Composite energy storage configuration method for wind power access system based on multi-objective optimization

Abstract

The present application provides a composite energy storage configuration method for a wind power access system based on multi-objective optimization. The method includes: obtaining the wind power output power of the wind power access system; obtaining the optimized modal number and optimized penalty factor based on a heuristic optimization algorithm and the wind power output power; performing modal decomposition on the wind power output power based on the optimized modal number and the optimized penalty factor to obtain a number of modal components; and obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and multi-objective functions. The composite energy storage configuration method for a wind power access system based on multi-objective optimization can improve the efficiency of obtaining the configuration of the energy storage device.

Description

Technical Field

[0001] The present application belongs to the field of wind power systems and relates to a composite energy storage configuration method, and in particular to a composite energy storage configuration method for a wind power access system based on multi-objective optimization. Background Art

[0002] Wind power output has obvious randomness and volatility, and usually requires energy storage to smooth the output power in order to improve the stability and power quality of wind power access system transmission and grid connection. Energy storage can be divided into power-type energy storage and energy-type energy storage according to the charging and discharging characteristics. Different types of energy storage have different energy, power and operating time scales, which can realize smoothing uncertain new energy output, assist the power grid in peak regulation, frequency regulation, voltage regulation and black start, and increase the space for new energy consumption. Therefore, multiple types of composite energy storage are often configured in wind and solar access systems to achieve multiple regulation requirements at different time scales, such as electrochemical-supercapacitor, electrochemical-flywheel or electrochemical-compressed air and other composite energy storage configuration types.

[0003] At present, the composite energy storage configuration method of the wind power access system generally adopts a phased or layered optimization step. Since the calculation of this optimization method is time-consuming, the current composite energy storage configuration method has the problem of low calculation efficiency. Summary of the invention

[0004] The purpose of this application is to provide a composite energy storage configuration method, device, medium and electronic equipment for a wind power access system based on multi-objective optimization, so as to solve the problem of low computational efficiency in the current composite energy storage configuration method.

[0005] In a first aspect, the present application provides a composite energy storage configuration method for a wind power access system based on multi-objective optimization, the method comprising: obtaining the wind power output power of the wind power access system; obtaining an optimized number of modes and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power; performing modal decomposition on the wind power output power based on the optimized number of modes and the optimized penalty factor to obtain a number of modal components; obtaining a power configuration and a capacity configuration of a composite energy storage device in the wind power access system based on the modal components and a multi-objective function, the multi-objective function comprising a function of the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device.

[0006] In the method, by obtaining the optimized number of modes and the optimized penalty factor based on the heuristic optimization algorithm and the wind power output power, and obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and the multi-objective function, the efficiency of obtaining the energy storage device configuration can be improved, and compared with the existing composite energy storage configuration method, the power configuration and the capacity configuration are more accurate, which can further enhance the engineering practical effect of the energy storage device.

[0007] In one embodiment of the present application, a method for obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and the multi-objective function includes: obtaining the boundary component between the high-frequency component and the low-frequency component in the modal component based on the modal component; iteratively solving the multi-objective function based on the boundary component to obtain the power configuration and capacity configuration.

[0008] In one embodiment of the present application, the implementation method of iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration includes: if the process of iteratively solving the multi-objective function based on the demarcation component satisfies the constraint condition, then iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration; if the process of iteratively solving the multi-objective function based on the decomposition component does not satisfy the constraint condition, then obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component to obtain the power configuration and the capacity configuration.

[0009] In one embodiment of the present application, the method for obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component includes:

[0010] S1, based on the updated parameters of the heuristic optimization algorithm, the wind power output power and the heuristic optimization algorithm, obtaining the updated number of optimized modes and the updated optimization penalty factor; S2, based on the updated number of optimized modes and the updated optimization penalty factor, performing modal decomposition on the wind power output power to obtain a number of updated modal components; S3, obtaining the updated boundary components based on the updated modal components; S4, if the process of iteratively solving the multi-objective function based on the updated boundary components satisfies the constraints, then iteratively solving the multi-objective function based on the updated boundary components to obtain the power configuration and the capacity configuration, if the process of iteratively solving the multi-objective function based on the updated boundary components does not satisfy the constraints, then returning to S1, the parameter values ​​of the updated parameters are different during each execution of S1.

[0011] In one embodiment of the present application, the multi-objective function includes a first objective function, a second objective function and a third objective function, the first objective function is a function of the economic cost of the entire life cycle of the composite energy storage device, the second objective function is a function of unbalanced power compensation of the composite energy storage device, the third objective function is a function of power balance of the wind power access system, and the heuristic optimization algorithm is a sparrow search algorithm.

[0012] In one embodiment of the present application, the first objective function is:

[0013] C total =C inv +C om +C loss +C ur +C sd -C res

[0014] Among them, C total is the economic cost of the composite energy storage device throughout its life cycle, C inv is the investment and installation cost of the composite energy storage device, C om is the operation and maintenance cost of the composite energy storage device, C loss is the unit power loss cost of the composite energy storage device, C ur is the replacement cost of the composite energy storage device, C sd is the disposal cost of the composite energy storage device, C res is the residual value recovery value of the composite energy storage device, and the second objective function is:

[0015]

[0016] Where f2 is the second objective function, T FLc is the life cycle of the composite energy storage device, t is time, P ex (t) is the expected power of the composite energy storage device at time t, P com (t) is the charge and discharge power of the composite energy storage device at time t, and the third objective function is:

[0017] P c +P b =P send -P g

[0018] Among them, P c is the compressed air energy storage power of the composite energy storage device, P b is the electrochemical energy storage power of the composite energy storage device, P send is the power generated by the wind power access system, Pg is the grid-connected capacity of the wind power access system.

