High-altitude wind power station power fluctuation stabilizing control method, device, equipment, medium and product

Abstract

The invention discloses a high-altitude wind power station power fluctuation stabilizing control method, device, equipment, medium and product, and relates to the technical field of wind power generation, and the method comprises the steps: adjusting and smoothing the output power fluctuation of a high-altitude wind power station based on a high-altitude wind storage combined power generation system; determining an estimated fluctuation quantity of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time; based on the estimated fluctuation quantity of the output active power of the high-altitude wind power station, stabilizing the output power fluctuation of the high-altitude wind power station by using a weighted moving average filtering algorithm, and determining a grid-connected expected power curve; taking the grid-connected expected power curve as a power target reference of closed-loop control, and formulating a double-closed-loop fuzzy power smooth control strategy; and stabilizing and controlling the output power of the high-altitude wind power station according to the double-closed-loop fuzzy power smooth control strategy, and the service life of an energy storage system can be prolonged while the power output smoothness can be optimized.

Description

Technical Field

[0001] The present application relates to the field of wind power generation technology, and in particular to a method, device, equipment, medium and product for controlling power fluctuations in a high-altitude wind power station. Background Art

[0002] In the context of the transformation of the global energy structure, reducing dependence on fossil fuels and reducing environmental pollution have become important issues facing today's society. As a green, clean and renewable energy source, wind energy is becoming increasingly important in the development of the global energy system. High-altitude wind energy projects, which were born out of breakthroughs in aerial wind power generation technology, have opened up a new track in the field of wind power. By utilizing more abundant wind energy resources, the efficiency and power generation of units can be improved. At the same time, the development and application of high-altitude wind power will promote technological innovation and industrial upgrading in related fields.

[0003] The inherent intermittent and random characteristics of wind energy make the output power of high-altitude wind power stations highly uncontrollable. If high-altitude wind power is directly connected to the power grid, the fluctuation of the output power with a large amplitude will have an adverse effect on the grid voltage and frequency stability and power quality of the power system, causing grid frequency fluctuations and voltage flicker, seriously undermining the stability and safety of grid operation and dispatching.

[0004] There are basically two ways to solve the problem of output power fluctuation of high-altitude wind power stations: the first is to use reactive compensation through parallel capacitor banks, static VAR compensators or static synchronous VAR compensators and other equipment to improve power quality and reduce wind power fluctuations, but this method requires huge investment and is difficult to control and maintain. The second is to configure an energy storage system to coordinate the operation of high-altitude wind power stations, and use the rapid charging and discharging of the energy storage system to smooth the output fluctuations of high-altitude wind power stations. It has high controllability and does not require changing the control structure of wind turbines. The use of energy storage technology can not only maximize the use of wind energy resources and reduce wind abandonment, but also improve the overall flexibility of the system.

[0005] However, current research and technical solutions for the coordinated operation of energy storage systems with wind power stations are mostly concentrated on traditional ground wind power stations, while there are fewer related solutions for high-altitude wind power stations, which are mainly in the preliminary research and concept verification stage. When high-altitude wind power stations are operated in conjunction with energy storage systems, how to simultaneously optimize the smoothness of power output and improve the life of the energy storage system remains a difficult problem. Summary of the invention

[0006] The purpose of this application is to provide a method, device, equipment, medium and product for controlling power fluctuations in a high-altitude wind power station, which can optimize the smoothness of power output while increasing the life of the energy storage system.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for controlling power fluctuations of a high-altitude wind power station, comprising:

[0009] Based on the high-altitude wind-storage combined power generation system, the output power fluctuation of the high-altitude wind power station is regulated and smoothed; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes energy-type energy storage equipment and power-type energy storage equipment;

[0010] Determining an estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time;

[0011] Based on the estimated fluctuation of the active power output of the high-altitude wind power station, a weighted moving average filtering algorithm is used to smooth the output power fluctuation of the high-altitude wind power station, and a grid-connected expected power curve is determined;

[0012] The grid-connected expected power curve is used as a power target reference for closed-loop control, and a double closed-loop fuzzy power smoothing control strategy is formulated;

[0013] The output power of the high-altitude wind power station is smoothly controlled according to the double closed-loop fuzzy power smoothing control strategy.

