A method, device and equipment for configuring multi-level coordinated energy storage capacity in an offshore wind farm

Abstract

The present application discloses a method, device and equipment for configuring multi-level coordinated energy storage capacity in an offshore wind farm. It relates to the field of new energy technology, wherein the method includes constructing functions based on the operating objectives of energy storage power stations at various levels to obtain objective functions corresponding to the energy storage power stations at various levels; optimizing each objective function based on the wind and solar output parameters of each first-level energy storage power station and the preset constraints corresponding to each level of energy storage power station to obtain initial energy storage capacity configuration parameters corresponding to each energy storage power station; performing peak and frequency regulation calculations based on the net load data of each level of energy storage power station to obtain the frequency regulation power and peak regulation power of each first-level energy storage power station; optimizing the first objective function of the same first-level energy storage power station based on each frequency regulation power, each peak regulation power and wind and solar output parameters to obtain the result of multi-level coordinated energy storage capacity configuration in an offshore wind farm. The method of the present application realizes stable and economical grid connection of offshore wind farms and improves the new energy absorption capacity of offshore wind farms.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a method, device, storage medium and electronic equipment for configuring multi-level coordinated energy storage capacity in an offshore wind farm. Background Art

[0002] With the development of the new energy industry, the installed capacity of offshore wind power in coastal cities in my country has also increased year by year. At the same time, the proportion of temperature-controlled loads and electric vehicles has increased, the uncertainty on the load side has increased, and the difficulty of grid dispatching and operation facing the randomness of source and load has also increased. Energy storage, as a regulator of the power system, can effectively control the output power of wind power and significantly improve the absorption of offshore wind power with reasonable configuration.

[0003] Current offshore wind power storage configuration methods mainly include energy storage capacity and power configuration methods that use offshore wind power forecasting to smooth out wind power output power fluctuations; and methods that are unable to cope with the uncertainty of offshore wind power output and energy storage life loss. However, existing methods all have insufficient flexibility in energy storage configuration, and energy storage configurations of different levels and types cannot be coordinated with each other, and different energy storage types cannot be used to achieve optimal effects for different optimization goals. Therefore, these methods cannot adapt to multi-objective optimization problems, and result in low accuracy in energy storage configuration, which cannot meet the stability and economy requirements of offshore wind farm grid connection. Summary of the invention

[0004] In view of this, the present invention provides a method, device, storage medium and electronic device for configuring multi-level coordinated energy storage capacity in an offshore wind farm, the main purpose of which is to solve the problem that the existing energy storage capacity configuration cannot adapt to multi-objective optimization and the energy storage capacity configuration accuracy is low.

[0005] To solve the above problems, the present application provides a method for configuring multi-level coordinated energy storage capacity in an offshore wind farm, comprising:

[0006] Functions are constructed based on the operation objectives of energy storage power stations at each level to obtain objective functions corresponding to the energy storage power stations at each level, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0007] Optimize each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and the preset constraints corresponding to each level of the energy storage power station, and obtain the initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0008] Perform peak-shaving and frequency-regulating calculations based on the net load data of the energy storage power stations at each level, and obtain the frequency-regulating power and peak-shaving power of each first-level energy storage power station at different preset time periods within the preset time range;

[0009] Based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station, the first objective function of the same first-level energy storage power station is optimized to obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0010] In order to solve the above problems, the present application provides a multi-level coordinated energy storage capacity configuration device for an offshore wind farm, comprising:

[0011] A function construction module, used to construct functions based on the operation objectives of energy storage power stations at various levels, and obtain objective functions corresponding to the energy storage power stations at various levels, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0012] A first optimization module is used to optimize each objective function based on the wind and solar power output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station, so as to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0013] A calculation module, used to perform peak-shaving and frequency-regulating calculations based on the net load data of the energy storage power stations at each level, and obtain the frequency-regulating power and peak-shaving power of each of the first-level energy storage power stations in different preset time periods within the preset time range;

[0014] The second optimization module is used to optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station, and obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0015] To solve the above problems, the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for configuring multi-level coordinated energy storage capacity of an offshore wind farm are implemented.

[0016] To solve the above problems, the present application provides an electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program on the memory, the steps of the above-mentioned method for configuring multi-level coordinated energy storage capacity of an offshore wind farm are implemented.

[0017] Beneficial effects of the present application: The present application effectively reduces the cost of energy storage power stations at all levels by constructing the objective functions of each first-level energy storage power station, each second-level energy storage power station and each third-level energy storage power station respectively; optimizes each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and the preset constraints corresponding to the energy storage power stations at each level, and obtains the initial energy storage capacity configuration parameters corresponding to the energy storage power stations at each level; performs peak and frequency regulation calculations based on the net load data of the power grid of the energy storage power stations at each level, and obtains the frequency regulation power and peak regulation power of each first-level energy storage power station in each different preset time period within the preset time range. rate; relying on the peak-shaving and frequency-regulating capacity configuration coordination method of the multi-level energy storage system, in order to strengthen the multi-time scale characteristics of multi-level capacity energy storage and multi-level scheduling, a coordinated control method for multiplexing peak-shaving and frequency-regulating of different types of energy storage is provided, which can improve energy utilization efficiency; based on the frequency regulation power, peak regulation power and wind-solar output parameters corresponding to the same first-level energy storage power station, the first objective function of the same first-level energy storage power station is optimized to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm, which improves the accuracy of the energy storage capacity configuration, realizes the stability and economical grid connection of offshore wind power, and improves the offshore wind power absorption capacity and the economic level of energy storage technology.

[0018] 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

[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0020] Figure 1 A schematic diagram of a flow chart of a method for configuring multi-level coordinated energy storage capacity in an offshore wind farm provided in an embodiment of the present application is shown;

[0021] Figure 2 A structural block diagram of a multi-level coordinated energy storage capacity configuration device for an offshore wind farm provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0023] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0024] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0025] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0026] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0027] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0028] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0029] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0030] The present application embodiment provides a method for configuring multi-level coordinated energy storage capacity in an offshore wind farm, such as Figure 1 As shown, including:

[0031] Step S101: constructing functions based on the operation objectives of energy storage power stations at each level to obtain objective functions corresponding to the energy storage power stations at each level, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0032] In the specific implementation process of this step, the level of the third-level energy storage power station is higher than that of the second-level energy storage power station and the first-level energy storage power station, and the level of the second-level energy storage power station is higher than that of the first-level energy storage power station. For example, the first-level energy storage power station can be a station-level energy storage power station, and the second-level energy storage power station can be a provincial-level energy storage power station. Each provincial-level energy storage power station can have multiple station-level energy storage power stations; the third-level energy storage power station can be the upper-level energy storage power station of each provincial-level energy storage power station, such as the East China Energy Storage Power Station and the South China Energy Storage Power Station. There can be multiple provincial-level energy storage power stations on the side of the East China Energy Storage Power Station and the South China Energy Storage Power Station. A three-level energy storage configuration method is adopted, and the first-level energy storage power station, the second-level energy storage power station and the third-level energy storage power station are independently configured with objective functions, and the objective functions include a power-type energy storage configuration objective function and an energy-type energy storage configuration objective function.

[0033] Step S102: Optimizing each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station, to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0034] In the specific implementation process of this step, the first objective function of the same first-level energy storage power station is optimized based on the wind and solar output parameters of the same first-level energy storage power station within a preset time range, the pre-acquired wind and solar output prediction parameters, and several preset first constraints, to obtain the initial energy storage capacity configuration parameters of the same first-level energy storage power station; the second objective function of the same second-level energy storage power station is optimized based on the wind and solar output parameters of several first-level energy storage power stations corresponding to the same second-level energy storage power station within a preset time range, the load demand data of the grid side to which the second-level energy storage power station belongs, and several preset second constraints corresponding to the same second-level energy storage power station, to obtain the initial energy storage capacity configuration parameters of the same second-level energy storage power station; the third objective function of the same third-level energy storage power station is optimized based on the wind and solar output parameters of several first-level energy storage power stations corresponding to each of the second-level energy storage power stations under the third-level energy storage power station within a preset time range, the load demand data of the grid side to which the third-level energy storage power station belongs, and several preset third constraints corresponding to the same third-level energy storage power station, to obtain the initial energy storage capacity configuration parameters of the third-level energy storage power station.

