New energy base energy storage optimal configuration method and system

Abstract

The embodiment of the invention provides a new energy base energy storage optimization configuration method and system, and the method comprises the steps: obtaining the actual power generation output power of a new energy base, and calculating the operation scheduling instruction of the new energy base and the deviation load of the actual power generation output power; based on the deviation load, establishing a target function by taking the maximum net present value of the whole life cycle of the energy storage system as a target, constructing an energy storage optimal configuration model, and setting constraint conditions of the energy storage optimal configuration model; and solving the energy storage optimal configuration model by using a non-dominated solution sorting genetic algorithm to obtain the energy storage optimal configuration capacity of the new energy base. According to the method, the large-scale new energy acceptance capability is improved, the new energy base punishment cost is reduced, the operation scheduling workload is simplified, and the utilization level of new energy resources is improved.

Description

Technical Field

[0001] This document relates to the field of new energy technology, and in particular to a method and system for optimizing energy storage configuration in a new energy base. Background Art

[0002] Under the strategic guidance of "building a new power system with new energy as the main body", while renewable energy power generation is developing in full swing, challenges such as large volatility of new energy power generation, poor power supply reliability and serious wind and solar power abandonment have also arisen. Large-scale new energy grid connection has caused serious grid operation safety problems and increased the difficulty of new energy operation and dispatch. Energy storage technology can solve the problem of renewable energy grid connection and absorption, reduce the investment cost of grid lines, improve power quality and power supply reliability, and obtain economic benefits. Summary of the invention

[0003] One or more embodiments of this specification provide a method for optimizing energy storage configuration in a new energy base, including:

[0004] Obtain the actual power output of the new energy base, and calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output;

[0005] Based on the deviation load, an objective function is established with the goal of maximizing the net present value of the energy storage system over its entire life cycle, an energy storage optimization configuration model is constructed, and constraint conditions of the energy storage optimization configuration model are set;

[0006] The non-dominated solution sorting genetic algorithm is used to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of energy storage in the new energy base.

[0007] Furthermore, the specific method for obtaining the actual power output of the new energy base is as follows:

[0008] The variable pitch wind turbine output characteristic curve is used as the wind turbine output characteristic curve to represent the actual power generation output of the new energy base. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine power generation is 0; when the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the wind turbine output power is obtained by fitting the variable pitch wind turbine curve; when the wind speed reaches the rated wind speed v r Less than cut-out wind speed v co When the fan is at rated power P r Run as follows:

[0009]

[0010] Among them, P w is the actual power output; v ci is the cut-in wind speed; v co is the cut-out wind speed; v r is the rated wind speed; Pr (v) is the fan output power; P r is the rated power of the fan.

[0011] Furthermore, based on the deviation load, an objective function is established with the maximum net present value of the energy storage system over its entire life cycle as the goal, and a specific method for constructing an energy storage optimization configuration model is as follows:

[0012] The net benefit present value (NPV) is obtained based on the energy storage system revenue and the energy storage system construction cost, as follows:

[0013] NPV=IC inv -C op ;

[0014] Among them, C op C is the operation and maintenance cost; inv is the construction cost of the energy storage system; when NPV≥0, it means that the energy storage project is profitable; when NPV≤0, it means that the energy storage project is not profitable; when NPV=0, it is the balance point between profit and loss of the energy storage project, that is, the dynamic payback period of the project.

[0015] Furthermore, the energy storage system revenue and energy storage system construction cost calculation method is as follows:

[0016] The benefits of energy storage systems include the benefits of reducing wind curtailment and the benefits of reducing power shortage penalties, as shown below:

[0017]

[0018]

[0019] Where, α is the power loss coefficient; Q loss (t) is the annual abandoned electricity sequence; r is the discount rate; T LCC is the whole life cycle; β is the power shortage penalty coefficient; Q penalty (t) is the annual power shortage sequence P ESS,C P is the abandoned power; ESS,D For power shortage;

[0020] The construction cost of the energy storage system includes the initial investment cost and operation and maintenance cost, which are as follows:

[0021] C inv =P ESS C ep +C ee E EsS ;

[0022] In the formula, C ep , C ee P is the investment cost per unit power and unit capacity of energy storage; ESS 、EESS Configure decision variables for power and capacity of energy storage;

[0023] The specific calculation method of operation and maintenance cost is:

[0024]

[0025] In the formula, C POP is the unit power operation and maintenance cost; C EoP is the unit capacity operation and maintenance cost; W(t) is the annual charging and discharging capacity of energy storage.

