, the steps of the energy storage configuration method of the new energy power station considering the energy storage life according to any one of claims 1-7 are implemented.

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