A high-altitude wind power station power fluctuation suppression control method, device, equipment, medium and product

Abstract

The application discloses a high-altitude wind power station power fluctuation suppression control method and device, equipment, medium and product, relates to the wind power generation technical field, and the method comprises the following steps: based on a high-altitude wind and energy storage combined power generation system, adjusting and smoothing the output power fluctuation of the high-altitude wind power station; determining the estimated fluctuation of the active power output of the high-altitude wind power station according to the real-time output active power 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 suppress the output power fluctuation of the high-altitude wind power station, determining a grid-connected expected power curve; taking the grid-connected expected power curve as a power target reference of closed-loop control, formulating a double-closed-loop fuzzy power smoothing control strategy; according to the double-closed-loop fuzzy power smoothing control strategy, suppressing and controlling the output power of the high-altitude wind power station, and the application can optimize the power output smoothing while improving the service life of the energy storage system.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a method, device, equipment, medium and product for suppressing power fluctuations in high-altitude wind power stations. Background Technology

[0002] Against the backdrop of global energy structure transformation, reducing dependence on fossil fuels and mitigating environmental pollution have become crucial issues facing society today. Wind energy, as a green, clean, and renewable energy source, is playing an increasingly important role in the development of the global energy system. High-altitude wind power projects, born from breakthroughs in aerial wind power technology, have opened up new avenues in the wind power sector, improving turbine efficiency and power generation by utilizing more abundant wind energy resources. Simultaneously, the development and application of high-altitude wind power will drive technological innovation and industrial upgrading in related fields.

[0003] The inherent intermittent and random nature of wind energy makes the output power of high-altitude wind power stations highly uncontrollable. If high-altitude wind power is directly connected to the power grid, the large fluctuations in output power will adversely affect the voltage and frequency stability of the power system, as well as power quality, causing grid frequency fluctuations and voltage flicker, which seriously undermines the stability and safety of grid operation and dispatch.

[0004] To address the power output fluctuation issue of high-altitude wind power stations, there are essentially two approaches: The first is to improve power quality and reduce wind power fluctuations by using parallel capacitor banks, static var compensators (SVCs), or static synchronous var compensators (SNCs) for reactive power compensation. However, this method involves significant investment and is difficult to control and maintain. The second approach involves configuring energy storage systems to operate in conjunction with the high-altitude wind power station. The rapid charging and discharging of the energy storage system helps to smooth out power output fluctuations. This approach offers higher controllability, requires no changes to the wind turbine control structure, and not only maximizes the utilization of wind energy resources and reduces wind curtailment, but also improves the overall flexibility of the system.

[0005] However, current research and technical solutions for the coordinated operation of energy storage systems with wind power plants are mostly focused on traditional ground-mounted wind power plants, while there are fewer solutions for high-altitude wind power plants, which are mainly in the preliminary research and proof-of-concept stage. When high-altitude wind power plants and energy storage systems are operated together, how to simultaneously optimize the smoothness of power output and improve the lifespan of energy storage systems remains a challenge. Summary of the Invention

[0006] The purpose of this application is to provide a method, device, equipment, medium, and product for power fluctuation smoothing control in high-altitude wind power stations, which can optimize power output smoothness while improving the lifespan of energy storage systems.

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

[0008] In a first aspect, the application provides a high-altitude wind power station power fluctuation suppression control method, 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 adjusted and smoothed; the high-altitude wind storage combined power generation system comprises a high-altitude wind power generation system, a hybrid energy storage system, and a control system; the high-altitude wind power generation system comprises a high-altitude wind power station and a flexible direct-current power transmission system; the hybrid energy storage system comprises an energy-type energy storage device and a power-type energy storage device;

[0010] According to the real-time output active power of the high-altitude wind power station, the estimated fluctuation amount of the output active power of the high-altitude wind power station is determined;

[0011] Based on the estimated fluctuation amount of the output active power of the high-altitude wind power station, the output power fluctuation of the high-altitude wind power station is suppressed by using a weighted moving average filtering algorithm to determine a grid-connected expected power curve;

[0012] The grid-connected expected power curve is taken as a power target reference of closed-loop control to formulate a double-closed-loop fuzzy power smoothing control strategy;

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

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

[0015] An output power fluctuation adjustment and smoothing module is configured to adjust 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 comprises a high-altitude wind power generation system, a hybrid energy storage system, and a control system; the high-altitude wind power generation system comprises a high-altitude wind power station and a flexible direct-current power transmission system; the hybrid energy storage system comprises an energy-type energy storage device and a power-type energy storage device;

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

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

[0018] A double-closed-loop fuzzy power smoothing control strategy formulation module is configured to take the grid-connected expected power curve as a power target reference of closed-loop control to formulate a double-closed-loop fuzzy power smoothing control strategy;

[0019] A damping control module is configured to damp the output power of the high-altitude wind power station according to the double-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 the high-altitude wind power station power fluctuation damping control method according to any one of the above.