[0014] In a second aspect, the present application provides a high-altitude wind power station power fluctuation smoothing control device, comprising:

[0015] An output power fluctuation regulation and smoothing module is used to regulate and smooth the output power fluctuation of a high-altitude wind power station based on a high-altitude wind-storage combined power generation system; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes an energy-type energy storage device and a power-type energy storage device;

[0016] An estimated fluctuation amount determination module is used to determine the estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time;

[0017] A grid-connected expected power curve determination module is used to smooth the output power fluctuation of the high-altitude wind power station based on the estimated fluctuation of the output active power of the high-altitude wind power station using a weighted moving average filtering algorithm to determine the grid-connected expected power curve;

[0018] A dual closed-loop fuzzy power smoothing control strategy formulation module is used to formulate a dual closed-loop fuzzy power smoothing control strategy by taking the grid-connected desired power curve as a power target reference for closed-loop control;

[0019] The smoothing control module is used to smoothly control the output power of the high-altitude wind power station according to the double closed-loop fuzzy power smoothing control strategy.

[0020] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for controlling power fluctuations in a high-altitude wind power station.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for controlling power fluctuation smoothing in a high-altitude wind power station.

[0022] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for controlling power fluctuation smoothing in a high-altitude wind power station.

[0023] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0024] This application is based on the real-time active power output of the high-altitude wind power station and the weighted moving average filtering algorithm to determine the grid-connected expected power curve that represents real-time changes, and based on the double closed-loop fuzzy power smoothing control strategy to smoothly control the output power of the high-altitude wind power station, so as to limit the output active power fluctuation in the maximum power tracking mode of the high-altitude wind power station to the fluctuation limit standard required by the power grid, while greatly reducing the data storage space, improving the data calculation speed, ensuring the timeliness of the control action, and taking into account the use cost and life of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A flow chart of a method for controlling power fluctuations in a high-altitude wind power station provided in one embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of a high-altitude wind-storage combined power generation system provided in one embodiment of the present application;

[0028] Figure 3 A topological diagram of a high-altitude wind power fluctuation smoothing system based on a hybrid energy storage system provided in one embodiment of the present application;

[0029] Figure 4 A double closed-loop fuzzy power smoothing control strategy for a high-altitude wind-storage combined power generation system provided in an embodiment of the present application

[0030] Figure 5 A schematic diagram of the change of the membership function of A provided in an embodiment of the present application; wherein, Figure 5 (a) is ΔP WF (t) Schematic diagram of membership function changes; Figure 5 (b) is SOC LB (t) Schematic diagram of membership function changes; Figure 5 (c) is a schematic diagram of the change of Ka membership function;

[0031] Figure 6 A schematic diagram of the reasoning results of the fuzzy controller A provided in one embodiment of the present application;

[0032] Figure 7 This is a schematic diagram of the change of the B membership function provided in an embodiment of the present application; wherein, Figure 7 (a) is ΔSOC VRB (t) Schematic diagram of membership function changes; Figure 7 (b) is SOC VRB (t) Schematic diagram of membership function changes; Figure 7 (c) is a schematic diagram of the change of Kb membership function;

[0033] Figure 8 A schematic diagram of the reasoning results of the fuzzy controller B provided in one embodiment of the present application;

[0034] Fig. 9 A comparison chart of the output power of a lithium battery and a vanadium flow battery provided in one embodiment of the present application;

[0035] Fig.10 This is a comparison chart of the SOC of a lithium battery and a vanadium flow battery provided in one embodiment of the present application;

[0036] Fig.11 This is a comparison diagram of output power before and after energy storage control leveling provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] The embodiment of the present application provides a method for controlling power fluctuations of a high-altitude wind power station. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps.

[0040] S1: Based on the high-altitude wind-storage combined power generation system, the output power fluctuation of the high-altitude wind power station is regulated and smoothed; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes energy-type energy storage equipment and power-type energy storage equipment.

[0041] S2: Determine an estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time.

[0042] S3: Based on the estimated fluctuation of the active power output of the high-altitude wind power station, a weighted moving average filtering algorithm is used to smooth the output power fluctuation of the high-altitude wind power station, and a grid-connected expected power curve is determined.