[0026] Furthermore, the constraints of the energy storage optimization configuration model include: wind turbine operating condition constraints, energy storage operating condition constraints and grid interaction power constraints.

[0027] Furthermore, the wind turbine operating condition constraint mainly includes the wind turbine output power constraint, which is as follows:

[0028] P WT,min (t)≤P WT (t)≤P WT,max (t);

[0029] Where P WT,min (t) is the minimum output power of the wind power system at time t, P WT (t) is the real-time output power of the wind power system at time t, P WT,min (t) is the maximum output power of the wind power system at time t;

[0030] Energy storage operation condition constraints mainly include charge and discharge power constraints, charge and discharge state constraints and charge state constraints:

[0031] 1) The calculation method of the energy storage system charging and discharging power constraint is as follows:

[0032]

[0033]

[0034] In the formula, and It is the upper limit of charging power and discharging power of the energy storage battery. and is the maximum charging current and maximum discharging current allowed by the energy storage battery, V ESS is the rated voltage of the energy storage battery, SOC max is the maximum value of the state of charge of the energy storage battery, SOC(t) is the state of charge of the energy storage battery at time t, Q max is the capacity of the energy storage battery, η ESS,C and η ESS,D is the charging efficiency and discharging efficiency of the energy storage battery, Pinv It refers to the capacity of the inverter in the energy storage system;

[0035] 2) The calculation method of the energy storage system charge and discharge state constraint is as follows:

[0036] 0≤α ESS,C +α ESS,D ≤1;

[0037] In the formula, α ESS,C and α ESS,D is the charging and discharging state of the energy storage battery, with a value of 0-1 variable, α ESS,C Taking 1 means charging, α ESS ,D 1 means discharging, and charging and discharging at the same time are not allowed;

[0038] 3) The calculation method of the state of charge constraint of the energy storage system is as follows:

[0039]

[0040] In the formula, SOC(t+1) and SOC(t) represent the state of charge of the energy storage battery at time t and time t+1. is the self-discharge rate of the energy storage battery, SOC min and SOC max It is the minimum and maximum value of the state of charge of the energy storage battery, SOC s and SOC e Refers to the initial state of charge and the final state of charge of the energy storage battery.

[0041] The calculation method of the power constraint for interaction with the grid is as follows:

[0042] P grid,min (t)≤P grid (t)≤P grid,max (t);

[0043] Where P grid,min (t) is the minimum power of the wind power system and the grid at time t, P grid (t) is the real-time interactive power between the wind power system and the power grid at time t, P grid,max (t) is the upper limit of the power of the interaction between the wind power system and the power grid at time t.

[0044] Furthermore, the non-dominated solution sorting genetic algorithm is used to solve the energy storage optimization configuration model to obtain the optimal configuration capacity of the energy storage of the new energy base. The specific method is: the NSGA-II algorithm is used to solve the energy storage optimization configuration model. The specific method is as follows:

[0045] (1) Initialize the system: input system parameters, wind turbine type and capacity, wind speed intensity, and dispatch instruction information;

[0046] (2) Initialize the population: design algorithm parameters, randomly generate the initial population, and generate the parent band population P0;

[0047] (3) Determine whether to generate the first generation population by calculating the fitness value of each individual in the population;

[0048] (4) Perform non-dominated sorting on the parent population and select individuals from the non-dominated solution set;

[0049] (5) Generate new offspring populations through selection, crossover, mutation, etc.;

[0050] (6) The parent population and the child population are merged, a fast non-dominated sort is performed on the merged population, and the crowding distance of the solution set individuals is calculated;

[0051] (7) Select suitable individuals as new parent populations to enter the next generation;

[0052] (8) Determine the termination condition. If yes, output the optimal energy storage capacity configuration; otherwise, increase the number of iterations by 1 and repeat (4) to (7).

