Energy storage optimal configuration method, device and equipment for wind power generation system

Abstract

The invention relates to the technical field of wind power generation, in particular to an energy storage optimal configuration method, device and equipment for a wind power generation system, and the method comprises the steps: building a double-layer configuration optimization model according to power grid side power flow constraint and grid-connected network loss and considering the configuration capacity and charge state of the wind power generation energy storage system, and improving the wind energy absorption rate. Secondly, the genetic algorithm is used to optimize the energy storage configuration position, power and capacity of the outer layer, so that the operation cost of the system is minimized, the whale optimization algorithm is used to optimize the energy storage charging and discharging strategy of the inner layer, the global search capability and the capability of jumping out of local optimum are improved, the parameter sensitivity is reduced, and the investment operation cost is further reduced. Therefore, the problems that a traditional single-layer energy storage optimization strategy is large in calculation amount and low in optimization result efficiency at present are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, device and equipment for optimizing energy storage configuration of a wind power generation system. Background Art

[0003] Due to the randomness and intermittency of wind speed, the power output of the power generation system fluctuates greatly and the power quality is poor. The impact power output characteristics of wind power generation devices also bring challenges to their connection to the power grid. Energy storage allows electric energy to be shifted in time, and has the characteristics of rapid power control and flexible energy throughput. It is an effective means of shock suppression and provides convenient conditions for solving the current problems of wind power generation connection and consumption. However, the construction and operation of energy storage systems require a lot of manpower and material resources, and cost is one of the pain points that restrict the commercial application of energy storage. Research on the optimal configuration of energy storage systems will not only help improve the consumption of wind power, but also have important significance for the safe and stable operation of the power grid.

[0004] The randomness and intermittency of wind power generation pose challenges to grid connection, resulting in low wave energy absorption rate and reduced resource utilization. Traditional single-layer energy storage optimization strategies have large computational complexity and the optimization results are difficult to maximize benefits. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method, device and equipment for optimizing energy storage configuration of a wind power generation system, so as to overcome the problems of large calculation amount and low efficiency of optimization results in the current traditional single-layer energy storage optimization strategy.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for optimizing energy storage configuration of a wind power generation system, comprising:

[0008] Acquiring system parameters of a wind power generation system, and configuring an objective function for the wind power generation system according to the system parameters;

[0009] Determining the inherent condition limitations and wind energy grid-connected absorption capacity of the wind power generation system according to the system parameters of the wind power generation system;

[0010] According to the constraints of the energy storage system itself, the wind energy grid-connected absorption capacity and the safety of the grid, setting system constraints for the wind power generation system;

[0011] According to the objective function of the wind power generation system and the system constraints of the wind power generation system, a two-layer optimization model is established for the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

[0012] Furthermore, in the above method, configuring an objective function for the wind power generation system according to the system parameters includes:

[0013] Determine a calculation function for the total annual cost of the energy storage system based on the network loss cost of the wind power generation system, the reactive power cost provided by the wind power generation device, the energy storage charging and discharging cost, and the energy storage cost;

[0014] Determine the energy loss cost function of the energy storage system when storing energy according to the grid loss power of the wind power generation system, the number of days the energy storage system interacts with the grid, the sampling time interval and the electricity price;

[0015] Determine the reactive loss function of the energy storage system when it is running according to the reactive power provided by the wind power generation device of the wind power generation system, the sampling time interval and the number of days the energy storage system interacts with the power grid;

[0016] Determine the charging and discharging costs of the energy storage system according to the energy storage charging power, energy storage discharging power, the number of days the energy storage system interacts with the power grid, the sampling time interval and the electricity price of the wind power generation system;

[0017] Determine the investment and construction cost function of the wind power generation system according to the number of energy storage installations, unit power cost, configured power, unit capacity cost, energy storage capacity, discount rate and service life of the wind power generation system;

[0018] Determining an operation and maintenance cost function of the wind power generation system according to the operation and maintenance cost per unit power generation of the wind power generation system and the annual discharge of energy storage;

[0019] The energy storage cost function of the wind power generation system is determined based on the investment and construction cost function and the operation and maintenance cost function of the wind power generation system.

[0020] Furthermore, in the method described above, the system constraints include: node voltage constraints, node power balance constraints, branch active power constraints, wind power generation device output constraints, energy storage charge state constraints, energy storage charge state continuity constraints, energy storage power constraints and energy storage charging and discharging constraints.