[0043] S4: Taking the grid-connected expected power curve as a power target reference for closed-loop control, a dual closed-loop fuzzy power smoothing control strategy is formulated.

[0044] S5: Smoothly control the output power of the high-altitude wind power station according to the double closed-loop fuzzy power smoothing control strategy.

[0045] In an exemplary embodiment, Figure 2 The high-altitude wind-storage combined power generation system designed is composed of a high-altitude wind power generation system, a hybrid energy storage system (HESS) and a control system. Figure 2 In, P WF is the output power of the high-altitude wind power station; P wb is the power control command of HESS; P GRID is the output power of the high-altitude wind-storage combined power generation system to the grid; P HES is the output power of HESS; where P WF Transmitted through the flexible direct current transmission system, the control system collects P WF The real-time signal can convey the charge and discharge instructions to the HESS under the constraint of high-altitude wind power fluctuation, thus achieving P WFFluctuation Suppression The HESS of the present application is composed of an energy storage device (such as a vanadium flow battery) and a power storage device (such as a lithium-ion battery).

[0046] The topology of high altitude wind power smoothing control system based on hybrid energy storage system is as follows: Figure 3 As shown in the figure, its core is to combine the hybrid energy storage system composed of lithium batteries and vanadium liquid flow batteries with high-altitude wind power stations, which are mainly composed of DC / AC inverters, DC / DC converters, AC / DC rectifiers and transformers. Since high-altitude wind power stations operate in maximum power tracking mode for a long time, wind shear changes will directly affect the power output of the unit, causing intermittent power fluctuations. When the output power of the high-altitude wind power station fluctuates greatly, the hybrid energy storage system adjusts its output power through flexible charging and discharging to smooth the power output of the high-altitude wind power station and ultimately deliver stable electricity to the power grid. HESS can quickly respond to power fluctuations of high-altitude wind power stations. Lithium-ion batteries are responsible for rapid power regulation in a short period of time, while vanadium liquid flow batteries are used for long-term energy balance to ensure the safe and stable operation of the power grid.

[0047] Furthermore, by building a complete hardware and control architecture to adjust and smooth the output power of wind turbines, the HESS needs to be adjusted according to the real-time fluctuations of the output of high-altitude wind power stations. The weighted moving average filtering method based on genetic algorithm shown below provides a stable expected power target for the HESS by analyzing the output power fluctuation data.

[0048] This application clarifies the hardware basis and energy storage response mechanism of the control method, and formulates the expected power output target for the HESS through a weighted moving average filtering method based on a genetic algorithm. The two work together to ensure the smooth output of power from the high-altitude wind power station and meet the requirements of grid connection.

[0049] According to the real-time active power situation of the high-altitude wind power station, the estimated fluctuation of the output active power at time t is calculated: ΔP WF (t)(i.e. the output power P of the hybrid energy storage system HESS (t)):

[0050] ΔP WF (t) = P WF (t)-P ref (t)

[0051] Among them, P WF (t) is the actual output active power of the wind farm at time t, P ref (t) is the expected grid-connected power at time t.

[0052] In an exemplary embodiment, S3 may be replaced by the following steps.

[0053] S31: constructing an objective function and constraint conditions based on the estimated fluctuation of the active power output of the high-altitude wind power station.

[0054] S32: For each candidate solution in the original population, a genetic algorithm is used to perform weighted moving average filtering on the fitness value corresponding to the current candidate solution to determine the fitness value of each candidate solution; the candidate solution is a filtering coefficient.

[0055] S33: Evaluate whether the fitness value of each candidate solution satisfies the constraint condition, and select the fitness value that satisfies the constraint condition.

[0056] S34: adopting a roulette wheel selection strategy, selecting individuals according to the screened fitness values, and performing crossover operations and mutation operations on the individuals to determine a new population.

[0057] S35: Take the new population as the original population, return to "for each candidate solution in the original population, use the genetic algorithm to perform weighted moving average filtering on the fitness value corresponding to the current candidate solution, and determine the fitness value of each candidate solution", and evaluate the fitness of the new population again until the iterative process meets the termination condition and determines the optimal filter coefficient.