[0035] Step S103 performs peak-shaving and frequency-shaving calculations based on the net load data of the energy storage power stations at each level, and obtains the frequency-shaving power and peak-shaving power of each first-level energy storage power station at different preset time periods within the preset time range;

[0036] During the specific implementation of this step, calculations are performed based on the wind and solar power output parameters and load demand data of the energy storage power stations at each level to obtain the net load data of the energy storage power stations at each level; peak and frequency regulation calculations are performed based on the net load data of the energy storage power stations at each level to obtain the frequency regulation power and peak regulation power of each first-level energy storage power station in different preset time periods within the preset time range. Specifically, calculation and processing are performed based on the wind and solar output parameters and predicted output parameters of the same first-level energy storage power station to obtain the first net load data of each first-level energy storage power station; calculation and processing are performed based on the load demand parameters of the power grid to which the same second-level energy storage power station belongs, the output parameters of other power sources, and the first net load data of each first-level energy storage power station corresponding to the same second-level energy storage power station to obtain the second net load data corresponding to each second-level energy storage power station; calculation and processing are performed based on the load demand parameters of the power grid to which the third-level energy storage power station belongs, the output parameters of other power sources, and the second net load data of each second-level energy storage power station corresponding to the third-level energy storage power station to obtain the third net load data corresponding to the third-level energy storage power station; peak and frequency regulation calculation and processing are performed based on the first net load data, the second net load data, and the third net load data to obtain the frequency regulation power and peak regulation power of each first-level energy storage power station in different preset time periods within the preset time range. Specifically, the first net load data corresponding to the same secondary energy storage power station is used to calculate and process the first net load data of each primary energy storage power station, so as to obtain the first load increments for different preset time periods corresponding to the same secondary energy storage power station; the third net load data corresponding to the tertiary energy storage power station and the second net load data of each secondary energy storage power station corresponding to the tertiary energy storage power station are used to calculate and process, so as to obtain the second load increments for different preset time periods corresponding to the tertiary energy storage power station; the peak and frequency regulation calculations are performed based on each of the first load increments, the second load increments and the net load data of each primary energy storage power station, so as to obtain the frequency regulation power and the peak regulation power of each primary energy storage power station for different preset time periods within the preset time range.

[0037] Step S104: Optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers, peak regulation powers and wind / solar output parameters corresponding to the same first-level energy storage power station to obtain a multi-level coordinated energy storage capacity configuration result for the offshore wind farm.

[0038] In the specific implementation process of this step, calculation and processing are performed based on the preset frequency regulation reference value, the preset frequency regulation charge fluctuation amplitude, the frequency regulation charge threshold corresponding to each frequency regulation period in the target peak regulation period, and the maximum capacity of the power-type energy storage, so as to obtain the peak regulation redundant energy storage capacity that can be called for in each frequency regulation period; the first power-type energy storage objective function is optimized based on each peak regulation redundant energy storage capacity, each peak regulation power, and each preset peak regulation constraint condition, so as to obtain the power-type energy storage capacity configuration parameters corresponding to each frequency regulation period; calculation and processing are performed based on the peak regulation initial charge, peak regulation termination charge, preset maximum peak regulation charge, and preset minimum peak regulation charge in the target peak regulation period, so as to obtain the frequency regulation redundant energy storage capacity for frequency regulation of the peak regulation energy storage corresponding to each frequency regulation period in the target peak regulation period; the first energy-type energy storage objective function is optimized based on each frequency regulation redundant energy storage capacity, each frequency regulation power, and each preset frequency regulation constraint condition, so as to obtain the target energy storage capacity configuration parameters corresponding to each peak regulation period, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0039] The present application effectively reduces the cost of energy storage power stations at all levels by constructing the objective functions of each first-level energy storage power station, each second-level energy storage power station and each third-level energy storage power station respectively; optimizes each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and the preset constraints corresponding to the energy storage power stations at each level, and obtains the initial energy storage capacity configuration parameters corresponding to the energy storage power stations at each level; performs peak and frequency regulation calculations based on the net load data of the energy storage power stations at each level, and obtains the frequency regulation power and peak regulation power of each first-level energy storage power station at different preset time periods within the preset time range; relying on The method for coordinating the peak-shaving and frequency-regulating capacity configuration of a multi-level energy storage system provides a coordinated control method for multiplexing peak-shaving and frequency-regulating energy storage of different types to enhance the multi-time-scale characteristics of multi-level capacity energy storage and multi-level scheduling, thereby improving energy utilization efficiency. The method optimizes the first objective function of the same first-level energy storage power station based on the frequency-regulating powers, peak-shaving powers and wind-solar output parameters corresponding to the same first-level energy storage power station, thereby obtaining a result of multi-level coordinated energy storage capacity configuration for an offshore wind farm, thereby improving the accuracy of energy storage capacity configuration, achieving stable and economical grid connection of offshore wind power, and improving wind power absorption capacity and the economic level of energy storage technology.

[0040] Another embodiment of the present application provides another method for configuring multi-level coordinated energy storage capacity in an offshore wind farm, including:

[0041] Step S201: constructing a first planning cost function model, an energy storage peak-shaving benefit function model, a station benefit function model and a station penalty function model for each of the first-level energy storage power stations;

[0042] In the specific implementation process of this step, a function is constructed based on the unit electricity investment price of the energy storage power station corresponding to each of the first-level energy storage power stations, the unit power investment price of the energy storage power station, the average operation and maintenance cost per unit electricity of the energy storage power station, the average annual replacement rate of the energy storage power station, the number of cyclic charge and discharge times of the energy storage power station, the energy planning capacity of the energy storage power station, the power planning capacity of the energy storage power station, the minimum operating state of charge of the energy storage power station, and the maximum operating state of charge of the energy storage power station, so as to obtain a planning cost function model corresponding to each of the first-level energy storage power stations; specifically, based on the unit electricity investment price of the energy storage power station corresponding to each of the first-level energy storage power stations, the unit power investment price of the energy storage power station, the energy planning capacity of the energy storage power station, and the power planning capacity of the energy storage power station, calculation processing is performed to obtain the initial investment and construction cost Inv1 of energy storage; the mathematical expression of the initial investment and construction cost of energy storage can be expressed as follows:

[0043] Inv1=C p P ess +C e E ess (1);

[0044] Among them, C e is the unit electricity investment price of the energy storage power station, C p is the unit power investment price of the energy storage power station, E ess The energy planning capacity of the energy storage power station, P ess The power planning capacity of the energy storage power station. Based on the average operation and maintenance cost per unit of electricity of the energy storage power station and the energy planning capacity of the energy storage power station, the energy storage operation and maintenance cost Inv2 is obtained; the mathematical expression of the energy storage operation and maintenance cost Inv2 can be shown as the following formula (2):

[0045] Inv2=λ ess E ess (2);

[0046] Among them, λ ess is the average operation and maintenance cost per unit of electricity of the energy storage power station. The mathematical expression of the average operation and maintenance cost can be expressed as follows:

[0047]

[0048] Among them, N ess SOC is the number of charge and discharge cycles of the energy storage power station. min The minimum operating state of charge, SOC, of ​​an energy storage power station max It is the maximum operating charge state of the energy storage power station.