[0019] In one embodiment of the present application, if the compressed air energy storage power is the compressed air energy storage charging power, the compressed air energy storage charging power is expressed as:

[0020]

[0021] Among them, P c1 (t) is the compressed air energy storage charging power at time t, P k (t) represents the power of the kth modal component at time t, K b represents the order of the boundary component in the modal component, σ cch is the charging parameter of the compressed air energy storage charging power. If the compressed air energy storage power is the compressed air energy storage discharging power, the compressed air energy storage discharging power is expressed as:

[0022]

[0023] Among them, P c2 (t) is the compressed air energy storage discharge power at time t, σ cdis is the discharge parameter of the compressed air energy storage discharge power. If the electrochemical energy storage power is the electrochemical energy storage charging power, the electrochemical energy storage charging power is expressed as:

[0024]

[0025] Among them, P b1 (t) is the electrochemical energy storage charging power at time t, K opt is the last modal component in the modal components, σ bch is the charging parameter of the electrochemical energy storage charging power. If the electrochemical energy storage power is the electrochemical energy storage discharging power, the electrochemical energy storage discharging power is expressed as:

[0026]

[0027] Among them, P b2 (t) is the electrochemical energy storage discharge power at time t, σ bdis is the discharge parameter of the electrochemical energy storage discharge power.

[0028] In a second aspect, the present application provides a composite energy storage configuration device for a wind power access system based on multi-objective optimization, characterized in that the device includes: an output power acquisition module, used to acquire the wind power output power of the wind power access system; a modal number acquisition module, used to acquire an optimized modal number and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power; a modal decomposition module, used to perform modal decomposition on the wind power output power based on the optimized modal number and the optimized penalty factor to obtain a number of modal components; a power acquisition module, used to acquire the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and a multi-objective function, wherein the multi-objective function includes functions regarding the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device.

[0029] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any method described in the first aspect of the present application.

[0030] In a fourth aspect, the present application provides an electronic device, comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing any one of the methods described in the first aspect of the present application when the computer program is called.

[0031] As described above, the composite energy storage configuration method, device, medium and electronic device of the wind power access system based on multi-objective optimization described in the present application have the following beneficial effects:

[0032] In the method, by obtaining the optimized number of modes and the optimized penalty factor based on the heuristic optimization algorithm and the wind power output power, and obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and the multi-objective function, the efficiency of obtaining the energy storage device configuration can be improved, and compared with the existing composite energy storage configuration method, the power configuration and the capacity configuration are more accurate, which can further enhance the engineering practical effect of the energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Shown is a structural schematic diagram of the wind power access system described in an embodiment of the present application.

[0034] Figure 2 Shown is a flow chart of a composite energy storage configuration method for a wind power access system based on multi-objective optimization as described in an embodiment of the present application.

[0035] Figure 3 Shown is a schematic diagram of the modal components described in an embodiment of the present application.

[0036] Figure 4Shown is a schematic diagram of the frequency spectrum of the modal components described in the embodiment of the present application.

[0037] Figure 5a Shown is a graph showing the time variation of the charging and discharging power of the compressed air energy storage in an embodiment of the present application.

[0038] Figure 5b Shown is a graph showing the time variation of the charge and discharge power of the electrochemical energy storage of an embodiment of the present application.

[0039] Figure 6 Shown is a flow chart of an implementation method for obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and multi-objective functions according to an embodiment of the present application.

[0040] Figure 7 Shown is a flowchart of an implementation method of the present application for iteratively solving the multi-objective function based on the boundary component to obtain the power configuration and the capacity configuration.

[0041] Figure 8 A flowchart of an implementation method for obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component according to an embodiment of the present application is shown.

[0042] Fig. 9 Shown is a structural schematic diagram of a composite energy storage configuration device for a wind power access system based on multi-objective optimization in an embodiment of the present application.

[0043] Component number description

[0044] 900 Wind power connection based on multi-objective optimization

[0045] Composite energy storage configuration installed in the system

[0046] Place

[0047] 910 Output power acquisition module

[0048] 920 Modal quantity acquisition module

[0049] 930 Modal Decomposition Module

[0050] 940 Power Acquisition Module

[0051] Steps S11-S14

[0052] Steps S21-S22

[0053] S31-S32 Steps

[0054] Steps S1-S4 DETAILED DESCRIPTION

[0055] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0056] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0057] The technical solutions in the embodiments of the present application are described in detail below in conjunction with the drawings in the embodiments of the present application.

[0058] See also Figure 1 , Figure 1 The schematic diagram of the structure of the wind power access system described in the embodiment of the present application. The wind power access system includes: a wind farm, a composite energy storage device, a collection bus, a tie line, a step-up transformer, a power grid, etc. The composite energy storage device is used to provide electrochemical energy storage and compressed air energy storage. Figure 1 The AC / DC in this article refers to the conversion of alternating current signals into direct current signals. Figure 1 The DC / AC in this case refers to the conversion of a direct current signal into an alternating current signal.

[0059] like Figure 2 As shown, this embodiment provides a composite energy storage configuration method for a wind power access system based on multi-objective optimization, which can be executed by a processor of a computer device, and includes:

[0060] S11, obtaining the wind power output power of the wind power access system.