[0053] One or more embodiments of this specification provide a new energy base energy storage optimization configuration system, including:

[0054] Power calculation module: used to obtain the actual power output of the new energy base, calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output;

[0055] Model building module: used to establish an objective function based on the deviation load and to maximize the net present value of the energy storage system over its entire life cycle, to build an energy storage optimization configuration model, and to set constraints for the energy storage optimization configuration model;

[0056] Optimization configuration module: used to solve the energy storage optimization configuration model using the non-dominated solution sorting genetic algorithm to obtain the optimal configuration capacity of energy storage in the new energy base.

[0057] One or more embodiments of the present specification provide an electronic device, including:

[0058] processor; and,

[0059] A memory arranged to store computer executable instructions, which, when executed, enable the processor to implement the steps of the above-mentioned new energy base energy storage optimization configuration method.

[0060] One or more embodiments of the present specification provide a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the above-mentioned new energy base energy storage optimization configuration method.

[0061] By adopting the embodiment of the present invention, a dual-objective optimization configuration model of a wind-storage system considering economy and reliability is constructed according to the deviation load of the operation dispatching instructions of the new energy base and the actual output of new energy. The model is solved by using a non-dominated solution sorting genetic algorithm with an elite strategy to obtain the optimal energy storage capacity of the new energy base, thereby improving the large-scale new energy acceptance capacity, reducing the penalty cost of the new energy base, simplifying the operation dispatching workload, and improving the utilization level of new energy resources.

[0062] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0064] Figure 1 A flowchart of a new energy base energy storage optimization configuration method provided by one or more embodiments of this specification;

[0065] Figure 2 A NSGA-II algorithm flow chart of a new energy base energy storage optimization configuration method provided by one or more embodiments of this specification;

[0066] Figure 3 A schematic diagram of the composition of a new energy base energy storage optimization configuration system provided by one or more embodiments of this specification;

[0067] Figure 4 A schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0069] Method Embodiment

[0070] According to an embodiment of the present invention, a method for optimizing energy storage configuration in a new energy base is provided. Figure 1 A flowchart of a method for optimizing energy storage configuration of a new energy base provided in one or more embodiments of this specification, such as Figure 1 As shown, the energy storage optimization configuration method for a new energy base according to an embodiment of the present invention specifically includes:

[0071] S1. Obtain the actual power generation output of the new energy base, and calculate the deviation load between the operation dispatching instruction of the new energy base and the actual power generation output.

[0072] Constructing a new energy base model: The power generation of the wind turbine is determined by the wind speed. The embodiment of the present invention adopts a wind speed model that obeys a two-parameter Weibull distribution. The probability density calculation is as follows:

[0073]

[0074] Where k is the shape parameter of the Weibull distribution and c is the scale parameter of the Weibull distribution.

[0075] Different model parameters are estimated for different time periods. After obtaining the wind speed samples for a certain time period, the maximum likelihood estimation method can be used to estimate the parameters in the Weibull distribution for each time period. The method is as follows:

[0076]

[0077] In the formula, are the estimated values ​​of c and k respectively; i is the wind speed sample number; v i is the i-th wind speed sample value; n is the total number of wind speed samples.

[0078] The specific method for obtaining the actual power output of the new energy base is:

[0079] The variable pitch wind turbine output characteristic curve is used as the wind turbine output characteristic curve to represent the actual power generation output of the new energy base. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine power generation is 0; when the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the wind turbine output power is obtained by fitting the variable pitch wind turbine curve; when the wind speed reaches the rated wind speed v r Less than cut-out wind speed v co When the fan is at rated power P r Run as follows:

[0080]

[0081] Among them, P w is the actual power output; v ci is the cut-in wind speed; v co is the cut-out wind speed; v r is the rated wind speed; P r (v) is the fan output power; P r is the rated power of the fan.