[0058] S36: Smoothing the output power fluctuation of the high-altitude wind power station according to the optimal filter coefficient, and determining a grid-connected expected power curve.

[0059] In an exemplary embodiment, the present application also designs a weighted moving average filtering algorithm to determine the grid-connected expected power curve P ref (t).

[0060] Furthermore, the smoothed value of the previous moment after algorithm processing and the data of the current moment are obtained by weighted summation. Since the required recent data is only the data of the previous moment, the data storage space can be greatly reduced and the data operation speed can be improved. The designed weighted moving average filtering algorithm smoothes the output power fluctuation of the high-altitude wind power station in the following steps:

[0061] P ref (t) = γP WF (t)+(1-γ)P ref (t-1),γ∈[0,1]

[0062] Among them, γ is the optimal filtering coefficient. The smaller γ is, the more significant the filtering effect is. When γ=1, there is no filtering effect.

[0063] In order to find the optimal value of γ, this application designs a weighted moving average filtering algorithm optimized by a genetic algorithm (GA). The optimal filtering coefficient is determined by a genetic algorithm to ensure that the active output power of the high-altitude wind power station after filtering meets the grid-connected smoothing standard. HESS (t) is as small as possible, which helps to reduce the configuration capacity of the hybrid energy storage system and improve the economic efficiency of the station.

[0064] The objective function and constraints are as follows:

[0065]

[0066] Among them, P HESS (t) is the output power of the hybrid energy storage system; T is the maximum sampling time step; t is the time step index; f(γ) is the fitness value; λ is the penalty coefficient; Q 1min P is the maximum allowable ramp rate (positive direction) of power fluctuation within 1 minute; ref (t) is the expected grid-connected power output target at time t; P ref (t-1) is the expected grid-connected power output target at time t-1; P ref (θ) is the grid-connected expected power output target at θ=t, t+1, ..., t+10; Q 10min is the maximum ramping amount (positive direction) allowed for power fluctuation within 10 minutes; θ is the time step index from time t to t+10.

[0067] As above, a penalty term is added to the constraint condition to impose a penalty on solutions that violate the fluctuation standard, v1(t) and v 10 (t) represents the degree of violation of the volatility constraints within 1 minute and 10 minutes respectively. If violated, it is set to 1, otherwise it is 0; λ is the penalty coefficient.

[0068] The steps of optimizing γ using genetic algorithm (GA) are as follows:

[0069] Step 1: Population initialization.

[0070] Initialize the population, that is, generate N random candidate solutions, each solution γ i ∈[0,1].

[0071] Step 2: Fitness evaluation.

[0072] For each candidate solution γ i , calculate its fitness value f(γ i ). First, use the current weighted moving average filter to calculate P ref (t) and the corresponding P HESS (t), and then calculate the objective function f(γ i ).

[0073] Evaluate whether the constraints are satisfied: -Q 1min ≤P ref (t)-P ref (t-1)≤Q 1min , If violated, the penalty term λ will be increased accordingly.

[0074] Step 3: Select.

[0075] Use the Roulette Wheel Selection strategy to select individuals based on their fitness values. Individuals with smaller fitness values ​​have a higher probability of being selected to generate the next generation. The selection probability p i Defined as:

[0076]

[0077] Step 4: Cross operation.

[0078] The single-point crossover operation is used to generate new individuals. Assume that two parent individuals γ are selected. a and γ b , the offspring individuals generated by crossover are new The expression is:

[0079] γ new =βγ a +(1-β)γ b

[0080] Where β∈[0,1] is a randomly generated crossover coefficient. The probability of an individual being selected to participate in the crossover operation is given by p i decide, and p i In the roulette wheel selection strategy, it is determined by the fitness value of the individual. The larger the fitness value, the higher the probability that the individual will be selected for the crossover operation here.

[0081] The main purpose of the crossover operation in Step 4 is to generate new individuals by recombining the genes of the parental individuals and exploring the neighborhood of existing solutions in the solution space. Crossover tends to retain better characteristics.

[0082] Step 5: Mutation operation.