[0061] Optionally, the wind power access system may refer to a system for accessing the electric energy generated by a wind farm to a power grid. A wind farm is usually composed of a plurality of wind turbines, which generate electric energy by converting wind energy. Then, the electric energy needs to be accessed to the power grid through appropriate power transmission equipment and systems so as to be used by consumers or stored.

[0062] Optionally, the wind power output power may refer to the electric power generated by the wind turbines in the wind power access system, that is, the electric power output from the wind farm to the power grid. The wind power output power may refer to the wind power output power of the wind power access system within a period of time, for example, the wind power output power within a day.

[0063] S12, obtaining an optimized number of modes and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power.

[0064] Optionally, the heuristic optimization algorithm may be a sparrow search algorithm. The implementation method for obtaining the optimized number of modes and the optimized penalty factor based on the heuristic optimization algorithm includes: iteratively processing the predation position of the sparrow population in the sparrow search algorithm to obtain the optimal predation position of the sparrow population. The predation position may be represented by a two-dimensional coordinate vector consisting of the number of modes and the penalty factor, and the optimal predation position includes the optimized number of modes and the optimized penalty factor. The objective function of the sparrow search algorithm may include a correlation coefficient between the decomposed reconstruction power and the wind power output power, the decomposed reconstruction power may refer to the reconstruction power calculated after decomposing the wind power output power according to the number of modes and the penalty factor, and the correlation coefficient may refer to the Pearson correlation coefficient.

[0065] Optionally, iterative processing of the predation position of the sparrow population in the sparrow search algorithm to obtain the optimal predation position of the sparrow population includes: I: initializing the sparrow population; II: calculating the initial fitness values ​​of the individuals of the sparrow population; III: sorting the sparrow population based on the initial fitness values ​​to obtain the perceiver individuals and pursuer individuals in the sparrow population; IV: increasing the evolutionary generation of the sparrow population once; V: calculating the individual fitness of this evolution and the optimal fitness of the sparrow population; VI: updating the position of the perceiver individual and the position of the pursuer individual; VII: some sparrow individuals perform danger monitoring and early warning and determine whether they are aware of the danger. If they are aware of the danger, recalculate the individual fitness of this evolution and the optimal fitness of the sparrow population and return to VI, otherwise go to VIII; VIII: determine whether the evolution conditions are met. If not, return to IV. If the evolution conditions are met, obtain the optimal predation position of the sparrow population. The observer has a higher individual fitness and is responsible for detecting the optimal predation position and range during the predation process; the pursuer has a lower individual fitness and needs to follow the predation direction provided by the observer to find food.

[0066] Alternatively, the position of the observer individual in the sparrow population in the r+1th evolutionary generation is expressed as:

[0067]

[0068] in, represents the position of the n-dimensional variable of the m-th sparrow in the r+1-th evolutionary generation, r represents the r-th evolutionary generation, represents the position of the n-dimensional variable of the m-th sparrow in the r-th evolutionary generation, r max represents the maximum evolutionary generation, η represents a random number in the interval (0, 1], H represents a random number that conforms to the normal distribution, ONE represents an n-dimensional row vector whose elements are all 1, S represents the danger monitoring value, and TH represents the warning threshold.

[0069] Alternatively, the position of the seeker individuals in the sparrow population in the r+1th evolutionary generation is expressed as:

[0070]

[0071] in, represents the position of the n-dimensional variable of the m-th sparrow in the r+1-th evolutionary generation, represents the position with the worst fitness at the rth evolutionary generation, It represents the optimal position with the highest fitness in the rth evolutionary generation, A represents a random number that conforms to the normal distribution, B represents a random matrix between [-1, 1], and D represents the total dimension of the optimization variable.

[0072] Optionally, the position of an individual in the sparrow population is updated to:

[0073]

[0074] in, represents the optimal position with the highest fitness in the current sparrow population, λ is a random number from a compound normal distribution, E is a random number between [-1, 1], and f m represents the fitness of the current sparrow population, f worst represents the worst fitness of the current sparrow population, τ is a smaller number set to avoid the denominator approaching zero, and G can represent a preset evolutionary algebra threshold, which can be flexibly set according to actual conditions. The evolution condition can refer to whether the evolutionary algebra of the current sparrow population does not exceed the preset evolutionary algebra threshold.

[0075] S13, performing modal decomposition on the wind power output power based on the optimized number of modes and the optimized penalty factor to obtain a plurality of modal components.

[0076] Optionally, the modal decomposition may refer to variational modal decomposition, which is a signal decomposition method that can decompose a non-stationary signal into multiple intrinsic mode functions with different frequency modulation and amplitude modulation characteristics. The number of modes refers to the number of modes selected when performing modal decomposition on a signal, and the penalty factor refers to a parameter used to balance the degree of model fitting and the complexity of the model during the modal decomposition process. Please refer to Figure 3 , Figure 3 It can be each modal component after the wind power output power is decomposed. Figure 4 , Figure 4 It can be the frequency spectrum of the modal component. Figure 3 and Figure 4 IMF1, IMF2, etc. refer to modal component 1, modal component 2, etc.

[0077] S14, obtaining a power configuration and a capacity configuration of the composite energy storage device in the wind power access system based on the modal components and a multi-objective function, wherein the multi-objective function includes functions related to the charging and discharging power of the composite energy storage device and the stored energy of the composite energy storage device.