[0021] Furthermore, the above method further includes:

[0022] When the two-layer optimization model is running, the genetic algorithm and the whale optimization algorithm are initialized;

[0023] Use Yalmip and Cplex toolboxes to perform power flow calculations and evaluate system cost adaptability;

[0024] Determine whether the whale optimization algorithm has reached the first preset number of iterations. If not, generate a new whale optimization algorithm population for iteration;

[0025] If yes, then evaluate the adaptability of the energy storage configuration and determine whether the whale optimization algorithm has reached the second preset number of iterations. If yes, terminate the operation;

[0026] If not, a new genetic algorithm population is generated and iterated.

[0027] In a second aspect, the present application provides a wind power generation system energy storage optimization configuration device, comprising:

[0028] A parameter acquisition module, used to acquire system parameters of the wind power generation system;

[0029] An objective function configuration module, configured to configure an objective function for the wind power generation system according to the system parameters;

[0030] A constraint setting module, used to determine the self-condition constraints and wind energy grid-connected absorption capacity of the wind power generation system according to the system parameters of the wind power generation system, and set system constraints for the wind power generation system according to the self-condition constraints of the wind power generation system, the wind energy grid-connected absorption capacity and grid-side security;

[0031] A double-layer optimization model establishment module is used to establish a double-layer optimization model for the wind power generation system according to the objective function of the wind power generation system and the system constraints of the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

[0032] In a third aspect, the present application provides a wind power generation system energy storage optimization configuration device, including a processor and a memory, wherein the processor is connected to the memory:

[0033] Wherein, the processor is used to call and execute the program stored in the memory;

[0034] The memory is used to store the program, and the program is at least used to execute any of the above methods for optimizing energy storage configuration of a wind power generation system.

[0035] The beneficial effects of the present invention are:

[0036] This application first obtains the system parameters of the wind power generation system, and configures the objective function for the wind power generation system according to the system parameters, determines the self-conditional restrictions of the wind power generation system and the wind energy grid-connected absorption capacity, and sets system constraints for the wind power generation system according to the self-conditional restrictions of the wind power generation system, the wind energy grid-connected absorption capacity and the grid-side security, and establishes a double-layer optimization model for the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration position, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy. In this application, according to the grid-side flow constraints and grid-connected network losses, and considering the configuration capacity and charge state of the wind power generation energy storage system, a double-layer configuration optimization model is established to improve the wind energy absorption rate. Secondly, the genetic algorithm is used to optimize the outer energy storage configuration position, power and capacity to minimize the system operation cost, and the inner layer uses the whale optimization algorithm to optimize the energy storage charging and discharging strategy, improve the global search and jump out of the local optimum ability, and reduce the parameter sensitivity, further reduce the investment and operation cost, thereby solving the current traditional single-layer energy storage optimization strategy, the problem of large calculation amount and low efficiency of optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flow chart provided by an embodiment of a method for optimizing energy storage configuration of a wind power generation system of the present invention;

[0039] Figure 2 It is a double-layer optimization model operation flow chart provided by an embodiment of a method for optimizing energy storage configuration of a wind power generation system of the present invention;

[0040] Figure 3 It is a structural schematic diagram provided by an embodiment of a wind power generation system energy storage optimization configuration device of the present invention;

[0041] Figure 4 It is a structural schematic diagram provided by an embodiment of a wind power generation system energy storage optimization configuration device of the present invention. DETAILED DESCRIPTION

[0042] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0043] Figure 1 This is a flow chart provided by an embodiment of a method for optimizing energy storage configuration of a wind power generation system of the present invention. Figure 1 , this embodiment may include the following steps:

[0044] S1. Obtain system parameters of the wind power generation system, and configure an objective function for the wind power generation system according to the system parameters.

[0045] S2. According to the system parameters of the wind power generation system, determine the inherent condition limitations of the wind power generation system and the wind energy grid-connected absorption capacity.

[0046] S3. According to the inherent condition limitations of the wind power generation system, the wind energy grid-connected absorption capacity and the safety of the power grid, system constraints are set for the wind power generation system.