[0083] Based on the set probability P m (Mutation rate), perform mutation operations on individuals in the population, change their γ values, and introduce diversity. The γ of the mutated individuals mut It can be expressed as:

[0084] γ mut =γ+δ

[0085] where δ∈[-0.05,0.05] is a small random perturbation that ensures γ is in the range [0,1].

[0086] The main purpose of the mutation operation in Step 5 is to randomly change certain genes of individuals and introduce new characteristics to increase the diversity of the population and prevent the population from falling into the local optimum too early. Crossover is responsible for developing existing excellent solutions, while mutation is responsible for exploring new solution spaces. The combination of the two can balance development (Exploration) and exploration (Exploitation).

[0087] Step 6: Fitness evaluation and iteration.

[0088] Return to Step 2 for the new population, perform fitness evaluation again, and update the fitness value. Repeat the selection, crossover, and mutation operations, and perform them iteratively. The termination condition is that the maximum number of iterations G is reached or the fitness value no longer changes significantly.

[0089] In an exemplary embodiment, S4 may be replaced by the following steps.

[0090] S41: Taking the estimated fluctuation amount and the state of charge of the power-type energy storage device as inputs of a fuzzy controller A to determine a smoothing coefficient.

[0091] S42: Determine the maximum actual power output by the hybrid energy storage system according to the smoothing coefficient and the estimated fluctuation amount.

[0092] S43: Using the charge state change amount and charge state of the energy storage device as inputs of the fuzzy controller B to determine the allocation coefficient.

[0093] S44: Determine the actual output power of the energy-type energy storage device and the power-type energy storage device according to the allocation coefficient and the actual maximum power.

[0094] S45: updating the charge state of the energy-type energy storage device and the power-type energy storage device in real time according to the actual output power of the energy-type energy storage device and the power-type energy storage device, and dynamically adjusting the dual closed-loop fuzzy power smoothing control strategy.

[0095] In an exemplary embodiment, S45 may be replaced by the following steps.

[0096] use and Update the state of charge of the energy storage device and the power storage device in real time, and dynamically adjust the double closed-loop fuzzy power smoothing control strategy; wherein SOC VRB (t+1) is the state of charge of the energy storage device at time t+1; SOC VRB(t) is the state of charge of the energy storage device at time t; P VRB (t) is the actual output power of the energy storage device; C VRB is the rated capacity of the energy storage device; Δt is the unit time interval; SOC LB (t+1) is the state of charge of the power storage device at time t+1; SOC LB (t) is the state of charge of the power type energy storage device at time t; P LB (t) is the actual output power of the power type energy storage device; C LB is the rated capacity of the power type energy storage device.

[0097] In an exemplary embodiment, Figure 2 The hardware topology structures including high-altitude wind power stations, flexible direct current transmission systems, and HESS established in the paper provide a physical basis for the double closed-loop fuzzy power smoothing control strategy. The designed power control method relies on these devices for actual control operations. ref (t) Provide power target reference for the dual closed-loop fuzzy power smoothing control strategy and guide the power distribution and charging and discharging management of the energy storage system.

[0098] In order to smooth the output power fluctuation of high-altitude wind power stations and take into account the cost and life of the energy storage system, the following design is made: Figure 4 The double closed-loop fuzzy power smoothing control strategy based on genetic algorithm optimization and smoothing selection is shown in the figure. Figure 4 As shown in the figure, the function of closed loop 1 is to smooth the total power output of wind power to avoid excessive fluctuations affecting the stability of the power grid, that is, to determine whether the SOC of the energy storage system is within the allowable range required to smooth the power fluctuations of the wind farm. Closed loop 2 uses the SOC state of the energy storage system to reasonably allocate the output power of the hybrid energy storage energy management system. Because the number of charge and discharge times and service life of vanadium liquid flow batteries are much higher than those of lithium batteries, the power allocation principle of this circuit is to reduce the number of charge and discharge times of lithium batteries, and give priority to the output power of vanadium liquid flow batteries.

[0099] By ΔP WF (t) and the lithium battery state of charge SOCLB(t) are used as the input of the fuzzy controller A to obtain the smoothing coefficient Ka. According to the estimated fluctuation ΔP of the output active power calculated WF (t), the maximum actual power output of the hybrid energy storage system Ph(t) is:

[0100] P h (t) = K a (t)×ΔP WF (t)

[0101] The change in the state of charge of the vanadium liquid flow battery at time t, ΔSOCVRB(t), is calculated as:

[0102]

[0103] In the formula, C VRB is the rated capacity of the vanadium redox flow battery.