[0082] S2. Based on the deviation load, an objective function is established with the goal of maximizing the net present value of the energy storage system over its entire life cycle, an energy storage optimization configuration model is constructed, and constraint conditions of the energy storage optimization configuration model are set.

[0083] Constructing the economic objective function: The net benefit present value (NPV) is obtained based on the energy storage system revenue and the energy storage system construction cost, as follows:

[0084] NPV=IC inv -C op ;

[0085] Among them, C op C is the operation and maintenance cost; inv is the construction cost of the energy storage system; when NPV≥0, it means that the energy storage project is profitable; when NPV≤0, it means that the energy storage project is not profitable; when NPV=0, it is the balance point between profit and loss of the energy storage project, that is, the dynamic payback period of the project.

[0086] Construct reliability objective function: The reliability of the wind-storage system is evaluated by taking the output offset degree (OOD) of the wind-storage system as the reliability evaluation index. The output operation simulation time interval is set to 5 minutes, and the total simulation time is set to 1 year. The wind-storage output offset degree (OOD) refers to the degree of deviation between the actual output of the system and the dispatching instruction after the energy storage is configured. The specific calculation method is as follows:

[0087]

[0088] Where P dl is the wind power dispatch instruction, Ppl It is the actual output of wind power.

[0089] The calculation method of the energy storage system revenue and energy storage system construction cost is as follows:

[0090] The benefits of energy storage systems include the benefits of reducing wind curtailment and the benefits of reducing power shortage penalties, as shown below:

[0091]

[0092]

[0093] Where, α is the power loss coefficient; Q loss (t) is the annual abandoned electricity sequence; r is the discount rate; T LCC is the whole life cycle; β is the power shortage penalty coefficient; Q penalty (t) is the annual power shortage sequence P Ess,C P is the abandoned power; Ess,D The power is short of electricity;

[0094] The construction cost of the energy storage system includes the initial investment cost and operation and maintenance cost, which are as follows:

[0095] C inv =P ESS C ep +C ee E ESS ;

[0096] In the formula, C ep , C ee P is the investment cost per unit power and unit capacity of energy storage; ESS 、E ESS Configure decision variables for power and capacity of energy storage;

[0097] The specific calculation method of operation and maintenance cost is:

[0098]

[0099] In the formula, C POP is the unit power operation and maintenance cost; C EOP is the unit capacity operation and maintenance cost; W(t) is the annual charging and discharging capacity of energy storage.

[0100] The constraints of the energy storage optimization configuration model include wind turbine operating condition constraints, energy storage operating condition constraints, and grid interaction power constraints:

[0101] The wind turbine operating condition constraints mainly include the wind turbine output power constraints, which are as follows:

[0102] P WT,min (t)≤P WT(t)≤P WT,max (t);

[0103] Where P WT,min (t) is the minimum output power of the wind power system at time t, P WT (t) is the real-time output power of the wind power system at time t, P WT,min (t) is the maximum output power of the wind power system at time t;

[0104] Energy storage operation condition constraints mainly include charge and discharge power constraints, charge and discharge state constraints and charge state constraints:

[0105] 1) The calculation method of the energy storage system charging and discharging power constraint is as follows:

[0106]

[0107] In the formula, and It is the upper limit of charging power and discharging power of the energy storage battery. and is the maximum charging current and maximum discharging current allowed by the energy storage battery, V ESS is the rated voltage of the energy storage battery, SOC max is the maximum value of the state of charge of the energy storage battery, SOC(t) is the state of charge of the energy storage battery at time t, Q max is the capacity of the energy storage battery, η ESS,C and η ESS,D is the charging efficiency and discharging efficiency of the energy storage battery, P inv It refers to the capacity of the inverter in the energy storage system;

[0108] 2) The calculation method of the energy storage system charge and discharge state constraint is as follows:

[0109] 0≤α ESS,C +α ESS,D ≤1;

[0110] In the formula, α ESS,C and α ESS,D is the charging and discharging state of the energy storage battery, with a value of 0-1 variable, α ESS,C Taking 1 means charging, α ESS,D 1 means discharging, and charging and discharging at the same time are not allowed;

[0111] 3) The calculation method of the state of charge constraint of the energy storage system is as follows:

[0112]

[0113] In the formula, SOC(t+1) and SOC(t) represent the state of charge of the energy storage battery at time t and time t+1. is the self-discharge rate of the energy storage battery, SOC min and SOC max It is the minimum and maximum value of the state of charge of the energy storage battery, SOC s and SOC e Refers to the initial state of charge and the final state of charge of the energy storage battery.