[0078] Optionally, the composite energy storage device is used to provide compressed air energy storage and electrochemical energy storage to the wind power access system. The charging and discharging power may refer to the power of the composite energy storage device when charging or discharging, and the stored energy may refer to the energy stored in the composite energy storage device. The function of the charging and discharging power of the composite energy storage device and the stored energy of the composite energy storage device may specifically refer to the function of the charging and discharging power of the composite energy storage device at different times and the stored energy of the composite energy storage device at different times.

[0079] Optionally, the multi-objective function includes a first objective function, a second objective function and a third objective function, the first objective function is a function for calculating the economic cost of the entire life cycle of the composite energy storage device, the second objective function is a function for calculating the unbalanced power of the wind power access system, the third objective function is a function regarding the power balance of the wind power access system, and the heuristic optimization algorithm is a sparrow search algorithm.

[0080] Optionally, the method for obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal component and the multi-objective function includes: iteratively solving the multi-objective function based on the modal component to obtain the optimal charge and discharge power of the composite energy storage device and the optimal energy storage energy of the composite energy storage device; obtaining the power configuration and the capacity configuration based on the optimal charge and discharge power of the composite energy storage device and the optimal energy storage energy of the composite energy storage device. The optimal charge and discharge power may be composed of the optimal electrochemical energy storage power of the composite energy storage device and the optimal compressed air energy storage power of the composite energy storage device.

[0081] Optionally, the power configuration may refer to the maximum charge and discharge power of the composite energy storage device during operation, and the capacity configuration may refer to the maximum energy storage energy of the composite energy storage device during operation. The optimal charge and discharge power of the composite energy storage device may refer to the optimal charge and discharge power within a period of time, and the optimal energy storage energy of the composite energy storage device may refer to the optimal energy storage energy within a period of time. The power configuration and the capacity configuration can be obtained based on the optimal charge and discharge power within the period of time and the optimal energy storage energy within the period of time. Please refer to Figure 5a and Figure 5b , Figure 5a Indicates the optimal electrochemical energy storage power in the composite energy storage device, the electrochemical energy storage power is configured to be 20MW, and the electrochemical energy storage capacity is configured to be 120MWh, Figure 5b It represents the optimal compressed air energy storage configuration in the composite energy storage device. The compressed air energy storage power configuration is 20MW, the compressed air energy storage capacity configuration is 120MWh, the power configuration of the composite energy storage device is 40MW, and the capacity configuration of the composite energy storage device is 240MWh.

[0082] According to the above description, the method includes: obtaining the wind power output power of the wind power access system; obtaining the optimized number of modes and the optimized penalty factor based on the heuristic optimization algorithm and the wind power output power; performing modal decomposition on the wind power output power based on the optimized number of modes and the optimized penalty factor to obtain a number of modal components; obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and a multi-objective function, wherein the multi-objective function includes functions regarding the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device.

[0083] In the composite energy storage configuration method for the wind power access system based on multi-objective optimization, the optimized number of modes and the optimized penalty factor are obtained based on the heuristic optimization algorithm and the wind power output power, and the power configuration and capacity configuration of the composite energy storage device in the wind power access system are obtained based on the modal components and the multi-objective function. This can improve the efficiency of obtaining the configuration of the energy storage device, and compared with the existing composite energy storage configuration method for the wind power access system based on multi-objective optimization, the power configuration and the capacity configuration are more accurate, which can further enhance the engineering practical effect of the energy storage device.

[0084] like Figure 6 As shown, this embodiment provides a method for obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal component and the multi-objective function, including:

[0085] S21, obtaining a boundary component between a high-frequency component and a low-frequency component in the modal component based on the modal component.

[0086] Optionally, the high-frequency component may refer to a component with a higher frequency in the modal component, and the low-frequency component may refer to a component with a lower frequency in the modal component.

[0087] Optionally, the boundary component may refer to the modal component with the weakest spectral aliasing or the narrowest aliasing bandwidth among the modal components. For example, modal component 4 and modal component 5 have the weakest spectral aliasing, and modal component 4 may be the boundary component.

[0088] S22, iteratively solving the multi-objective function based on the boundary component to obtain the power configuration and capacity configuration.

[0089] Optionally, the first objective function is:

[0090] f1=C total

[0091] C total =C inv +C om +C loss +C ur +C sd -C res

[0092] Among them, C total is the economic cost of the composite energy storage device throughout its life cycle, C inv is the investment and installation cost of the composite energy storage device, C om is the operation and maintenance cost of the composite energy storage device, C loss is the operating loss cost of the composite energy storage device, C uris the replacement cost of the composite energy storage device, C sd is the disposal cost of the composite energy storage device, C res is the residual value recovery value of the composite energy storage device, and the second objective function is:

[0093]

[0094] Where f2 is the second objective function, T FLC is the number of years of the entire life cycle of the composite energy storage device, t represents time, and P ex (t) is the expected power of the composite energy storage device at time t, P com (t) is the charge and discharge power of the composite energy storage device at time t. The life cycle of the composite energy storage device can be flexibly set according to actual conditions. The expected power can refer to the expected charge and discharge power of the composite energy storage device. The charge and discharge power of the composite energy storage device can refer to the actual charge and discharge power of the composite energy storage device. The third objective function is:

[0095] P c (t)+P b (t) = P send (t)-P g

[0096] Among them, P c (t) represents the compressed air energy storage power of the composite energy storage device at time t, P b (t) represents the electrochemical energy storage power of the composite energy storage device at time t, P send is the power generated by the wind power access system at time t, P g is the grid-connected capacity of the wind power access system. The third objective function can also be expressed as: f3 = P c (t)+P b (t)-P send (t)-P g , f3=0.