[0049] Based on the average annual replacement rate of energy storage power stations and the initial investment and construction cost of energy storage, the energy storage failure replacement cost Inv3 is obtained. The mathematical expression of the energy storage failure replacement cost can be expressed as the following formula (4):

[0050] Inv3 = ε·Inv1 (4);

[0051] Where: ε is the average annual replacement rate of energy storage power stations. Based on the energy storage initial investment and construction cost, energy storage operation and maintenance cost, and energy storage failure replacement cost of energy storage, a function is constructed to obtain a first planning cost function model. The mathematical expression of the first planning cost function model can be shown in the following formula (5):

[0052] Inv = Inv1 + Inv2 + Inv3 (5);

[0053] Based on the charging power of the energy storage power station in different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, the discharging power of the energy storage power station in different preset time periods, the electricity price at each time, the charging conversion efficiency of the energy storage power station in different preset time periods, and the discharging conversion efficiency in different preset time periods, a function is constructed to obtain an energy storage peak-shaving profit function model corresponding to each of the first-level energy storage power stations; specifically, the mathematical expression of the energy storage peak-shaving profit function model can be shown as follows:

[0054]

[0055] Among them, Ope represents the peak-shaving benefit of energy storage, represents the charging power of the energy storage power station in different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, Indicates the discharge power of the energy storage power station in different preset time periods, ρ t represents the electricity price at each time, η ch Represents the charging conversion efficiency of the energy storage power station in different preset time periods, η ch Indicates the discharge conversion efficiency in different preset time periods.

[0056] Based on the profit electricity price of the excess power of the station in each different preset time period within the preset time range corresponding to each first-level energy storage power station, the output imbalance penalty coefficient, the relationship variable between the station power generation and the target output, the wind and solar abandonment rate, the target output in each different preset time period, the wind power generation in each different preset time period, the photovoltaic power generation in each different preset time period, the energy storage power station charging power in each different preset time period, and the energy storage power station discharging power in each different preset time period, a function is constructed to obtain a station revenue function model corresponding to each first-level energy storage power station; specifically, the mathematical expression of the station revenue function model can be shown as follows:

[0057]

[0058] in, The mathematical expression of the profitable electricity price of the excess power of the station power generation in each different preset time period within the preset time range corresponding to each primary energy storage power station and the profitable electricity price of the excess power of the station power generation can be shown as the following formula (8):

[0059]

[0060] τ output imbalance penalty coefficient, b s The relationship variable between the station power generation and the target output, b s =0 means the station power generation is higher than the target output, b s =1 means the power generation capacity of the station is lower than the target output; α is the wind and solar power abandonment rate, Contribute to the goals for different preset time periods. The wind power generation power in different preset time periods, The photovoltaic power generation power for different preset time periods, The charging power of the energy storage station for different preset time periods and The discharge power of the energy storage power station for different preset time periods.

[0061] Based on the penalty fee for the shortfall of the power generation of the site in each different preset time period within the preset time range corresponding to each first-level energy storage power station, the relationship variable between the power generation of the site and the target output, the target output in each different preset time period, the charging power of the energy storage power station in each different preset time period, the discharging power of the energy storage power station in each different preset time period, the wind and solar abandonment rate, the wind power generation power in each different preset time period, and the photovoltaic power generation power in each different preset time period, a function is constructed to obtain a site penalty function model corresponding to each first-level energy storage power station; specifically, the mathematical expression of the site penalty function model can be shown as the following formula (9):

[0062]

[0063] in, is the penalty fee for the shortfall of the power generation of the station in each different preset time period within the preset time range corresponding to each primary energy storage power station. The mathematical expression of the penalty fee for the shortfall of the power generation of the station can be expressed as the following formula (10):

[0064]

[0065] Step S202: constructing functions based on the planning cost function model, the energy storage peak regulation benefit function model, the station benefit function model and the station penalty function model of the same first-level energy storage power station to obtain the first objective function corresponding to the same first-level energy storage power station, wherein the first objective function includes a first power-type energy storage objective function and a first energy-type energy storage objective function;

[0066] In the specific implementation of this step, the first objective function includes a first power-type energy storage objective function and a first energy-type energy storage objective function. Specifically, based on the planning cost function model of the energy storage device in the planning cost function model, the energy storage peak-shaving benefit function model of the energy storage device, the site benefit function model of the energy storage device, and the site penalty function model of the energy storage device, a function is constructed to generate the first energy storage objective function; based on the first planning cost function model of the power storage power station in the planning cost function model, a function is constructed to generate the first power storage objective function. Among them, the mathematical expression of the first energy storage objective function can be shown as follows:

[0067] minInv-Ope-Rev+Pen(11);

[0068] The mathematical expression of the first power type energy storage objective function can be expressed as the following formula (12):

[0069] min Inv′ (12);

[0070] Among them, Inv' is the first planning cost function model of the power type energy storage power station, and the first planning cost of the power type energy storage power station is obtained by adding the initial investment and construction cost of energy storage, energy storage operation and maintenance cost, and energy storage failure replacement cost of each power type energy storage power station in each first-level energy storage power station. Specifically, the mathematical expression of the initial investment and construction cost of energy storage of each power type energy storage power station is shown in the following formula (13):

[0071] Inv'1=C' p P' ess +C' e E' ess (13);

[0072] Among them, C' e is the unit electricity investment price of power storage power station; C' p P' is the unit power investment price of power storage power station; ess The power planning capacity of the power type energy storage power station, E e ' ssis the energy planning capacity of the power type energy storage power station. The mathematical expression of the power planning capacity of the power type energy storage power station can be expressed as the following formula (14):

[0073]

[0074] Among them, p es, is the output of energy storage per minute; n is the number of time periods; the mathematical expression of the energy planning capacity of the power type energy storage power station can be expressed as follows:

[0075]

[0076] Among them, SOC' min is the minimum operating state of charge, SOC' of the power storage power station max is the maximum operating charge state of the power-type energy storage power station. The mathematical expression of the energy storage operation and maintenance cost of each power-type energy storage power station can be expressed as follows:

[0077] Inv'2 = λ'essE'ess(16);

[0078] Among them, λ' ess is the average operation and maintenance cost per unit of electricity of the power-type energy storage power station. The mathematical expression of the energy storage failure replacement cost of each power-type energy storage power station can be expressed as the following formula (17):

[0079] Inv'3 = εInv'1 (17);

[0080] Wherein, ε is the average annual replacement rate of power-type energy storage power stations. Based on the initial investment and construction cost of power-type energy storage, the energy storage operation and maintenance cost, and the energy storage failure replacement cost, a function is constructed to obtain a first planning cost function model of power-type energy storage. The mathematical expression of the first planning cost function model of power-type energy storage can be shown in the following formula (18):

[0081] Inv'=Inv'1+Inv'2+Inv'3 (18).

[0082] Step S203: constructing a second planning cost function model, an energy storage operation cost model, a thermal power unit operation cost model and other unit operation cost models for each of the secondary energy storage power station and the tertiary energy storage power station respectively;

[0083] In the specific implementation process of this step, a function is constructed based on the energy storage initial investment and construction cost model, the energy storage operation and maintenance cost model, and the energy storage failure replacement cost model of each secondary energy storage power station to obtain a second planning cost function model of each secondary energy storage power station; the second planning cost function model of each secondary energy storage power station can be expressed as the following formula (19):

[0084] Inv0=Inv01+Inv02+Inv03(19);

[0085] Among them, Inv0 is the second planning cost, Inv 01 Inv is the initial investment and construction cost of the secondary energy storage power station. 02 is the energy storage operation and maintenance cost of the secondary energy storage power station, Inv 03 is the replacement cost of energy storage failure in the secondary energy storage power station. 01 The mathematical expression of can be shown as follows:

[0086] Inv 01 =C p0 P ess0 +C e0 E ss0 (20);

[0087] Inv 02 The mathematical expression of can be shown as follows:

[0088] I nv02 =λ ess0 E ess0 (twenty one);

[0089] Inv 03 The mathematical expression of can be shown as follows:

[0090] Inv 03 =ε0·Inv 01 (twenty two);

[0091] Among them, C e0 is the unit electricity investment price of the secondary energy storage power station; C p0 is the unit power investment price of the secondary energy storage power station; ess0 is the average operation and maintenance cost per unit of electricity of the secondary energy storage power station; ε0 is the average annual replacement rate of the secondary energy storage power station; N ess0 P is the number of times the secondary energy storage power station can be charged and discharged cyclically; ess0 E is the power of energy storage configuration; ess0 To configure the capacity of energy storage; SOC 0max ,SOC 0min They are the maximum and minimum SOC limits of energy storage respectively; Ope1 is the energy storage operation cost model; Ope2 is the thermal power unit operation model; Ope3 is the operation cost model of other units.