[0114] The calculation method of the power constraint for interaction with the grid is as follows:

[0115] P grid,min (t)≤P grid (t)≤P grid,max (t);

[0116] Where P grid,min (t) is the minimum power of the wind power system and the grid at time t, P grid (t) is the real-time interactive power between the wind power system and the power grid at time t, P grid,max (t) is the upper limit of the power of the interaction between the wind power system and the power grid at time t.

[0117] S3. Use the non-dominated solution sorting genetic algorithm to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of energy storage in the new energy base.

[0118] Using NSGA-II algorithm, according to Figure 2 The algorithm flow in the article solves the energy storage optimization configuration model. The specific method is as follows:

[0119] (1) Initialize the system: input system parameters, wind turbine type and capacity, wind speed intensity, and dispatch instruction information;

[0120] (2) Initialize the population: design algorithm parameters, randomly generate the initial population, and generate the parent band population P0;

[0121] (3) Determine whether to generate the first generation population by calculating the fitness value of each individual in the population;

[0122] (4) Perform non-dominated sorting on the parent population and select individuals from the non-dominated solution set;

[0123] (5) Generate new offspring populations through selection, crossover, mutation, etc.;

[0124] (6) The parent population and the child population are merged, a fast non-dominated sort is performed on the merged population, and the crowding distance of the solution set individuals is calculated;

[0125] (7) Select suitable individuals as new parent populations to enter the next generation;

[0126] (8) Determine the termination condition. If yes, output the optimal energy storage capacity configuration; otherwise, increase the number of iterations by 1 and repeat (4) to (7).

[0127] The beneficial effects of the present invention are as follows:

[0128] By adopting the embodiment of the present invention, a dual-objective optimization configuration model of a wind-storage system considering economy and reliability is constructed according to the deviation load of the operation dispatching instructions of the new energy base and the actual output of new energy. The model is solved by using a non-dominated solution sorting genetic algorithm with an elite strategy to obtain the optimal energy storage capacity of the new energy base, thereby improving the large-scale new energy acceptance capacity, reducing the penalty cost of the new energy base, simplifying the operation dispatching workload, and improving the utilization level of new energy resources.

[0129] System Example

[0130] According to an embodiment of the present invention, a new energy base energy storage optimization configuration system is provided. Figure 3 A schematic diagram of a new energy base energy storage optimization configuration system provided in one or more embodiments of this specification, such as Figure 3 As shown, the energy storage optimization configuration system for a new energy base according to an embodiment of the present invention specifically includes:

[0131] Power calculation module 30: used to obtain the actual power output of the new energy base, and calculate the deviation load between the operation scheduling instruction of the new energy base and the actual power output;

[0132] Model building module 32: used to establish an objective function based on the deviation load and to maximize the net present value of the energy storage system over its entire life cycle, to build an energy storage optimization configuration model, and to set constraints for the energy storage optimization configuration model;

[0133] Optimization configuration module 34: used to solve the energy storage optimization configuration model using the non-dominated solution sorting genetic algorithm to obtain the optimal configuration capacity of energy storage in the new energy base.

[0134] The embodiment of the present invention is a system embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0135] Device Example 1

[0136] An embodiment of the present invention provides an electronic device, such as Figure 4 As shown, it includes: a memory 40, a processor 42, and a computer program stored in the memory 40 and executable on the processor 42. When the computer program is executed by the processor 42, the following method steps are implemented:

[0137] S1. Obtain the actual power output of the new energy base, and calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output;

[0138] S2. Based on the deviation load, an objective function is established with the goal of maximizing the net present value of the energy storage system over its entire life cycle, an energy storage optimization configuration model is constructed, and constraints of the energy storage optimization configuration model are set;

[0139] S3. Use the non-dominated solution sorting genetic algorithm to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of energy storage in the new energy base.