[0097] Optionally, the charge and discharge power of the composite energy storage device at time t is expressed as:

[0098] P com (t) = P c (t)+P b (t)

[0099] Optionally, the form of performing multi-objective optimization according to the multi-objective function can be expressed as:

[0100] min f=(f1,f2,f3)

[0101] Wherein, f is the multi-objective function, f1 is the first objective function, f2 is the second objective function, f3 is the third objective function, and min f represents the minimum value of the multi-objective function. In addition, f1, f2, and f3 all have the same independent variable P com (t) is omitted here to simplify the representation of the multi-objective function.

[0102] Optionally, the investment and installation cost of the composite energy storage device is expressed as:

[0103]

[0104] Among them, P com (t) The charge and discharge power of the composite energy storage device at time t, E com (t) is the energy storage capacity of the composite energy storage device at time t, h pt is the unit power cost, h et is the unit capacity cost, and k is the investment discount rate. The unit power cost, the unit capacity cost, and the investment discount rate can be set according to actual conditions, and this embodiment does not explicitly limit this.

[0105] Optionally, the operation and maintenance cost of the composite energy storage device is expressed as:

[0106]

[0107] Among them, h pom is the maintenance cost per unit power, h eom The unit power maintenance cost and the unit capacity maintenance cost can be set according to actual conditions, and this embodiment does not explicitly limit this.

[0108] Optionally, the unit power loss cost of the composite energy storage device is expressed as:

[0109]

[0110] Among them, h loss is the unit power loss cost, P loss (t) is the power loss of the composite energy storage device at time t.

[0111] Optionally, the replacement cost of the composite energy storage device is expressed as:

[0112]

[0113] Among them, R rep is the number of times the composite energy storage device is updated and replaced, h ur is the unit replacement cost, E urThe unit power loss cost and the unit replacement cost can be flexibly set according to actual conditions, and this embodiment will not be repeated here.

[0114] Optionally, the waste disposal cost of the composite energy storage device is expressed as:

[0115]

[0116] Among them, h psd is the unit power waste disposal cost, h esd is the unit capacity waste disposal cost, t represents time t, and n represents the nth update replacement. The unit power waste disposal cost and the unit capacity waste disposal cost can be set according to actual conditions, and this embodiment does not explicitly limit this.

[0117] Optionally, the residual value recovery value of the composite energy storage device is expressed as:

[0118]

[0119] Among them, μ res The residual value recovery rate can be flexibly set according to actual conditions, and this embodiment does not explicitly limit this.

[0120] Optionally, if the compressed air energy storage power is the compressed air energy storage charging power, the compressed air energy storage charging power is expressed as:

[0121]

[0122] Among them, P c1 (t) is the compressed air energy storage charging power at time t, P k (t) represents the power of the kth modal component at time t, K b represents the order of the boundary component in the modal component, σ cch is the charging parameter of the compressed air energy storage charging power. If the compressed air energy storage power is the compressed air energy storage discharging power, the compressed air energy storage discharging power is expressed as:

[0123]

[0124] Among them, P c2 (t) is the compressed air energy storage discharge power at time t, σ cdis is the discharge parameter of the compressed air energy storage discharge power. If the electrochemical energy storage power is the electrochemical energy storage charging power, the electrochemical energy storage charging power is expressed as:

[0125]

[0126] Among them, P b1 (t) is the electrochemical energy storage charging power at time t, K opt is the last modal component among the modal components, σ bch is the charging parameter of the electrochemical energy storage charging power. If the electrochemical energy storage power is the electrochemical energy storage discharging power, the electrochemical energy storage discharging power is expressed as:

[0127]

[0128] Among them, P b2 (t) is the electrochemical energy storage discharge power at time t, σ bdis is the discharge parameter of the electrochemical energy storage discharge power.

[0129] Optionally, the modal components may be arranged in ascending order according to the frequencies corresponding to the maximum amplitude values ​​of the modal components. For example, the first modal component may be the modal component with the smallest frequency corresponding to the maximum amplitude value among the modal components, and the last modal component may be the modal component with the largest frequency corresponding to the maximum amplitude value among the modal components.

[0130] like Figure 7 As shown, this embodiment provides an implementation method for iteratively solving the multi-objective function based on the boundary component to obtain the power configuration and the capacity configuration, including:

[0131] S31: If the process of iteratively solving the multi-objective function based on the demarcation component satisfies the constraint condition, then iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration.

[0132] S32: If the process of iteratively solving the multi-objective function based on the decomposition components does not satisfy the constraint conditions, an updated boundary component is obtained and the multi-objective function is iteratively solved based on the updated boundary component to obtain the power configuration and the capacity configuration.

[0133] Optionally, the constraint condition includes a first constraint condition, a second constraint condition, a third constraint condition and a fourth constraint condition, and the first constraint condition is:

[0134]

[0135] Among them, SOC com (t+1) represents the charge state of the composite energy storage device at time t+1, SOC com (t) represents the charge state of the composite energy storage device at time t, E comN [SOCcom (t+1)-SOC com (t)] represents the energy change of the composite energy storage device during the period from the charge state at time t to the charge state at time t+1, σ ch represents the charging coefficient of the composite energy storage device, σ dis The charging coefficient of the composite energy storage device and the discharging coefficient of the composite energy storage device can be flexibly set according to actual conditions, and this embodiment will not be repeated here.