[0092] Step S204: constructing functions based on the second planning cost function model, the energy storage operation cost model, the thermal power unit operation cost model and other unit operation cost models of the same secondary energy storage power station to obtain a second objective function corresponding to the same secondary energy storage power station, wherein the second objective function includes a second power-type energy storage objective function and a second energy-type energy storage objective function;

[0093] In the specific implementation of this step, the second objective function includes a second power-type energy storage objective function and a second energy-type energy storage objective function. Specifically, based on the planning cost function model of the energy storage device in the second planning cost function model, the energy storage peak-shaving benefit function model of the energy storage device, the site benefit function model of the energy storage device, and the site penalty function model of the energy storage device, a function is constructed to generate the second energy storage objective function; based on the second planning cost function model of the power storage power station in the second planning cost function model, a function is constructed to generate the second power-type energy storage objective function. Among them, the mathematical expression of the second energy storage objective function can be shown as follows:

[0094] minInv0+Ope1+Ope2+Ope3 (23);

[0095] The mathematical expression of the second power type energy storage objective function can be expressed as follows:

[0096]

[0097] Among them, V AGC is the total bid-winning capacity of the unit; p v The unit price for purchasing frequency modulation capacity for AGC.

[0098] Step S205: constructing a function based on the second planning cost function model of the three-level energy storage power station, the energy storage operation cost model, the thermal power unit operation cost model and other unit operation cost models to obtain a third objective function corresponding to the three-level energy storage power station, wherein the third objective function includes a third power-type energy storage objective function and a third energy-type energy storage objective function;

[0099] In the specific implementation of this step, the third objective function includes a third power-type energy storage objective function and a third energy-type energy storage objective function. Specifically, based on the planning cost function model of the energy-type energy storage device in the second planning cost function model of the three-level energy storage power station, the energy storage peak-shaving benefit function model of the energy-type energy storage device, the site benefit function model of the energy-type energy storage device, and the site penalty function model of the energy-type energy storage device, a function is constructed to generate the third energy-type energy storage objective function; based on the second planning cost function model of the power-type energy storage power station in the second planning cost function model of the three-level energy storage power station, a function is constructed to generate the third power-type energy storage objective function.

[0100] Step S206: Optimizing each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station, to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0101] During the specific implementation of this step, the first objective function of the same first-level energy storage power station is optimized based on the wind and solar power output parameters of the same first-level energy storage power station within a preset time range, the pre-acquired wind and solar power output prediction parameters, and a number of preset first constraints to obtain the initial energy storage capacity configuration parameters of the same first-level energy storage power station. Specifically, for the same first-level energy storage power station, the wind and solar output parameters and the output prediction parameters corresponding to different preset time periods within the preset time range are judged one by one whether they meet the preset first constraint conditions, and a judgment result is obtained; when the judgment result is that any one of the preset first constraint conditions is not met, the current energy storage capacity configuration parameters and the current operation decision parameters of the first-level energy storage power station are adjusted until the wind and solar output parameters and the output prediction parameters in different preset time periods meet the preset first constraint conditions, and the initial energy storage capacity configuration parameters are obtained; wherein, the preset first constraint conditions corresponding to the first energy type energy storage objective function in the first objective function include the peak load balance constraint of each preset time period, the energy storage power station planning constraint, the energy storage power station operation constraint, the wind turbine operation constraint and the photovoltaic unit operation constraint. wherein, the energy storage power station planning constraint includes the energy capacity and power capacity ratio constraint of the energy storage power station, and the installed energy capacity and power capacity range constraint, and the mathematical expression of the energy storage power station planning constraint can be shown as follows Formula (25):

[0102]

[0103] Among them, Ratio E / P is the energy rate of the energy storage power station, with a value range of 2-10h; E essPlanning capacity for energy storage power station; P ess Plan the capacity of the energy storage power station; P max_ess The maximum value of the planned capacity of the energy storage power station. The energy storage power station operation constraints include: energy storage charging and discharging power, energy storage capacity, cycle life, energy conversion efficiency and other constraints. The mathematical expression of the energy storage power station operation constraints can be expressed as the following formula (26):

[0104]

[0105] in, It is the charging and discharging status flag parameter of the energy storage power station, which is a 0-1 variable. are the upper and lower limits of the energy storage power station’s warning operation state of charge; γ is the discharge power attenuation factor in the warning working interval of the energy storage power station; SOC t SOC is the operating state of charge of the energy storage power station at time t; min , SOC max The wind turbine operating constraints and photovoltaic operating constraints include: output characteristic constraints, wind and solar abandonment rate constraints, etc. The mathematical expressions of the wind turbine operating constraints and photovoltaic operating constraints can be expressed as the following formula (27):

[0106]

[0107] in, It is the charging and discharging status flag parameter of the energy storage power station, which is a 0-1 variable; SOC min , SOC max They are the minimum and maximum operating charge states of the energy storage power station respectively; They are the upper and lower limits of the energy storage power station's warning operation state of charge, respectively; γ is the discharge power attenuation factor of the energy storage power station's warning working interval, which is 0.5; SOC t SOC is the operating state of charge of the energy storage power station at time t; ini , SOC end They are the initial and final operating charge states of the energy storage power station respectively; is the abandoned wind and solar power at the station at time t; β is the upper limit of the station output fluctuation rate, which is 0.1; α max It is the upper limit of the wind and solar power abandonment rate of the station, which is 0.1.

[0108] Among them, each of the preset first constraint conditions corresponding to the first frequency-based energy storage objective function in the first objective function includes a short-term power fluctuation rate constraint, and the mathematical expression of the short-term power fluctuation rate constraint can be expressed as the following formula (28):

[0109]

[0110] Among them, δ c represents the power fluctuation limit at the station level, p net,i represents the net load in the ith minute, p es,i represents the output of energy storage in the i-th minute, p es,i >0 means energy storage charging, p es,i ≤0 means energy storage discharge, It represents the average value of the net load in the jth hour corresponding to the i-th minute.

[0111] Based on the wind and solar output parameters of several first-level energy storage power stations corresponding to the same second-level energy storage power station within a preset time range, the load demand data on the grid side to which the second-level energy storage power station belongs, and several preset second constraints corresponding to the same second-level energy storage power station, the second objective function of the same second-level energy storage power station is optimized to obtain the initial energy storage capacity configuration parameters of the same second-level energy storage power station; based on the wind and solar output parameters of several first-level energy storage power stations corresponding to each of the second-level energy storage power stations under the third-level energy storage power station within a preset time range, the load demand data on the grid side to which the third-level energy storage power station belongs, and several preset third constraints corresponding to the same third-level energy storage power station, the third objective function of the same third-level energy storage power station is optimized to obtain the initial energy storage capacity configuration parameters of the third-level energy storage power station.