[0021] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the high-altitude wind power station power fluctuation damping control method according to any one of the above.

[0022] In a fifth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the high-altitude wind power station power fluctuation damping control method according to any one of the above.

[0023] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0024] Based on the real-time output active power of the high-altitude wind power station and the weighted moving average filtering algorithm, the present application determines the grid-connected expected power curve representing real-time changes, and based on the double-loop fuzzy power smoothing control strategy, the output power of the high-altitude wind power station is damp controlled, so that the output active power fluctuation of the high-altitude wind power station in the maximum power tracking mode is limited within the grid fluctuation limit value standard, while the data storage space is greatly reduced, the data operation speed is improved, the control action timeliness is ensured, and the use cost and service life of the energy storage system are considered. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0026] Figure 1 The high-altitude wind power station power fluctuation damping control method flow chart provided by an embodiment of the present application;

[0027] Figure 2 The high-altitude wind storage combined power generation system structure schematic diagram provided by an embodiment of the present application;

[0028] Figure 3 The high-altitude wind power fluctuation damping topology structure diagram based on the hybrid energy storage system provided by an embodiment of the present application;

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

[0030] Figure 5 The A membership function change schematic diagram provided by an embodiment of the present application is shown in the following figure: Figure 5 (a) in the figure is ΔP WF (t) membership function change schematic diagram; Figure 5 (b) in the figure is SOC LB (t) membership function change schematic diagram; Figure 5 (c) in the figure is Ka membership function change schematic diagram;

[0031] Figure 6 The fuzzy controller A reasoning result schematic diagram provided by an embodiment of the present application is shown in the following figure:

[0032] Figure 7 The B membership function change schematic diagram provided by an embodiment of the present application is shown in the following figure: Figure 7 (a) in the figure is ΔSOC VRB (t) membership function change schematic diagram; Figure 7 (b) in the figure is SOC VRB (t) membership function change schematic diagram; Figure 7 (c) in the figure is Kb membership function change schematic diagram;

[0033] Figure 8 The fuzzy controller B reasoning result schematic diagram provided by an embodiment of the present application is shown in the following figure:

[0034] Figure 9 The lithium battery and vanadium flow battery output power comparison diagram provided by an embodiment of the present application is shown in the following figure:

[0035] Figure 10 The lithium battery and vanadium flow battery SOC comparison diagram provided by an embodiment of the present application is shown in the following figure:

[0036] Figure 11 The energy storage control before and after output power comparison diagram provided by an embodiment of the present application is shown in the following figure: DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0038] The above objects, features and advantages of the present application will become more apparent from the following detailed description considered in conjunction with the accompanying drawings and specific embodiments.

[0039] The embodiment of the present application provides a high-altitude wind power station power fluctuation suppression control method, which is executed by a computer device, specifically, can be executed by a terminal or a server or the like computer device alone, or can be executed by the terminal and the server together, in the embodiment of the present application, as shown in the figure, Figure 1 The method comprises 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 adjusted and smoothed; the high-altitude wind storage combined power generation system comprises a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system comprises a high-altitude wind power station and a flexible AC transmission system; the hybrid energy storage system comprises an energy type energy storage device and a power type energy storage device.

[0041] S2: According to the real-time output active power of the high-altitude wind power station, the estimated fluctuation amount of the output active power of the high-altitude wind power station is determined.

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

[0043] S4: The grid-connected expected power curve is taken as a power target reference of closed-loop control, and a double-closed-loop fuzzy power smoothing control strategy is formulated.

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

[0045] In an exemplary embodiment, Figure 2 For the designed high-altitude wind storage combined power generation system, the system is composed of a high-altitude wind power generation system, a hybrid energy storage system (HESS) and a control system, Figure 2 P WF is the output power of the high-altitude wind power station; P wb is the power control instruction of the HESS; P GRID is the output power of the HESS; P HES is the output power of the HESS; wherein P WF is transmitted through the flexible AC transmission system, and the control system transmits the charging and discharging instructions to the HESS by collecting the real-time signals of P WF , so as to realize the suppression of P WFFluctuation mitigation. The HESS of this application consists of energy-type energy storage devices (such as vanadium redox flow batteries) and power-type energy storage devices (such as lithium-ion batteries).