[0136] Among them, E comN The total capacity of the composite energy storage device in all sampling periods can be expressed as:

[0137] E comN =ΔE com，max / ΔSO C com，max

[0138] ΔE com，max =maxE com，all -minE com，all

[0139] ΔSOC com，max =maxSOC com，all -minSOC com，all

[0140] SOC com (t-1)-SOC com (t) = E com (t) / S com (t)

[0141]

[0142] Among them, ΔE com，max represents the maximum energy fluctuation of the composite energy storage device in all sampling periods, maxE com，all Indicates the energy storage capacity of the composite energy storage device in the entire sampling period, minE com，all Indicates the minimum energy storage capacity of the composite energy storage device in all sampling periods, ΔSOC com，max Indicates the maximum charge state fluctuation of the composite energy storage device during the entire sampling period, maxSOC com，all Indicates the maximum charge state of the composite energy storage device during the entire sampling period, minSOC com，all Indicates the minimum charge state of the composite energy storage device during the entire sampling period, SOC com (t-1) represents the charge state of the composite energy storage device at time t-1, SOC com(t) represents the charge device of the composite energy storage device at time t, S com (t) represents the charge and discharge state of the energy storage device at time t, E com (T) is the energy storage capacity of the composite energy storage device at time T, E com0 is the initial energy of the composite energy storage device, ΔT is the charge and discharge time of the composite energy storage device, P com (t) represents the charge and discharge power of the composite energy storage device at time t.

[0143] Optionally, the second constraint condition is:

[0144] t i+1 -t i ≥T rec

[0145] Among them, t i+1 is the time for the composite energy storage device to be adjusted for the i+1th time, t i is the time for the i-th adjustment of the composite energy storage device, T rec The adjustment time can be flexibly set according to actual conditions, and this embodiment will not be repeated here.

[0146] Optionally, the third constraint condition is:

[0147]

[0148] Among them, q bt represents the charge and discharge state of the electrochemical energy storage at time t. If the charge and discharge state of the electrochemical energy storage changes, then q bt is 1, otherwise it is 0, q ct represents the charge and discharge state of the compressed air energy storage at time t. If the charge and discharge state of the compressed air energy storage changes, then q ct is 1, otherwise it is 0, β is the number of charge and discharge times of the composite energy storage device, T FNL t=1 may represent the time when the composite energy storage device stops working, and β may represent the number of charge and discharge times of the composite energy storage device from the time t=1 to the time when the composite energy storage device stops working.

[0149] Optionally, the fourth constraint condition is:

[0150]

[0151] Among them, E b The electrochemical energy storage capacity value in the composite energy storage device, E c can be the compressed air energy storage capacity value in the composite energy storage device, E bmin is the minimum electrochemical energy storage capacity, E bmaxis the maximum value of electrochemical energy storage capacity, E cmin is the minimum value of compressed air energy storage capacity, E cmax The minimum value of the electrochemical energy storage capacity, the maximum value of the electrochemical energy storage capacity, the minimum value of the compressed air energy storage capacity, and the maximum value of the compressed air energy storage capacity can all be flexibly set according to actual conditions, and this embodiment will not be repeated here.

[0152] See also Figure 8 This embodiment provides a method for obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component, including:

[0153] S1, based on the update parameters of the heuristic optimization algorithm, the wind power output power and the heuristic optimization algorithm, obtaining an updated number of optimization modes and an updated optimization penalty factor.

[0154] Optionally, the update parameter may refer to the population size, evolutionary generations, and parameter limits in the heuristic optimization algorithm. Since the heuristic optimization algorithm is affected by the update parameter, the updated number of optimization modes and the updated optimization penalty factor may also be different from the number of optimization modes and the optimization penalty factor. For example, the number of optimization modes is 7, and the updated number of optimization modes is 8.

[0155] S2: performing modal decomposition on the wind power output power based on the updated number of optimized modes and the updated optimized penalty factor to obtain a plurality of updated modal components.

[0156] S3, obtaining the updated boundary component based on the updated modal component.

[0157] Optionally, the updated boundary component may refer to a modal component with the weakest spectral aliasing degree or the narrowest aliasing bandwidth among the updated modal components.

[0158] S4: If the process of iteratively solving the multi-objective function based on the updated boundary component satisfies the constraint condition, the multi-objective function is iteratively solved based on the updated boundary component to obtain the power configuration and the capacity configuration; if the process of iteratively solving the multi-objective function based on the updated boundary component does not satisfy the constraint condition, return to S1, and the parameter value of the updated parameter is different during each execution of S1.

[0159] Optionally, the different parameter values ​​of the update parameters during each execution of S1 may mean that during each execution of S1, the parameter values ​​of the update parameters are different from the parameter values ​​of the update parameters during the historical execution of S1. For example, S1 is executed 3 times, the population size in the update parameters during the first execution is 8, the population size in the update parameters during the second execution is 9, and the population size in the update parameters during the third execution is 10.