[0112] Step S207: Obtaining the net load data of the energy storage power stations at each level;

[0113] In the specific implementation process of this step, calculation and processing are performed based on the wind and solar output parameters and the predicted output parameters of the same first-level energy storage power station to obtain the first net load data of each of the first-level energy storage power stations; calculation and processing are performed based on the load demand parameters of the power grid to which the same second-level energy storage power station belongs, the output parameters of other power sources, and the first net load data of each first-level energy storage power station corresponding to the same second-level energy storage power station to obtain the second net load data corresponding to each of the second-level energy storage power stations; calculation and processing are performed based on the load demand parameters of the power grid to which the third-level energy storage power station belongs, the output parameters of other power sources, and the second net load data of each of the second-level energy storage power stations corresponding to the third-level energy storage power station to obtain the third net load data corresponding to the third-level energy storage power station.

[0114] Step S207: Calculating and processing the first net load data of each primary energy storage station based on the second net load data corresponding to the same secondary energy storage station to obtain the first load increments of different preset time periods corresponding to the same secondary energy storage station;

[0115] In the specific implementation process of this step, the first net load data of each first-level energy storage power station is calculated and processed using the first objective function to obtain the first index value of each first-level energy storage power station, the first index value includes energy storage capacity and ratio, the energy storage capacity is power value or energy value, and the ratio is the ratio value of energy storage power divided by new energy installed capacity, the second net load data of each second-level energy storage power station is calculated and processed using the second objective function to obtain the second index value of each second-level energy storage power station, and the first load increment is obtained by calculating and processing based on each first index value and each second index value. Specifically, each second index value is added to obtain the second target index value; each first index value is added to obtain the first target index value; the second target index value is subtracted from the first target index value to obtain the first load increment.

[0116] Step S208: Calculate and process the third net load data corresponding to the third-level energy storage power station and the second net load data of each of the second-level energy storage power stations corresponding to the third-level energy storage power station to obtain the second load increments corresponding to the third-level energy storage power station in different preset time periods;

[0117] In the specific implementation process of this step, the third net load data of the three-level energy storage power station is calculated and processed using the third objective function to obtain the third index value of the three-level energy storage power station, and the second load increment is obtained by calculating and processing based on each of the second index values ​​and the third index value. Specifically, each of the second index values ​​is added to obtain the second target index value; and the second load increment is obtained by subtracting the third index value from the second target index value.

[0118] Step S209: performing peak-shaving and frequency-shaving calculations based on the first load increments, the second load increments, and the first net load data of each primary energy storage power station, to obtain the frequency-shaving power and the peak-shaving power of each primary energy storage power station in each different preset time period within the preset time range;

[0119] In the specific implementation process of this step, relying on the coordination method of peak-shaving and frequency-regulating capacity configuration of multi-level energy storage systems, in order to strengthen the multi-time scale characteristics of multi-level capacity energy storage and multi-level scheduling, a coordinated control strategy based on different types of energy storage peak-shaving and frequency-regulating reuse is proposed: First, in order to achieve the output fluctuation of wind and solar power stations, meet the peak-shaving demand of long time scales and suppress short-term power fluctuations, and obtain the energy storage planning and operation plan at the optimal cost. It is necessary to obtain the frequency regulation target output according to different time scales based on the actual output of the first-level energy storage power station, and subtract it from the actual output to obtain the frequency regulation power; after averaging the frequency regulation target output for the first preset time, subtract it from the predicted output parameter to obtain the peak-shaving power, and the first preset time can be 1h.

[0120] Step S210: Optimizing the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station, and obtaining target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain a multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0121] In the specific implementation process of this step, calculation and processing are performed based on the preset frequency modulation reference value, the preset frequency modulation charge fluctuation amplitude, the frequency modulation charge threshold corresponding to each frequency modulation period within the target peak modulation period, and the maximum capacity of the power-type energy storage to obtain the peak-shaving redundant energy storage capacity that can be called for in each frequency modulation period; the mathematical expression of the peak-shaving redundant energy storage capacity can be expressed as follows:

[0122]

[0123] Wherein, t represents the peak load period, which can be 60 minutes, w represents the frequency modulation period, which can be 1 minute, and w is a series of frequency modulation periods within each t period. Due to the shallow charge and discharge characteristics of energy storage during frequency modulation, the SOC level of the energy storage device used for frequency modulation is at a slightly fluctuating reference value level. SOC r=β±δ; β and δ represent the frequency modulation reference value and the SOC fluctuation amplitude during frequency modulation, respectively. Traverse all w time periods, and set an SOC upper and lower limit threshold SOCη for each w time period, and the difference between it and the actual SOC is the available peak-shaving redundant energy storage capacity in each w time period: Based on each of the peak-shaving redundant energy storage capacities, each of the peak-shaving powers, and each preset peak-shaving constraint condition, optimize the first power-type energy storage objective function to obtain the power-type energy storage capacity configuration parameters corresponding to each of the frequency modulation time periods; Based on the peak-shaving initial charge, peak-shaving termination charge, preset maximum peak-shaving charge, and preset minimum peak-shaving charge in the target peak-shaving time period, calculate and process to obtain the frequency-shaving redundant energy storage capacity for frequency modulation corresponding to each frequency modulation time period in the target peak-shaving time period; The mathematical expression of the frequency-shaving redundant energy storage capacity for frequency modulation can be expressed as the following formula (30):

[0124]

[0125] Among them, the initial and final SOC of the peak-shaving energy storage in the t-th peak-shaving cycle are SOCs(t) and SOCe(t) respectively; when charging, SOCe(t) = SOCmax(t), SOCs(t) = SOCmin(t); when discharging, SOCe(t) = SOCmin(t), SOCs(t) = SOCmax(t). SOC' 0max is the preset maximum peak load, SOC' 0max (t) is the peak load termination charge within the target peak load period t; SOC' 0min is the preset minimum peak load; SOC' 0max (t) is the peak load in the target peak load period t. The first energy storage objective function is optimized based on the frequency regulation redundant energy storage capacity, the frequency regulation power and the preset frequency regulation constraints to obtain the target energy storage capacity configuration parameters corresponding to the peak load period, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm. The mathematical expression of the peak load storage constraint for each frequency regulation period can be expressed as follows:

[0126]

[0127] The mathematical expression of the peak load storage participating in frequency regulation in each frequency regulation period can be expressed as follows:

[0128]

[0129] Based on this capacity and the peak-shaving demand in each t period of a day, taking into account the cycle aging cost and the impact of the charge and discharge depth on the energy storage operation cost, the power-type energy storage SOC is adjusted in real time to improve the utilization rate of the energy storage device. The SOC benchmark for energy storage operation during peak and valley periods is optimized to achieve further reuse of peak-shaving and frequency regulation.

[0130] The present application mainly includes starting from the three-level energy storage configuration, making the capacity configuration of the first-level, second-level and third-level energy storage power stations independently configured, and the power-type energy storage power station and the energy-type energy storage power station independently configured, and performing the station capacity configuration to effectively reduce the station operation cost, and obtaining the target energy storage configuration data of the target power grid; relying on the peak-shaving and frequency-regulating capacity configuration coordination scheme of the multi-level energy storage system, in order to strengthen the multi-time scale characteristics of multi-level capacity energy storage and multi-level scheduling, a coordinated control strategy based on the reuse of different types of energy storage for peak-shaving and frequency regulation is proposed; utilizing the energy storage operation constraints and offshore wind power output characteristics, combined with the capacity attenuation characteristics of the energy storage power station and the full life cycle cost, a multi-level coordinated energy storage capacity configuration method for the target offshore wind farm is constructed, and an economic evaluation method based on the typical wind-storage joint participation in the peak-shaving and frequency regulation of the power grid is used to solve the problem of low accuracy of the existing offshore wind power storage configuration, and realize the stable and economical grid connection of offshore wind power, and improve the wind power absorption capacity and the economic level of energy storage technology.