[0047] S4. According to the objective function of the wind power generation system and the system constraints of the wind power generation system, a two-layer optimization model is established for the wind power generation system. The outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

[0048] It can be understood that the present embodiment first obtains the system parameters of the wind power generation system, and configures the objective function for the wind power generation system according to the system parameters, determines the self-conditional constraints of the wind power generation system and the wind energy grid-connected absorption capacity, and sets the system constraints for the wind power generation system according to the self-conditional constraints of the wind power generation system, the wind energy grid-connected absorption capacity and the grid-side security, and establishes a double-layer optimization model for the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration position, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy. In the present embodiment, according to the grid-side flow constraints and grid-connected network losses, and considering the configuration capacity and charge state of the wind power generation energy storage system, a double-layer configuration optimization model is established to improve the wind energy absorption rate. Secondly, genetic algorithms are used to optimize the outer energy storage configuration location, power and capacity to minimize the system operating cost. The inner layer uses the whale optimization algorithm to optimize the energy storage charging and discharging strategy, improve the global search and escape from the local optimum, and reduce parameter sensitivity, further reducing investment and operating costs, thus solving the current traditional single-layer energy storage optimization strategy with large computational complexity and low optimization result efficiency.

[0049] Preferably, configuring an objective function for the wind power generation system according to the system parameters comprises:

[0050] Determine the total annual cost calculation function of the energy storage system based on the network loss cost of the wind power generation system, the reactive power cost provided by the wind power generation device, the energy storage charging and discharging cost, and the energy storage cost;

[0051] Determine the energy loss cost function of the energy storage system when storing energy based on the grid loss power of the wind power generation system, the number of days the energy storage system interacts with the grid, the sampling time interval and the electricity price;

[0052] Determine the reactive power loss function of the energy storage system when it is in operation based on the reactive power provided by the wind power generation device of the wind power generation system, the sampling time interval, and the number of days the energy storage system interacts with the power grid;

[0053] Determine the energy storage system charging and discharging loss cost function based on the energy storage charging power, energy storage discharging power, number of days the energy storage system interacts with the grid, sampling time interval, and electricity price of the wind power generation system;

[0054] Determine the investment and construction cost function of the wind power generation system based on the number of energy storage installations, unit power cost, configured power, unit capacity cost, energy storage capacity, discount rate and service life of the wind power generation system;

[0055] Determine the operation and maintenance cost function of the wind power generation system based on the unit power generation operation and maintenance cost of the wind power generation system and the annual discharge of energy storage;

[0056] The energy storage cost function of the wind power generation system is determined based on the investment and construction cost function and the operation and maintenance cost function of the wind power generation system.

[0057] It is understandable that in order to minimize the investment and operation costs of the energy storage system, the grid loss, reactive power provided by the wind turbine, energy storage charging and discharging, and the cost of the energy storage itself are considered as a whole to constitute the total cost of the energy storage system. The calculation function of the annual total cost of the energy storage system is specifically shown in formula (1):

[0058] L=C LOSS +C W +C PB +C ES (1)

[0059] Where, L is the total annual cost of the energy storage system; C LOSS C is the network loss cost; W Reactive power costs provided to wind power generation devices; C PB C is the energy storage charging and discharging cost; ES The cost of energy storage.

[0060] The energy storage system interacts frequently with the power grid during operation, and the energy loss caused is difficult to avoid. The energy loss cost function of the energy storage system during energy storage is specifically shown in formula (2):

[0061]

[0062] Where P LOSS (i) is the power loss of the power grid; Δt is the sampling time interval; R(i) is the electricity price; T is the number of days that the energy storage system interacts with the power grid.

[0063] The operation of the energy storage system has a certain reactive power loss, so it needs to be obtained from the wind power generation device. The reactive power loss function of the energy storage system during operation is shown in formula (3):

[0064]

[0065] Where P w (i) Reactive power provided by wind turbines.

[0066] The interaction between the energy storage system and the power grid should reduce the charging cost as much as possible and increase the discharging cost to meet the economic benefits. The specific cost function of the energy storage system charging and discharging loss is shown in formula (4):

[0067]

[0068] Where P ch (i) is the charging power; P dis (i) is the discharge power.

[0069] Usually, a huge amount of money is required to build the energy storage system in the early stage. After operation, post-maintenance is also essential. The investment and construction cost function, operation and maintenance cost function and energy storage cost function of the wind power generation system are specifically shown in formula (5):

[0070]

[0071] In the formula, C cons ES C is the investment and construction cost; run ES C is the operation and maintenance cost; p is the unit power cost; P es is the configuration power; C E is the unit capacity cost; E capa is the capacity; ρ is the discount rate; n is the service life; m is the number of energy storage installations; C run is the operation and maintenance cost per unit of power generation; Y is the annual discharge of energy storage.