[0160] The protection scope of the composite energy storage configuration method for a wind power access system based on multi-objective optimization described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0161] See also Fig. 9 This embodiment provides a composite energy storage configuration device for a wind power access system based on multi-objective optimization, the device comprising:

[0162] The output power acquisition module 900 is used to acquire the wind power output power of the wind power access system;

[0163] A modal quantity acquisition module 910, configured to acquire an optimized modal quantity and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power;

[0164] A modal decomposition module 920, configured to perform modal decomposition on the wind power output power based on the optimized modal number and the optimized penalty factor to obtain a plurality of modal components;

[0165] The power acquisition module 930 is used to acquire the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and the multi-objective function, wherein the multi-objective function includes functions related to the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device.

[0166] According to the above description, in the device, by obtaining the optimized number of modes and the optimized penalty factor based on the heuristic optimization algorithm and the wind power output power, and obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal components and the multi-objective function, the efficiency of obtaining the configuration of the composite energy storage device can be improved, and compared with the existing configuration device, the power configuration and the capacity configuration are more accurate, which can further enhance the engineering practical effect of the configuration device.

[0167] In the several embodiments provided in the present application, it should be understood that the disclosed device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0168] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0169] Those of ordinary skill in the art should further appreciate that the units and steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0170] This embodiment provides an electronic device, the electronic device comprising a memory storing a computer program; a processor connected to the memory for communication, and executing the computer program when the computer program is called. Figure 2 The method.

[0171] The present application embodiment also provides a computer-readable storage medium. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be completed by a program to instruct the processor, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state hard disk, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state hard disk (SSD)), etc.

[0172] The present application embodiment may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer or data center.

[0173] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product may be a software installation package, and when the above method is required, the computer program product may be downloaded and executed on a computer.

[0174] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0175] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A composite energy storage configuration method for a wind power access system based on multi-objective optimization, characterized in that: The method comprises: Obtain wind power output power of the wind power access system; Obtaining an optimized number of modes and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power; Performing modal decomposition on the wind power output power based on the optimized modal number and the optimized penalty factor to obtain a plurality of modal components; The power configuration and capacity configuration of the composite energy storage device in the wind power access system are obtained based on the modal component and the multi-objective function, the multi-objective function includes functions about the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device, and the implementation method of obtaining the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal component and the multi-objective function includes: obtaining the boundary component between the high-frequency component and the low-frequency component in the modal component based on the modal component; iteratively solving the multi-objective function based on the boundary component to obtain the power configuration and the capacity configuration; The implementation method of iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration includes: if the process of iteratively solving the multi-objective function based on the demarcation component satisfies the constraint condition, then iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration; if the process of iteratively solving the multi-objective function based on the demarcation component does not satisfy the constraint condition, then obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component to obtain the power configuration and the capacity configuration; The implementation method for obtaining updated boundary components and iteratively solving the multi-objective function based on the updated boundary components includes: S1, based on the updated parameters of the heuristic optimization algorithm, the wind power output power and the heuristic optimization algorithm, obtaining the updated number of optimized modes and the updated optimization penalty factor; S2, based on the updated number of optimized modes and the updated optimization penalty factor, modal decomposition of the wind power output power to obtain a number of updated modal components; S3, based on the updated modal components, obtaining the updated boundary components; S4, if the process of iteratively solving the multi-objective function based on the updated boundary components satisfies the constraints, then iteratively solving the multi-objective function based on the updated boundary components to obtain the power configuration and the capacity configuration, if the process of iteratively solving the multi-objective function based on the updated boundary components does not satisfy the constraints, then returning to S1, the parameter value of the updated parameter is different during each execution of S1; The multi-objective function includes a first objective function, a second objective function and a third objective function, wherein the first objective function is a function for calculating the economic cost of the entire life cycle of the composite energy storage device, the second objective function is a function for calculating the unbalanced power of the wind power access system, and the third objective function is a function for the power balance of the wind power access system. The heuristic optimization algorithm is a sparrow search algorithm; the first objective function is: f1=C total C total =C inv +C om +C loss +C ur +C sd -C res Among them, C total is the economic cost of the composite energy storage device throughout its life cycle, C inv is the investment and installation cost of the composite energy storage device, C om is the operation and maintenance cost of the composite energy storage device, C loss is the operating loss cost of the composite energy storage device, C ur is the replacement cost of the composite energy storage device, C sd is the disposal cost of the composite energy storage device, C res is the residual value recovery value of the composite energy storage device, and the second objective function is: Where f2 is the second objective function, T FLC is the life cycle of the composite energy storage device, t is time, P ex (t) is the expected power of the composite energy storage device at time t, P com (t) is the charge and discharge power of the composite energy storage device at time t, and the third objective function is: P c +P b =P send -P g Among them, P c is the compressed air energy storage power of the composite energy storage device, P b is the electrochemical energy storage power of the composite energy storage device, P send is the power generated by the wind power access system, P g is the grid-connected capacity of the wind power access system; If the compressed air energy storage power is the compressed air energy storage charging power, then the compressed air energy storage charging power is expressed as: Among them, P c1 (t) is the compressed air energy storage charging power at time t, P k (t) represents the power of the kth modal component at time t, K b represents the order of the boundary component in the modal component, σ cch is the charging parameter of the compressed air energy storage charging power. If the compressed air energy storage power is the compressed air energy storage discharging power, the compressed air energy storage discharging power is expressed as: Among them, P c2 (t) is the compressed air energy storage discharge power at time t, σ cdis is the discharge parameter of the compressed air energy storage discharge power. If the electrochemical energy storage power is the electrochemical energy storage charging power, the electrochemical energy storage charging power is expressed as: Among them, P b1 (t) is the electrochemical energy storage charging power at time t, K opt is the last modal component in the modal components, σ bch is the charging parameter of the electrochemical energy storage charging power. If the electrochemical energy storage power is the electrochemical energy storage discharging power, the electrochemical energy storage discharging power is expressed as: Among them, P b2 (t) is the electrochemical energy storage discharge power at time t, σ bdis is the discharge parameter of the electrochemical energy storage discharge power.