[0131] Another embodiment of the present application provides a multi-level coordinated energy storage capacity configuration device for an offshore wind farm, such as Figure 2 As shown, including:

[0132] Function construction module 1, used to construct functions based on the operation objectives of energy storage power stations at various levels, and obtain objective functions corresponding to the energy storage power stations at various levels, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0133] The first optimization module 2 is used to optimize each objective function based on the wind and solar power output parameters of each of the first-level energy storage power stations within a preset time range and the preset constraints corresponding to the energy storage power stations at each level, and obtain the initial energy storage capacity configuration parameters corresponding to the energy storage power stations at each level;

[0134] Calculation module 3, used to perform peak-shaving and frequency-regulation calculations based on the net load data of the energy storage power stations at each level, and obtain the frequency-regulation power and peak-shaving power of each first-level energy storage power station in different preset time periods within the preset time range;

[0135] The second optimization module 4 is used to optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station, and obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0136] In the specific implementation process, the function construction module 1 is specifically used to construct the first planning cost function model, energy storage peak-shaving benefit function model, station benefit function model and station penalty function model of each of the first-level energy storage power stations respectively; based on the planning cost function model, energy storage peak-shaving benefit function model, station benefit function model and station penalty function model of the same first-level energy storage power station, function construction is performed to obtain the first objective function corresponding to the same first-level energy storage power station, and the first objective function includes a first power-type energy storage objective function and a first energy-type energy storage objective function; the second planning cost function model, energy storage operation cost model, thermal power unit operation cost model of each of the second-level energy storage power station and the third-level energy storage power station are respectively constructed. and other unit operating cost models; based on the second planned cost function model, energy storage operating cost model, thermal power unit operating cost model and other unit operating cost models of the same secondary energy storage power station, function construction is performed to obtain a second objective function corresponding to the same secondary energy storage power station, and the second objective function includes a second power-type energy storage objective function and a second energy-type energy storage objective function; based on the second planned cost function model, energy storage operating cost model, thermal power unit operating cost model and other unit operating cost models of the tertiary energy storage power station, function construction is performed to obtain a third objective function corresponding to the tertiary energy storage power station, and the third objective function includes a third power-type energy storage objective function and a third energy-type energy storage objective function.

[0137] In the specific implementation process, the function construction module 1 is also used to: construct functions based on the unit electricity investment price of the energy storage power station corresponding to each of the first-level energy storage power stations, the unit power investment price of the energy storage power station, the average operation and maintenance cost of the unit electricity of the energy storage power station, the average annual replacement rate of the energy storage power station, the number of cyclic charging and discharging times of the energy storage power station, the energy planning capacity of the energy storage power station, the power planning capacity of the energy storage power station, the minimum operating state of charge of the energy storage power station, and the maximum operating state of charge of the energy storage power station, so as to obtain the planning cost function model corresponding to each of the first-level energy storage power stations; construct functions based on the charging power of the energy storage power station in different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, the discharging power of the energy storage power station in different preset time periods, the electricity price at each time, the charging conversion efficiency of the energy storage power station in different preset time periods, and the discharging conversion efficiency in different preset time periods, so as to obtain the energy storage peak-shaving profit function model corresponding to each of the first-level energy storage power stations; construct functions based on the charging power of the energy storage power station in different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, the discharging power of the energy storage power station in different preset time periods, the electricity price at each time, the charging conversion efficiency of the energy storage power station in different preset time periods, and the discharging conversion efficiency in different preset time periods, so as to obtain the energy storage peak-shaving profit function model corresponding to each of the first-level energy storage power stations; Functions are constructed based on the profit electricity price for the excess power generation of the site in different preset time periods, the penalty coefficient for output imbalance, the relationship variable between the site power generation and the target output, the wind and solar power abandonment rate, the target output for each different preset time period, the wind power generation power for each different preset time period, the photovoltaic power generation power for each different preset time period, the charging power of the energy storage power station for each different preset time period, and the discharging power of the energy storage power station for each different preset time period, so as to obtain the site profit function model corresponding to each of the first-level energy storage power stations; based on the penalty fee for the shortfall of the site power generation in each different preset time period within the preset time range corresponding to each of the first-level energy storage power stations, the relationship variable between the site power generation and the target output, the target output for each different preset time period, the charging power of the energy storage power station for each different preset time period, the discharging power of the energy storage power station for each different preset time period, the wind and solar power abandonment rate, the wind power generation power for each different preset time period, and the photovoltaic power generation power for each different preset time period, so as to obtain the site penalty function model corresponding to each of the first-level energy storage power stations.

[0138] In the specific implementation process, the first optimization module 2 is specifically used to: optimize the first objective function of the same first-level energy storage power station based on the wind and solar output parameters of the same first-level energy storage power station within a preset time range, the pre-acquired wind and solar output prediction parameters, and several preset first constraints, to obtain the initial energy storage capacity configuration parameters of the same first-level energy storage power station; optimize the second objective function of the same second-level energy storage power station based on the wind and solar output parameters of several first-level energy storage power stations corresponding to the same second-level energy storage power station within a preset time range, the load demand data of the grid side to which the second-level energy storage power station belongs, and several preset second constraints corresponding to the same second-level energy storage power station, to obtain the initial energy storage capacity configuration parameters of the same second-level energy storage power station; optimize the third objective function of the same third-level energy storage power station based on the wind and solar output parameters of several first-level energy storage power stations corresponding to each second-level energy storage power station under the third-level energy storage power station within a preset time range, the load demand data of the grid side to which the third-level energy storage power station belongs, and several preset third constraints corresponding to the same third-level energy storage power station, to obtain the initial energy storage capacity configuration parameters of the third-level energy storage power station.

[0139] In the specific implementation process, the first optimization module 2 is also used to: for the same first-level energy storage power station, determine one by one whether the wind and solar output parameters corresponding to each different preset time period within the preset time range and the output prediction parameters meet each preset first constraint condition, and obtain a judgment result; when the judgment result is that any one of the preset first constraint conditions is not met, adjust the current energy storage capacity configuration parameters and current operation decision parameters of the first-level energy storage power station until the wind and solar output parameters and the output prediction parameters in each different preset time period meet each preset first constraint condition, and obtain the initial energy storage capacity configuration parameters; wherein each preset first constraint condition includes peak and frequency regulation balance constraints in each different preset time period, energy storage power station planning constraints, energy storage power station operation constraints, wind turbine operation constraints, and photovoltaic unit operation constraints.

[0140] In the specific implementation process, the first optimization module 2 is also used for: performing peak-shaving and frequency-regulating calculations based on the net load data of the energy storage power stations at each level to obtain the frequency-regulating power and peak-shaving power of each first-level energy storage power station in different preset time periods within the preset time range, specifically including: performing calculations and processing based on the second net load data corresponding to the same second-level energy storage power station with the first net load data of each first-level energy storage power station to obtain the first load increments for different preset time periods corresponding to the same second-level energy storage power station; performing calculations and processing based on the third net load data corresponding to the third-level energy storage power station and the second net load data of each second-level energy storage power station corresponding to the third-level energy storage power station to obtain the second load increments for different preset time periods corresponding to the third-level energy storage power station; performing peak-shaving and frequency-regulating calculations based on each first load increment, the second load increment, and the first net load data of each first-level energy storage power station to obtain the frequency-regulating power and the peak-shaving power of each first-level energy storage power station in different preset time periods within the preset time range.