[0104] The allocation coefficient Kb(t) is obtained by taking ΔSOCVRB(t) and the state of charge of the lithium battery SOCVRB(t) as the input of the fuzzy controller B. Then the actual output power distribution of the vanadium flow battery and the lithium battery is:

[0105] P VRB (t) = K b (t)×P h (t)

[0106] P LB (t) = P h (t)-P VRB (t)

[0107] Where P VRB (t) is the output power of the vanadium liquid flow battery at time t, P LB (t) is the output power of the lithium battery at time t

[0108] According to the actual output power of vanadium flow batteries and lithium batteries, the SOC value of each battery is updated in real time to reflect the real-time status of the battery in the energy storage system, ensuring that the control strategy can be dynamically adjusted according to the actual situation and providing an important basis for the design of fuzzy logic control strategy:

[0109]

[0110] Among them, C LB is the rated capacity of the lithium battery. The SOC value of each battery updated in real time is an important input variable of fuzzy controller A and fuzzy controller B.

[0111] In order to clarify the process of fuzzy controller A and fuzzy controller B mapping input variables to output variables, this application also provides input-to-output mapping rules to achieve the adjustment of nonlinear input-output relationship, thereby achieving fine control of the system and determining the input variable ΔP of fuzzy controller A. WF (t), SOCLB(t), membership function of output variable Ka Figure 5 As shown, ΔP WFIn the membership function of (t), NB (negative large), NM (negative small), Z (zero), PM (positive small), PB (positive large) represent the five designed fuzzy subsets, ranging from [-∞, +∞]. In the membership function of SOCLB(t), L (zero), Z (small), M (medium), HM (medium large), H (large) represent the five fuzzy subsets, ranging from [0, 1]. In the membership function of Ka, L (zero), LM (small), M (medium), HM (medium large), H (large) represent the five fuzzy subsets, ranging from [0, 1]. The reasoning results of fuzzy controller A are as follows: Figure 6 shown.

[0112] Similarly, fuzzy controller B is defined similarly to fuzzy controller A. The membership functions of the input variables ΔSOCVRB(t), SOCVRB(t) and the output variable Kb of fuzzy controller B are as follows: Figure 7 As shown in the figure, in the membership function of ΔSOCVRB(t), NB (negative large), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), and PB (positive large) represent the seven designed fuzzy subsets, respectively, and the range is [-∞, +∞]. In the membership function of SOCVRB(t), L (zero), Z (small), M (medium), HM (medium large), and H (large) represent five fuzzy subsets, respectively, and the range is [0, 1]. In the membership function of Kb, L (zero), LM (small), M (medium), HM (medium large), and H (large) represent five fuzzy subsets, respectively, and the range is [0, 1]. The reasoning results of fuzzy controller B are shown in the figure. Figure 8 shown.

[0113] from Figure 9-10 It can be seen that the vanadium liquid flow battery is frequently charged and discharged, taking on part of the charging and discharging tasks of the lithium battery, making the lithium battery run smoothly and avoiding frequent charging and discharging.

[0114] from Fig.11 It can be seen that the output power of the high-altitude wind power station fluctuates greatly when there is no hybrid energy storage control. After adding the hybrid energy storage system and applying the designed double closed-loop fuzzy power smoothing control strategy, the output power of the high-altitude wind power station becomes smoother, which is conducive to improving the grid stability. At the same time, it avoids excessive charging and discharging of the hybrid energy storage system, reduces the number of charge and discharge times of the lithium battery, prolongs the service life of the lithium battery, and utilizes the energy storage system to effectively smooth the wind power output.

[0115] Based on the same inventive concept, the embodiment of the present application also provides a high-altitude wind power station power fluctuation smoothing control device for implementing the high-altitude wind power station power fluctuation smoothing control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the high-altitude wind power station power fluctuation smoothing control device provided below can refer to the limitations of the high-altitude wind power station power fluctuation smoothing control method above, and will not be repeated here.