2. A composite energy storage configuration device for a wind power access system based on multi-objective optimization, characterized in that: The device comprises: An output power acquisition module is used to acquire the wind power output power of the wind power access system; A modal quantity acquisition module, used for acquiring an optimized modal quantity and an optimized penalty factor based on a heuristic optimization algorithm and the wind power output power; A modal decomposition module, configured to perform modal decomposition on the wind power output power based on the optimized modal number and the optimized penalty factor to obtain a plurality of modal components; A power acquisition module, used for acquiring the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal component and the multi-objective function, wherein the multi-objective function includes functions about the charging and discharging power of the composite energy storage device and the energy storage energy of the composite energy storage device, and the implementation method of acquiring the power configuration and capacity configuration of the composite energy storage device in the wind power access system based on the modal component and the multi-objective function includes: acquiring the boundary component between the high-frequency component and the low-frequency component in the modal component based on the modal component; iteratively solving the multi-objective function based on the boundary component to acquire the power configuration and the capacity configuration; The implementation method of iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration includes: if the process of iteratively solving the multi-objective function based on the demarcation component satisfies the constraint condition, then iteratively solving the multi-objective function based on the demarcation component to obtain the power configuration and the capacity configuration; if the process of iteratively solving the multi-objective function based on the demarcation component does not satisfy the constraint condition, then obtaining an updated demarcation component and iteratively solving the multi-objective function based on the updated demarcation component to obtain the power configuration and the capacity configuration; The implementation method for obtaining updated boundary components and iteratively solving the multi-objective function based on the updated boundary components includes: S1, based on the updated parameters of the heuristic optimization algorithm, the wind power output power and the heuristic optimization algorithm, obtaining the updated number of optimized modes and the updated optimization penalty factor; S2, based on the updated number of optimized modes and the updated optimization penalty factor, modal decomposition of the wind power output power to obtain a number of updated modal components; S3, based on the updated modal components, obtaining the updated boundary components; S4, if the process of iteratively solving the multi-objective function based on the updated boundary components satisfies the constraints, then iteratively solving the multi-objective function based on the updated boundary components to obtain the power configuration and the capacity configuration, if the process of iteratively solving the multi-objective function based on the updated boundary components does not satisfy the constraints, then returning to S1, the parameter value of the updated parameter is different during each execution of S1; The multi-objective function includes a first objective function, a second objective function and a third objective function, wherein the first objective function is a function for calculating the economic cost of the entire life cycle of the composite energy storage device, the second objective function is a function for calculating the unbalanced power of the wind power access system, the third objective function is a function regarding the power balance of the wind power access system, and the heuristic optimization algorithm is a sparrow search algorithm; The first objective function is: f1=C total C total =C inv +C om +C loss +C ur +C sd -C res Among them, C total is the economic cost of the composite energy storage device throughout its life cycle, C inv is the investment and installation cost of the composite energy storage device, C om is the operation and maintenance cost of the composite energy storage device, C loss is the operating loss cost of the composite energy storage device, C ur is the replacement cost of the composite energy storage device, C sd is the disposal cost of the composite energy storage device, C res is the residual value recovery value of the composite energy storage device, and the second objective function is: Where f2 is the second objective function, T FLC is the life cycle of the composite energy storage device, t is time, P ex (t) is the expected power of the composite energy storage device at time t, P com (t) is the charge and discharge power of the composite energy storage device at time t, and the third objective function is: P c +P b =P send -P g Among them, P c is the compressed air energy storage power of the composite energy storage device, P b is the electrochemical energy storage power of the composite energy storage device, P send is the power generated by the wind power access system, P g is the grid-connected capacity of the wind power access system; If the compressed air energy storage power is the compressed air energy storage charging power, then the compressed air energy storage charging power is expressed as: Among them, P c1 (t) is the compressed air energy storage charging power at time t, P k (t) represents the power of the kth modal component at time t, K b represents the order of the boundary component in the modal component, σ cch is the charging parameter of the compressed air energy storage charging power. If the compressed air energy storage power is the compressed air energy storage discharging power, the compressed air energy storage discharging power is expressed as: Among them, P c2 (t) is the compressed air energy storage discharge power at time t, σ cdis is the discharge parameter of the compressed air energy storage discharge power. If the electrochemical energy storage power is the electrochemical energy storage charging power, the electrochemical energy storage charging power is expressed as: Among them, P b1 (t) is the electrochemical energy storage charging power at time t, K opt is the last modal component in the modal components, σ bch is the charging parameter of the electrochemical energy storage charging power. If the electrochemical energy storage power is the electrochemical energy storage discharging power, the electrochemical energy storage discharging power is expressed as: Among them, P b2 (t) is the electrochemical energy storage discharge power at time t, σ bdis is the discharge parameter of the electrochemical energy storage discharge power.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 1 is implemented.

4. An electronic device, characterized in that: The electronic device comprises: A memory storing a computer program; A processor is communicatively connected to the memory and executes the method of claim 1 when calling the computer program.

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