[0141] In the specific implementation process, the second optimization module 4 is specifically used to: calculate and process based on the preset frequency modulation reference value, the preset frequency modulation charge fluctuation amplitude, the frequency modulation charge threshold corresponding to each frequency modulation period within the target peak modulation period, and the maximum capacity of the power-type energy storage, to obtain the callable peak-shaving redundant energy storage capacity of each frequency modulation period; optimize the first power-type energy storage objective function based on each peak-shaving redundant energy storage capacity, each peak-shaving power and each preset peak-shaving constraint condition to obtain the power-type energy storage capacity configuration parameters corresponding to each frequency modulation period; based on the The initial peak-shaving load, the peak-shaving termination load, the preset maximum peak-shaving load, and the preset minimum peak-shaving load within the target peak-shaving period are calculated and processed to obtain the frequency regulation redundant energy storage capacity of the peak-shaving energy storage corresponding to each frequency regulation period within the target peak-shaving period for frequency regulation; based on each of the frequency regulation redundant energy storage capacities, each of the frequency regulation powers, and each preset frequency regulation constraint condition, the first energy-type energy storage objective function is optimized to obtain the target energy storage capacity configuration parameters corresponding to each of the peak-shaving periods, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0142] The present application effectively reduces the cost of energy storage power stations at all levels by constructing the objective functions of each first-level energy storage power station, each second-level energy storage power station and each third-level energy storage power station respectively; optimizes each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and the preset constraints corresponding to the energy storage power stations at each level, and obtains the initial energy storage capacity configuration parameters corresponding to the energy storage power stations at each level; performs peak and frequency regulation calculations based on each initial energy storage capacity configuration parameter, and obtains the frequency regulation power and peak regulation power of each first-level energy storage power station at different preset time periods within the preset time range; relies on multiple The invention discloses a method for coordinating the peak-shaving and frequency-regulating capacity configuration of a multi-level energy storage system, which provides a coordinated control method for multiplexing peak-shaving and frequency-regulating energy storage of different types to enhance the multi-time scale characteristics of multi-level capacity energy storage and multi-level scheduling, thereby improving energy utilization efficiency; based on the frequency regulation power, peak regulation power and wind / solar output parameters corresponding to the same first-level energy storage power station, the first objective function of the same first-level energy storage power station is optimized to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm, thereby improving the accuracy of the energy storage capacity configuration, realizing stable and economical grid connection of offshore wind power, and improving the wind power absorption capacity and the economic level of energy storage technology.

[0143] Another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the following method steps are implemented:

[0144] Step 1: construct functions based on the operation objectives of energy storage power stations at each level to obtain objective functions corresponding to the energy storage power stations at each level, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0145] Step 2: Optimize each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0146] Step 3: Perform peak-shaving and frequency-shaving calculations based on the net load data of the energy storage power stations at each level to obtain the frequency-shaving power and peak-shaving power of each first-level energy storage power station at different preset time periods within the preset time range;

[0147] Step 4: Optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station to obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0148] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0149] The specific implementation process of the above method steps can be found in the above-mentioned embodiment of the multi-level coordinated energy storage capacity configuration method for offshore wind farms, and this embodiment will not be repeated here.

[0150] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0151] Step 1: construct functions based on the operation objectives of energy storage power stations at each level to obtain objective functions corresponding to the energy storage power stations at each level, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station;

[0152] Step 2: Optimize each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station;

[0153] Step 3: Perform peak-shaving and frequency-shaving calculations based on the net load data of the energy storage power stations at each level to obtain the frequency-shaving power and peak-shaving power of each first-level energy storage power station at different preset time periods within the preset time range;

[0154] Step 4: Optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station to obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

[0155] The specific implementation process of the above method steps can refer to any of the above embodiments of the multi-level coordinated energy storage capacity configuration method for offshore wind farms, and this embodiment will not be repeated here.

[0156] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for configuring multi-level coordinated energy storage capacity in an offshore wind farm, characterized in that: include: Functions are constructed based on the operation objectives of energy storage power stations at each level to obtain objective functions corresponding to the energy storage power stations at each level, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station; the third-level energy storage power station is higher in level than the second-level energy storage power station and the first-level energy storage power station, the second-level energy storage power station is higher in level than the first-level energy storage power station, and the first objective function includes a first power-type energy storage objective function and a first energy-type energy storage objective function; Optimize each objective function based on the wind and solar output parameters of each first-level energy storage power station within a preset time range and the preset constraints corresponding to each level of the energy storage power station, and obtain the initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station; Perform peak-shaving and frequency-regulating calculations based on the net load data of the energy storage power stations at each level, and obtain the frequency-regulating power and peak-shaving power of each first-level energy storage power station at different preset time periods within the preset time range; The first objective function of the same first-level energy storage power station is optimized based on each frequency modulation power and each peak modulation power corresponding to the same first-level energy storage power station to obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm, specifically including: Based on the preset frequency modulation reference value, the preset frequency modulation charge fluctuation amplitude, the frequency modulation charge threshold corresponding to each frequency modulation period within the target peak modulation period, and the maximum capacity of the power-type energy storage, calculation and processing are performed to obtain the peak-shaving redundant energy storage capacity that can be called for in each frequency modulation period; Optimizing the first power-type energy storage objective function based on each of the peak-shaving redundant energy storage capacities, each of the peak-shaving powers, and each of the preset peak-shaving constraints to obtain power-type energy storage capacity configuration parameters corresponding to each of the frequency regulation periods; Based on the peak-shaving initial charge, peak-shaving termination charge, preset maximum peak-shaving charge, and preset minimum peak-shaving charge in the target peak-shaving period, a calculation is performed to obtain the frequency regulation redundant energy storage capacity of the peak-shaving energy storage corresponding to each frequency regulation period in the target peak-shaving period for frequency regulation; The first energy type energy storage objective function is optimized based on each of the frequency regulation redundant energy storage capacities, each of the frequency regulation powers and each of the preset frequency regulation constraints to obtain target energy storage capacity configuration parameters corresponding to each of the peak regulation periods, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

2. The method according to claim 1, characterized in that The function is constructed based on the operation objectives of the energy storage power stations at each level to obtain the objective functions corresponding to the energy storage power stations at each level, specifically including: Respectively constructing a first planning cost function model, an energy storage peak-shaving benefit function model, a station benefit function model, and a station penalty function model for each of the first-level energy storage power stations; Function construction is performed based on the planning cost function model, energy storage peak regulation benefit function model, station benefit function model and station penalty function model of the same first-level energy storage power station to obtain the first objective function corresponding to the same first-level energy storage power station; Respectively constructing a second planning cost function model, an energy storage operation cost model, a thermal power unit operation cost model, and other unit operation cost models for each of the secondary energy storage power station and the tertiary energy storage power station; Functions are constructed based on the second planning cost function model, energy storage operation cost model, thermal power unit operation cost model and other unit operation cost models of the same secondary energy storage power station to obtain a second objective function corresponding to the same secondary energy storage power station, wherein the second objective function includes a second power-type energy storage objective function and a second energy-type energy storage objective function; Based on the second planning cost function model of the three-level energy storage power station, the energy storage operation cost model, the thermal power unit operation cost model and other unit operation cost models, a function is constructed to obtain a third objective function corresponding to the three-level energy storage power station, and the third objective function includes a third power-type energy storage objective function and a third energy-type energy storage objective function.