[0116] In an exemplary embodiment, a high-altitude wind power station power fluctuation smoothing control device is provided, comprising:

[0117] The output power fluctuation regulation and smoothing module is used to regulate and smooth the output power fluctuation of the high-altitude wind power station based on the high-altitude wind-storage combined power generation system; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes energy-type energy storage equipment and power-type energy storage equipment.

[0118] The estimated fluctuation amount determination module is used to determine the estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time.

[0119] The grid-connected expected power curve determination module is used to smooth the output power fluctuation of the high-altitude wind power station based on the estimated fluctuation of the active power output of the high-altitude wind power station using a weighted moving average filtering algorithm to determine the grid-connected expected power curve.

[0120] The dual closed-loop fuzzy power smoothing control strategy formulation module is used to use the grid-connected expected power curve as a power target reference for closed-loop control and formulate a dual closed-loop fuzzy power smoothing control strategy.

[0121] The smoothing control module is used to smoothly control the output power of the high-altitude wind power station according to the double closed-loop fuzzy power smoothing control strategy.

[0122] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store power fluctuation smoothing control data of a high-altitude wind power station. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a power fluctuation smoothing control method of a high-altitude wind power station is implemented.

[0123] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0124] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0125] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0127] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0128] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for controlling power fluctuations in a high-altitude wind power station, characterized in that: The high-altitude wind power station power fluctuation smoothing control method comprises: Based on the high-altitude wind-storage combined power generation system, the output power fluctuation of the high-altitude wind power station is regulated and smoothed; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes energy-type energy storage equipment and power-type energy storage equipment; Determining an estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time; Based on the estimated fluctuation of the active power output of the high-altitude wind power station, a weighted moving average filtering algorithm is used to smooth the output power fluctuation of the high-altitude wind power station, and a grid-connected expected power curve is determined; The grid-connected expected power curve is used as a power target reference for closed-loop control, and a double closed-loop fuzzy power smoothing control strategy is formulated; The output power of the high-altitude wind power station is smoothly controlled according to the double closed-loop fuzzy power smoothing control strategy.

2. The method for controlling power fluctuations of a high-altitude wind power station according to claim 1 is characterized in that: Based on the estimated fluctuation of the active power output of the high-altitude wind power station, the output power fluctuation of the high-altitude wind power station is smoothed by using a weighted moving average filtering algorithm to determine the grid-connected expected power curve, which specifically includes: Based on the estimated fluctuation of the active power output of the high-altitude wind power station, construct an objective function and constraint conditions; For each candidate solution in the original population, a weighted moving average filtering is performed on the fitness value corresponding to the current candidate solution using a genetic algorithm to determine the fitness value of each candidate solution; the candidate solution is a filtering coefficient; Evaluate whether the fitness value of each candidate solution satisfies the constraint condition, and select the fitness value that satisfies the constraint condition; A roulette wheel selection strategy is adopted to select individuals according to the screened fitness values, and crossover and mutation operations are performed on the individuals to determine a new population; The new population is used as the original population, and "for each candidate solution in the original population, a weighted moving average filter is performed on the fitness value corresponding to the current candidate solution using a genetic algorithm to determine the fitness value of each candidate solution", and the fitness of the new population is evaluated again until the iterative process meets the termination condition, and the optimal filtering coefficient is determined; The output power fluctuation of the high-altitude wind power station is smoothed according to the optimal filter coefficient, and the grid-connected expected power curve is determined.

3. The method for controlling power fluctuations of a high-altitude wind power station according to claim 2 is characterized in that: Based on the estimated fluctuation of the active power output of the high-altitude wind power station, an objective function and constraint conditions are constructed, specifically including: use Construct the objective function; where P HESS (t) is the output power of the hybrid energy storage system; T is the maximum sampling time step; t is the time step index; f(γ) is the fitness value; λ is the penalty coefficient; v1(t) is the degree of violation of the fluctuation constraint within 1 minute; v 10 (t) is the degree of violation of the volatility constraint within 10 minutes; use Construct constraints; where Q 1min P is the maximum climbing amount allowed by power fluctuation within 1 minute; ref (t) is the expected grid-connected power output target at time t; P ref (t-1) is the expected grid-connected power output target at time t-1; P ref (θ) is the grid-connected expected power output target at θ=t, t+1, ..., t+10; Q 10min is the maximum ramping amount allowed for power fluctuation within 10 minutes; θ is the time step index from time t to t+10.