[0072] Preferably, the system constraints include: node voltage constraints, node power balance constraints, branch active power constraints, wind power generation device output constraints, energy storage charge state constraints, energy storage charge state continuity constraints, energy storage power constraints and energy storage charging and discharging constraints.

[0073] It is understandable that when the energy storage system interacts with the power grid, it is necessary not only to consider the energy storage's own conditional constraints, but also to focus on the grid side constraints. Therefore, the present invention comprehensively considers the constraints such as the power balance constraints of the grid nodes, the output constraints of the wind turbines, the node voltage amplitude constraints, the branch active power constraints, the energy storage charging and discharging power constraints, and the state of charge constraints.

[0074] (1) The node voltage constraint is:

[0075] U i,min ≤U i,t ≤U i,max (6)

[0076] Where U i,min , U i,max and U i,t are the lowest and highest voltages of node i and the actual voltage at time t respectively.

[0077] (2) The node power balance constraint is:

[0078]

[0079] Where P it , Q it are the active power and reactive power of node i at time t; U it , U jt are the voltages of nodes i and j at time t; δ it ,δ jt are the phase angles of nodes i and j at time t; G ij , B ij are the real and imaginary parts of the i-th row and j-th column of the node admittance matrix respectively; N is the number of nodes.

[0080] (3) The branch active power constraint is:

[0081] -P l,max ≤P l,t ≤P l,max (8)

[0082] Where P l,max is the maximum active power of branch l; P l,t is the actual active power of branch l at time t.

[0083] (4) The output constraints of wind power generation devices are:

[0084] Pw,min ≤P w,t ≤P w,max (9)

[0085] Where P w,min , P w,max and P w,t They are the minimum output, maximum output and actual output of the wind power generation device at time t respectively.

[0086] (5) The energy storage charge state constraint is:

[0087] S bess,min ≤S bess (i)≤S bess,max (10)

[0088] In the formula, S bess,min and S bess,max are the minimum and maximum charge states of the energy storage system, respectively.

[0089] (6) The continuity constraint of energy storage charge state is:

[0090]

[0091] In the formula, S oc,x,0 and S oc,x,t are the charge states of the energy storage system x at the initial time and time t respectively; P ch,x and P dis,x are the charging and discharging power of the energy storage system x respectively; η ch,x and η dis,x are the charging and discharging efficiency of the energy storage system x; E es,x is the rated capacity of the energy storage system x.

[0092] (7) The energy storage power constraint is:

[0093] P ES,i,t =x i (P dis,i,t -P ch,i,t ) (12)

[0094] Where P ES,i,t is the actual charging and discharging power of the energy storage at node i at time t; i is a state variable, 1 means connected to energy storage, otherwise not connected; P dis,i,t P is the discharge power of energy storage node i at time t; ch,i,t The charging power of node i at time t.

[0095] (8) Energy storage charging and discharging constraints are:

[0096]

[0097] Where Pess is the rated power of the energy storage connected to the i-th node.

[0098] Preferably, it also includes:

[0099] When the two-layer optimization model is running, the population of the genetic algorithm and the whale optimization algorithm are initialized;

[0100] Use Yalmip and Cplex toolboxes to perform power flow calculations and evaluate system cost adaptability;

[0101] Determine whether the whale optimization algorithm has reached the first preset number of iterations. If not, generate a new whale optimization algorithm population for iteration;

[0102] If yes, then evaluate the adaptability of the energy storage configuration and determine whether the whale optimization algorithm has reached the second preset number of iterations. If yes, terminate the operation;

[0103] If not, a new genetic algorithm population is generated and iterated.

[0104] It is understandable that in order to maximize the benefits of the energy storage system, it is necessary to coordinate the design of the energy storage access location, capacity, daily operation strategy, etc., which involves many constraints such as power grid trends and the limitations of the energy storage itself. Therefore, the optimal configuration of energy storage in wind power generation systems is a nonlinear multi-objective problem that requires heavy calculations. Here, a two-layer optimization model is designed to calculate the optimal strategy using an intelligent optimization algorithm. The outer layer optimizes the energy storage access nodes and capacity to minimize construction costs, while the inner layer optimizes the energy storage system's daily operation strategy to minimize operating costs and maximize the benefits of the energy storage system. The specific process is as follows: Figure 2 shown.