3. The method according to claim 2, characterized in that The first planning cost function model, the energy storage peak regulation benefit function model, the station benefit function model and the station penalty function model of each of the first-level energy storage power stations are respectively constructed, specifically including: Based on the unit electricity investment price of the energy storage power station corresponding to each of the first-level energy storage power stations, the unit power investment price of the energy storage power station, the average operation and maintenance cost per unit electricity of the energy storage power station, the average annual replacement rate of the energy storage power station, the number of cyclic charge and discharge times of the energy storage power station, the energy planning capacity of the energy storage power station, the power planning capacity of the energy storage power station, the minimum operating state of charge of the energy storage power station, and the maximum operating state of charge of the energy storage power station, a function is constructed to obtain a planning cost function model corresponding to each of the first-level energy storage power stations; Based on the charging power of the energy storage power station at different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, the discharging power of the energy storage power station at different preset time periods, the electricity price at each time, the charging conversion efficiency of the energy storage power station at different preset time periods, and the discharging conversion efficiency at different preset time periods, a function is constructed to obtain an energy storage peak regulation benefit function model corresponding to each of the first-level energy storage power stations; Based on the profitable electricity price for the excess power of the station power generation in different preset time periods within the preset time range corresponding to each of the first-level energy storage power stations, the output imbalance penalty coefficient, the relationship variable between the station power generation and the target output, the wind and solar abandonment rate, the target output in different preset time periods, the wind power generation in different preset time periods, the photovoltaic power generation in different preset time periods, the energy storage power station charging power in different preset time periods, and the energy storage power station discharging power in different preset time periods, a function is constructed to obtain a station revenue function model corresponding to each of the first-level energy storage power stations; Based on the penalty fee for the shortfall of the station's power generation power in each different preset time period within the preset time range corresponding to each of the first-level energy storage power stations, the relationship variable between the station's power generation power and the target output, the target output in each different preset time period, the charging power of the energy storage power station in each different preset time period, the discharging power of the energy storage power station in each different preset time period, the wind and solar abandonment rate, the wind power generation power in each different preset time period, and the photovoltaic power generation power in each different preset time period, a function is constructed to obtain a station penalty function model corresponding to each of the first-level energy storage power stations.

4. The method according to claim 1, characterized in that The objective functions are optimized based on the wind and solar output parameters of each of the first-level energy storage power stations within the preset time range and the preset constraints corresponding to the energy storage power stations at each level, and the initial energy storage capacity configuration parameters corresponding to the energy storage power stations at each level are obtained, specifically including: Based on the wind and solar power output parameters of the same first-level energy storage power station within a preset time range, the pre-acquired wind and solar power output prediction parameters, and a number of preset first constraints, the first objective function of the same first-level energy storage power station is optimized to obtain the initial energy storage capacity configuration parameters of the same first-level energy storage power station; Based on the wind and solar output parameters of the plurality of primary energy storage power stations corresponding to the same secondary energy storage power station within a preset time range, the load demand data of the secondary energy storage power station, and the plurality of preset second constraints corresponding to the same secondary energy storage power station, the second objective function of the same secondary energy storage power station is optimized to obtain the initial energy storage capacity configuration parameters of the same secondary energy storage power station; Based on the wind and solar output parameters of several first-level energy storage power stations corresponding to each second-level energy storage power station under the three-level energy storage power station within a preset time range, the load demand data of the power grid to which the three-level energy storage power station belongs, and several preset third constraints corresponding to the same three-level energy storage power station, the third objective function of the same three-level energy storage power station is optimized to obtain the initial energy storage capacity configuration parameters of the three-level energy storage power station.

5. The method according to claim 4, characterized in that The first objective function of the same first-level energy storage power station is optimized based on the wind and solar power output parameters of the same first-level energy storage power station within the preset time range, the pre-acquired wind and solar power output prediction parameters, and a plurality of preset first constraints to obtain the initial energy storage capacity configuration parameters of the same first-level energy storage power station, specifically including: For the same first-level energy storage power station, determine one by one whether the wind and solar power output parameters corresponding to different preset time periods within the preset time range and the output prediction parameters meet the preset first constraint conditions to obtain a determination result; When the judgment result is that any one of the preset first constraint conditions is not satisfied, the current energy storage capacity configuration parameters and the current operation decision parameters of the first-level energy storage power station are adjusted until the wind and solar power output parameters and the output prediction parameters in different preset time periods satisfy the preset first constraint conditions, and the initial energy storage capacity configuration parameters are obtained; Among them, each of the preset first constraint conditions includes peak-shaving and frequency-regulating balance constraints in each preset time period, energy storage power station planning constraints, energy storage power station operation constraints, wind turbine operation constraints, and photovoltaic unit operation constraints.

6. The method according to claim 5, characterized in that The peak-shaving and frequency-shaving calculations are performed based on the net load data of the energy storage power stations at each level to obtain the frequency-shaving power and peak-shaving power of each first-level energy storage power station in different preset time periods within the preset time range, specifically including: Calculating and processing based on the second net load data corresponding to the same secondary energy storage power station and the first net load data of each primary energy storage power station, obtaining the first load increments for each different preset time period corresponding to the same secondary energy storage power station; Calculate and process the third net load data corresponding to the third-level energy storage power station and the second net load data of each of the second-level energy storage power stations corresponding to the third-level energy storage power station to obtain the second load increments corresponding to the third-level energy storage power station in different preset time periods; Peak-shaving and frequency-regulating calculations are performed based on the first load increments, the second load increments, and the first net load data of each primary energy storage power station to obtain the frequency-regulating power and the peak-shaving power of each primary energy storage power station in different preset time periods within the preset time range.

7. A multi-level coordinated energy storage capacity configuration device for an offshore wind farm, characterized in that: include: A function construction module, for constructing functions based on the operation objectives of energy storage power stations at various levels, and obtaining objective functions corresponding to the energy storage power stations at various levels, wherein the objective functions include a first objective function corresponding to a first-level energy storage power station, a second objective function corresponding to a second-level energy storage power station, and a third objective function corresponding to a third-level energy storage power station; the third-level energy storage power station is higher in level than the second-level energy storage power station and the first-level energy storage power station, the second-level energy storage power station is higher in level than the first-level energy storage power station, and the first objective function includes a first power-type energy storage objective function and a first energy-type energy storage objective function; A first optimization module is used to optimize each objective function based on the wind and solar power output parameters of each first-level energy storage power station within a preset time range and preset constraints corresponding to each level of the energy storage power station, so as to obtain initial energy storage capacity configuration parameters corresponding to each level of the energy storage power station; A calculation module, used to perform peak-shaving and frequency-regulating calculations based on the net load data of the energy storage power stations at each level, and obtain the frequency-regulating power and peak-shaving power of each of the first-level energy storage power stations in different preset time periods within the preset time range; The second optimization module is used to optimize the first objective function of the same first-level energy storage power station based on the frequency regulation powers and peak regulation powers corresponding to the same first-level energy storage power station, and obtain the target energy storage capacity configuration parameters of the same first-level energy storage power station, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm, and is specifically used to calculate and process based on the preset frequency regulation reference value, the preset frequency regulation charge fluctuation amplitude, the frequency regulation charge threshold corresponding to each frequency regulation period within the target peak regulation period, and the maximum capacity of the power-type energy storage to obtain the callable peak regulation redundant energy storage capacity for each frequency regulation period; based on each peak regulation redundant energy storage capacity, each peak regulation power and each preset peak regulation constraint condition, the The first power-type energy storage objective function is optimized to obtain power-type energy storage capacity configuration parameters corresponding to each frequency regulation period; based on the peak-shaving initial charge, peak-shaving termination charge, preset maximum peak-shaving charge, and preset minimum peak-shaving charge in the target peak-shaving period, calculation and processing are performed to obtain the frequency regulation redundant energy storage capacity for frequency regulation of the peak-shaving energy storage corresponding to each frequency regulation period in the target peak-shaving period; based on each frequency regulation redundant energy storage capacity, each frequency regulation power, and each preset frequency regulation constraint condition, the first energy-type energy storage objective function is optimized to obtain the target energy storage capacity configuration parameters corresponding to each peak-shaving period, so as to obtain the multi-level coordinated energy storage capacity configuration result of the offshore wind farm.

8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for configuring multi-level coordinated energy storage capacity of an offshore wind farm as described in any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: The method comprises at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for configuring multi-level coordinated energy storage capacity of an offshore wind farm as described in any one of claims 1 to 6 when executing the computer program on the memory.

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