4. The method for controlling power fluctuations of a high-altitude wind power station according to claim 3 is characterized in that: The output power fluctuation of the high-altitude wind power station is smoothed according to the optimal filter coefficient to determine the grid-connected expected power curve, specifically including: Using P ref (t) = γP WF (t)+(1-γ)P ref (t-1) Determine the grid-connected expected power curve; where γ is the optimal filter coefficient, γ∈[0,1]; P WF (t) is the actual output active power of the high-altitude wind power station at time t.

5. The method for controlling power fluctuations of a high-altitude wind power station according to claim 1 is characterized in that: The grid-connected expected power curve is used as a power target reference for closed-loop control, and a double closed-loop fuzzy power smoothing control strategy is formulated, specifically including: The estimated fluctuation amount and the state of charge of the power type energy storage device are used as inputs of the fuzzy controller A to determine the smoothing coefficient; Determine the maximum actual power output of the hybrid energy storage system according to the smoothing coefficient and the estimated fluctuation amount; The change in the state of charge of the energy storage device and the state of charge are used as inputs of the fuzzy controller B to determine the allocation coefficient; Determine the actual output power of the energy type energy storage device and the power type energy storage device according to the allocation coefficient and the actual power maximum value; The charge states of the energy-type energy storage device and the power-type energy storage device are updated in real time according to their actual output powers, and the dual closed-loop fuzzy power smoothing control strategy is dynamically adjusted.

6. The method for controlling power fluctuations of a high-altitude wind power station according to claim 5 is characterized in that: According to the actual output power of the energy storage device and the power storage device, the charge state of the energy storage device and the power storage device are updated in real time, and the dual closed-loop fuzzy power smoothing control strategy is dynamically adjusted, specifically including: use and Update the state of charge of the energy storage device and the power storage device in real time, and dynamically adjust the double closed-loop fuzzy power smoothing control strategy; wherein SOC VRB (t+1) is the state of charge of the energy storage device at time t+1; SOC VRB (t) is the state of charge of the energy storage device at time t; P VRB (t) is the actual output power of the energy storage device; C VRB is the rated capacity of the energy storage device; Δt is the unit time interval; SOC LB (t+1) is the state of charge of the power storage device at time t+1; SOC LB (t) is the state of charge of the power type energy storage device at time t; P LB (t) is the actual output power of the power type energy storage device; C LB is the rated capacity of the power type energy storage device.

7. A power fluctuation control device for a high-altitude wind power station, characterized in that: The high-altitude wind power station power fluctuation smoothing control device comprises: An output power fluctuation regulation and smoothing module is used to regulate and smooth the output power fluctuation of a high-altitude wind power station based on a high-altitude wind-storage combined power generation system; the high-altitude wind-storage combined power generation system includes a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system includes a high-altitude wind power station and a flexible direct current transmission system; the hybrid energy storage system includes an energy-type energy storage device and a power-type energy storage device; An estimated fluctuation amount determination module is used to determine the estimated fluctuation amount of the active power output by the high-altitude wind power station according to the active power output by the high-altitude wind power station in real time; A grid-connected expected power curve determination module is used to smooth the output power fluctuation of the high-altitude wind power station based on the estimated fluctuation of the output active power of the high-altitude wind power station using a weighted moving average filtering algorithm to determine the grid-connected expected power curve; A dual closed-loop fuzzy power smoothing control strategy formulation module is used to formulate a dual closed-loop fuzzy power smoothing control strategy by taking the grid-connected desired power curve as a power target reference for closed-loop control; The smoothing control module is used to smoothly control the output power of the high-altitude wind power station according to the double closed-loop fuzzy power smoothing control strategy.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power fluctuation smoothing control method for a high-altitude wind power station according to any one of claims 1 to 6.

9. 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 for controlling power fluctuations in a high-altitude wind power station according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for controlling power fluctuations in a high-altitude wind power station according to any one of claims 1 to 6 is implemented.

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