[0140] Device Example 2

[0141] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 42, the following method steps are implemented:

[0142] S1. Obtain the actual power output of the new energy base, and calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output;

[0143] S2. Based on the deviation load, an objective function is established with the goal of maximizing the net present value of the energy storage system over its entire life cycle, an energy storage optimization configuration model is constructed, and constraints of the energy storage optimization configuration model are set;

[0144] S3. Use the non-dominated solution sorting genetic algorithm to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of energy storage in the new energy base.

[0145] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0146] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy storage configuration in a new energy base, characterized in that: include: Obtain the actual power output of the new energy base, and calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output; Based on the deviation load, an objective function is established with the goal of maximizing the net present value of the energy storage system over its entire life cycle, an energy storage optimization configuration model is constructed, and constraint conditions of the energy storage optimization configuration model are set; The non-dominated solution sorting genetic algorithm is used to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of energy storage in the new energy base.

2. The method according to claim 1, characterized in that The specific method for obtaining the actual power output of the new energy base is: The variable pitch wind turbine output characteristic curve is used as the wind turbine output characteristic curve to represent the actual power generation output of the new energy base. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine power generation is 0; when the wind speed is greater than the cut-in wind speed and less than the rated wind speed, the wind turbine output power is obtained by fitting the variable pitch wind turbine curve; when the wind speed reaches the rated wind speed v r Less than cut-out wind speed v co When the fan is at rated power P r Run as follows: Among them, P w is the actual power output; v ci is the cut-in wind speed; v co is the cut-out wind speed; v r is the rated wind speed; P r (v) is the fan output power; P r is the rated power of the fan.

3. The method according to claim 1, characterized in that Based on the deviation load, an objective function is established with the maximum net present value of the energy storage system over its entire life cycle as the goal, and the specific method for constructing the energy storage optimization configuration model is as follows: The net benefit present value (NPV) is obtained based on the energy storage system revenue and the energy storage system construction cost, as follows: NPV=I-C inv -C op ; Among them, C op C is the operation and maintenance cost; inv is the construction cost of the energy storage system; when NPV≥0, it means that the energy storage project is profitable; when NPV≤0, it means that the energy storage project is not profitable; when NPV=0, it is the balance point between profit and loss of the energy storage project, that is, the dynamic payback period of the project.

4. The method according to claim 3, characterized in that The calculation method of the energy storage system revenue and energy storage system construction cost is as follows: The benefits of energy storage systems include the benefits of reducing wind curtailment and the benefits of reducing power shortage penalties, as shown below: Where, α is the power loss coefficient; Q loss (t) is the annual abandoned electricity sequence; r is the discount rate; T LCC is the whole life cycle; β is the power shortage penalty coefficient; Q penalty (t) is the annual power shortage sequence P ESS,C P is the abandoned power; ESS,D The power is short of electricity; The construction cost of the energy storage system includes the initial investment cost and operation and maintenance cost, which are as follows: C inv =P ESS C ep +C ee E EsS ; In the formula, C ep , C ee P is the investment cost per unit power and unit capacity of energy storage; ESS 、E ESS Configure decision variables for power and capacity of energy storage; The specific calculation method of operation and maintenance cost is: In the formula, C POP is the unit power operation and maintenance cost; C EOP is the unit capacity operation and maintenance cost; W(t) is the annual charging and discharging capacity of energy storage.

5. The method according to claim 1, characterized in that The constraints of the energy storage optimization configuration model include: wind turbine operating condition constraints, energy storage operating condition constraints and grid interaction power constraints.