[0105] Genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanism. It is widely used in various fields, such as power, environment and process industry. Its basic principle is to simulate the evolution process of organisms in nature, realize the survival of the fittest, and finally find the best solution.

[0106] In genetic algorithms, the individual information in the population is similar to the decision variables that affect the objective function. Binary encoding is used in individual information, which is particularly suitable for representing discrete integer variables. The individual information of the population in this paper includes the energy storage access location and capacity.

[0107] After the genetic algorithm determines the population, each individual passes its information to the inner optimization, and the optimization result obtained by the whale optimization algorithm is used as the fitness of the genetic individual. According to the fitness of each individual, high-quality individuals are screened and retained to the next generation until the optimal solution is obtained. Genetic operations mainly include selection operators, crossover operators, and mutation operators.

[0108] Whales are large in size and move relatively slowly compared to other predators. If they want to catch more prey, they need a unique way of cooperation to find more fish. The whale optimization algorithm is inspired by this. Whales spiral upward to approach the target and gradually narrow the encirclement until they reach the location of the target fish. According to the characteristics of predation, the whale optimization algorithm has three update methods: encircling prey, spiral search, and random search.

[0109] (1) Surrounding the prey

[0110] The whales share prey information and move closer to the whale closest to the prey in the group, gradually shrinking the encirclement to surround the prey. The position update formula is:

[0111]

[0112] Where t is the number of iterations; Z is the whale position; Z is the global optimal position; A and C are coefficient matrices; s1 and s2 are [0,1] uniformly distributed random numbers; t max is the maximum number of iterations.

[0113] (2) Spiral search

[0114] The whale slowly approaches the target school of fish by searching in a spiral upward direction. The expression for the spiral search is:

[0115] Z(t+1)=Z * (t)-D·e fh cos(2πh) (15)

[0116] Where f is a constant and h is a uniformly distributed random number between [0,1].

[0117] When whales search for prey, they not only spiral upward, but also shrink the circle of fish according to the location of the prey. Therefore, they need to surround the prey and search in spirals simultaneously. At this time, the update formula is:

[0118]

[0119] In the formula, is a uniformly distributed random number in the range [0,1].

[0120] (3) Random Search

[0121] In order to improve the global search capability, the whales are allowed to have a certain degree of randomness when searching for prey, and the search range of the whale group is increased. When the absolute value of coefficient A is greater than or equal to 1, it means that the whales are no longer searching in the circle. At this time, random search should be selected, otherwise spiral search should be selected. Among them, the random search is updated as follows:

[0122] Z(t+1)=Z rand (t)-A·|C·Zrand (t)-Z(t)| (17)

[0123] In the formula, Z rand is a random whale position.

[0124] After the outer layer optimizes the given energy storage access location and capacity, the inner layer first randomly generates the charging and discharging power of the energy storage at each sampling moment, and then performs power correction according to the continuity constraint of the energy storage charge state, and obtains the single individual daily operation strategy. Finally, the whale optimization algorithm is used to iterate multiple times to find the optimal daily operation strategy of the wind power energy storage system under the current given configuration.

[0125] The present invention also provides a wind power generation system energy storage optimization configuration device for implementing the above method embodiment. Figure 3 1 is a schematic diagram of a wind power generation system energy storage optimization configuration device according to an embodiment of the present invention. Figure 3 As shown, including:

[0126] Parameter acquisition module 1, used to acquire system parameters of the wind power generation system;

[0127] An objective function configuration module 2, used for configuring an objective function for a wind power generation system according to system parameters;

[0128] The constraint condition setting module 3 is used to determine the self-condition constraints of the wind power generation system and the wind energy grid-connected absorption capacity according to the system parameters of the wind power generation system, and set system constraints for the wind power generation system according to the self-condition constraints of the wind power generation system, the wind energy grid-connected absorption capacity and the safety of the power grid side;

[0129] The double-layer optimization model establishment module 4 is used to establish a double-layer optimization model for the wind power generation system according to the objective function of the wind power generation system and the system constraints of the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

[0130] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0131] The present invention also provides a wind power generation system energy storage optimization configuration device for implementing the above method embodiment. Figure 4 1 is a schematic diagram of a wind power generation system energy storage optimization configuration device according to an embodiment of the present invention. Figure 4As shown, the wind power generation system energy storage optimization configuration device of this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. The processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, and the program is at least used to execute the wind power generation system energy storage optimization configuration method in the above embodiment.