6. The method according to claim 5, characterized in that The wind turbine operating condition constraints mainly include the wind turbine output power constraints, which are as follows: P WT,min (t)≤P WT (t)≤P WT,max (t); Where P WT,min (t) is the minimum output power of the wind power system at time t, P WT (t) is the real-time output power of the wind power system at time t, P WT,min (t) is the maximum output power of the wind power system at time t; Energy storage operation condition constraints mainly include charge and discharge power constraints, charge and discharge state constraints and charge state constraints: 1) The calculation method of the energy storage system charging and discharging power constraint is as follows: In the formula, and It is the upper limit of charging power and discharging power of the energy storage battery. and is the maximum charging current and maximum discharging current allowed by the energy storage battery, V ESS is the rated voltage of the energy storage battery, SOC max is the maximum value of the state of charge of the energy storage battery, SOC(t) is the state of charge of the energy storage battery at time t, Q max is the capacity of the energy storage battery, η ESS,C and η ESS,D is the charging efficiency and discharging efficiency of the energy storage battery, P inv It refers to the capacity of the inverter in the energy storage system; 2) The calculation method of the energy storage system charge and discharge state constraint is as follows: 0≤α ESS,C +α ESS,D ≤1； In the formula, α ESS,C and α ESS,D is the charging and discharging state of the energy storage battery, with a value of 0-1 variable, α ESS,C Taking 1 means charging, α ESS,D 1 means discharging, and charging and discharging at the same time are not allowed; 3) The calculation method of the state of charge constraint of the energy storage system is as follows: In the formula, SOC(t+1) and SOC(t) represent the state of charge of the energy storage battery at time t and time t+1. is the self-discharge rate of the energy storage battery, SOC min and SOC max It is the minimum and maximum value of the state of charge of the energy storage battery, SOC s and SOC e Refers to the initial state of charge and the final state of charge of the energy storage battery. The calculation method of the power constraint for interaction with the grid is as follows: P grid,min (t)≤P grid (t)≤P grid,max (t); Where P grid,min (t) is the minimum power of the wind power system and the grid at time t, P grid (t) is the real-time interactive power between the wind power system and the power grid at time t, P grid,max (t) is the upper limit of the power of the interaction between the wind power system and the power grid at time t.

7. The method according to claim 1, characterized in that The non-dominated solution sorting genetic algorithm is used to solve the energy storage optimization configuration model and obtain the optimal configuration capacity of the energy storage base in the new energy base. The specific method is: the NSGA-II algorithm is used to solve the energy storage optimization configuration model. The specific method is as follows: (1) Initialize the system: input system parameters, wind turbine type and capacity, wind speed intensity, and dispatch instruction information; (2) Initialize the population: design algorithm parameters, randomly generate the initial population, and generate the parent band population P0; (3) Determine whether to generate the first generation population by calculating the fitness value of each individual in the population; (4) Perform non-dominated sorting on the parent population and select individuals from the non-dominated solution set; (5) Generate new offspring populations through selection, crossover, mutation, etc.; (6) The parent population and the child population are merged, a fast non-dominated sort is performed on the merged population, and the crowding distance of the solution set individuals is calculated; (7) Select suitable individuals as new parent populations to enter the next generation; (8) Determine the termination condition. If yes, output the optimal energy storage capacity configuration; otherwise, increase the number of iterations by 1 and repeat (4) to (7).

8. A new energy base energy storage optimization configuration system, characterized in that: include: Power calculation module: used to obtain the actual power output of the new energy base, calculate the deviation load between the operation dispatching instructions of the new energy base and the actual power output; Model building module: used to establish an objective function based on the deviation load and to maximize the net present value of the energy storage system over its entire life cycle, to build an energy storage optimization configuration model, and to set constraints for the energy storage optimization configuration model; Optimization configuration module: used to solve the energy storage optimization configuration model using the non-dominated solution sorting genetic algorithm to obtain the optimal configuration capacity of energy storage in the new energy base.

9. An electronic device, characterized in that: include: processor; as well as, A memory arranged to store computer executable instructions, which, when executed, enable the processor to implement the steps of the new energy base energy storage optimization configuration method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Used to store computer executable instructions, which, when executed, implement the steps of the new energy base energy storage optimization configuration method as described in any one of claims 1 to 7.

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