[0132] The specific implementation scheme of the wind power generation system energy storage optimization configuration device provided in the embodiment of the present application can refer to the implementation scheme of the wind power generation system energy storage optimization configuration method in any of the above embodiments, which will not be repeated here.

[0133] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0134] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0135] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0136] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0137] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0141] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing energy storage configuration of a wind power generation system, characterized in that: include: Acquiring system parameters of a wind power generation system, and configuring an objective function for the wind power generation system according to the system parameters; Determining the inherent condition limitations and wind energy grid-connected absorption capacity of the wind power generation system according to the system parameters of the wind power generation system; According to the constraints of the energy storage system itself, the wind energy grid-connected absorption capacity and the safety of the grid, setting system constraints for the wind power generation system; According to the objective function of the wind power generation system and the system constraints of the wind power generation system, a two-layer optimization model is established for the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

2. The method according to claim 1, characterized in that: The configuring an objective function for the wind power generation system according to the system parameters comprises: Determine a calculation function for the total annual cost of the energy storage system based on the network loss cost of the wind power generation system, the reactive power cost provided by the wind power generation device, the energy storage charging and discharging cost, and the energy storage cost; Determine the energy loss cost function of the energy storage system when storing energy according to the grid loss power of the wind power generation system, the number of days the energy storage system interacts with the grid, the sampling time interval and the electricity price; Determine the reactive loss function of the energy storage system when it is running according to the reactive power provided by the wind power generation device of the wind power generation system, the sampling time interval and the number of days the energy storage system interacts with the power grid; Determine the charging and discharging costs of the energy storage system according to the energy storage charging power, energy storage discharging power, the number of days the energy storage system interacts with the power grid, the sampling time interval and the electricity price of the wind power generation system; Determine the investment and construction cost function of the wind power generation system according to the number of energy storage installations, unit power cost, configured power, unit capacity cost, energy storage capacity, discount rate and service life of the wind power generation system; Determining an operation and maintenance cost function of the wind power generation system according to the operation and maintenance cost per unit power generation of the wind power generation system and the annual discharge of energy storage; The energy storage cost function of the wind power generation system is determined based on the investment and construction cost function and the operation and maintenance cost function of the wind power generation system.

3. The method according to claim 2, characterized in that The system constraints include: node voltage constraints, node power balance constraints, branch active power constraints, wind power generation device output constraints, energy storage charge state constraints, energy storage charge state continuity constraints, energy storage power constraints and energy storage charging and discharging constraints.

4. The method according to claim 3, characterized in that: Also includes: When the two-layer optimization model is running, the genetic algorithm and the whale optimization algorithm are initialized; Use Yalmip and Cplex toolboxes to perform power flow calculations and evaluate system cost adaptability; Determine whether the whale optimization algorithm has reached the first preset number of iterations. If not, generate a new whale optimization algorithm population for iteration; If yes, then evaluate the adaptability of the energy storage configuration and determine whether the whale optimization algorithm has reached the second preset number of iterations. If yes, terminate the operation; If not, a new genetic algorithm population is generated and iterated.

5. A wind power generation system energy storage optimization configuration device, characterized in that: include: A parameter acquisition module, used to acquire system parameters of the wind power generation system; An objective function configuration module, configured to configure an objective function for the wind power generation system according to the system parameters; A constraint setting module, used to determine the self-condition constraints and wind energy grid-connected absorption capacity of the wind power generation system according to the system parameters of the wind power generation system, and set system constraints for the wind power generation system according to the self-condition constraints of the wind power generation system, the wind energy grid-connected absorption capacity and grid-side security; A double-layer optimization model establishment module is used to establish a double-layer optimization model for the wind power generation system according to the objective function of the wind power generation system and the system constraints of the wind power generation system, wherein the outer model designs a genetic algorithm to optimize the energy storage configuration location, power and capacity, and the inner model designs a whale optimization algorithm to optimize the energy storage charging and discharging strategy.

6. A wind power generation system energy storage optimization configuration device, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the wind power generation system energy storage optimization configuration method described in any one of claims 1-4.

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