[0046] The topology of a high-altitude wind power smoothing control system based on a hybrid energy storage system is as follows: Figure 3 As shown, its core is the integration of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and vanadium redox flow batteries with a high-altitude wind power station. It mainly comprises a DC / AC inverter, a DC / DC converter, an AC / DC rectifier, and a transformer. Because high-altitude wind power stations operate in maximum power point tracking (MPPT) mode for extended periods, wind shear changes directly affect the power output of the turbines, causing intermittent power fluctuations. When the output power of the high-altitude wind power station fluctuates significantly, the HESS adjusts its output power through flexible charging and discharging to smooth the power output of the high-altitude wind power station, ultimately delivering stable power to the grid. HESS can quickly respond to power fluctuations in high-altitude wind power stations; lithium-ion batteries handle rapid power regulation in short periods, while vanadium redox batteries are used for long-term energy balancing, ensuring the safe and stable operation of the power grid.

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

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

[0049] Based on the real-time active power situation of the high-altitude wind power station, calculate the estimated fluctuation ΔP of the output active power at time t. 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) represents the actual active power output of the wind farm at time t, P ref (t) represents the expected grid-connected power at time t.

[0052] In one exemplary embodiment, S3 can be replaced by the following steps.

[0053] S31: Construct a target function and a constraint condition 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, determine the fitness value of each candidate solution by using a genetic algorithm to perform weighted moving average filtering on the fitness value corresponding to the current candidate solution; the candidate solution is a filter coefficient.

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

[0056] S34: Select individuals according to the selected fitness value using a roulette wheel selection strategy, and perform crossover 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, determine the fitness value of each candidate solution by using a genetic algorithm to perform weighted moving average filtering on the fitness value corresponding to the current candidate solution", and perform fitness evaluation on the new population again until the iteration process meets the termination condition to determine the optimal filter coefficient.

[0058] S36: According to the optimal filter coefficient, the output power fluctuation of the high-altitude wind power station is smoothed to determine the grid-connected expected power curve.

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

[0060] Further, the smoothed value after algorithm processing at the previous moment and the data at the current moment are obtained by weighted summation to obtain the smoothed value at the current moment. Since the required recent data is only the data at 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 for smoothing the output power fluctuation of the high-altitude wind power station is as follows:

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

[0062] Wherein, γ is the optimal filter coefficient, the smaller the γ, the more significant the filtering effect, and when γ=1, there is no filtering effect.

[0063] To achieve the optimal value of γ, the application designs a weighted moving average filtering algorithm optimized by genetic algorithm (GA), determines the optimal filtering coefficient through genetic algorithm optimization, and ensures that the active output power of the high-altitude wind power station after filtering meets the grid connection suppression standard while P 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 the constraint condition are as follows:

[0065]

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

[0067] As above, the penalty term for the constraint condition is added, which is used to punish the solution that violates the fluctuation standard, v1(t) and v 10 (t) represent the violation degree of the fluctuation constraint within 1 minute and 10 minutes respectively, and is set to 1 if it is violated, otherwise 0; λ is the penalty coefficient.

[0068] The genetic algorithm (GA) optimization step of γ is 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 filtering to calculate P ref (t) and the corresponding P HESS (t), and then calculate the objective function f(γ i ).

[0073] Assess whether the constraints are met: -Q 1min ≤P ref (t)-P ref (t-1)≤Q 1min , If violated, a penalty item λ will be added accordingly.

[0074] Step 3: Select.

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

[0076]

[0077] Step 4: Cross-operation.

[0078] New individuals are generated using a single-point crossover operation. Assume two parent individuals γ are selected. a and γ b The offspring individuals γ generated by its crossover new The expression is:

[0079] γ new =βγ a +(1-β)γ b

[0080] In the formula, 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 Decision, and p i In roulette wheel selection, the fitness value of an individual determines its choice. The higher the fitness value, the greater the probability that the individual will be selected for the current crossover operation.

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

[0082] Step 5: Mutation operation.

[0083] Based on the given probability P m (Mutation rate) is used to introduce diversity by performing mutation operations on individuals in a population, changing their γ value. The γ value of the mutated individual... mut It can be represented as:

[0084] γ mut =γ+δ

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

[0086] The main purpose of the Step 5 mutation operation is to randomly change some genes of individuals, introduce new characteristics, and increase the diversity of the population to prevent the population from falling into local optimum too early. Crossover is responsible for developing existing good solutions, and mutation is responsible for exploring new solution space. The combination of the two can balance exploration and exploitation.

[0087] Step 6: Fitness evaluation and iteration.

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

[0089] In an exemplary embodiment, S4 can be replaced with the following steps.

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

[0091] S42: Determine the actual power maximum value of the hybrid energy storage system output according to the damping coefficient and the estimated fluctuation.

[0092] S43: Take the state of charge variation and the state of charge of the energy-type energy storage device as inputs of fuzzy controller B to determine the distribution 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 distribution coefficient and the actual power maximum value.

[0094] S45: Real-time update the state of charge of the energy-type energy storage device and the power-type energy storage device according to the actual output power of the energy-type energy storage device and the power-type energy storage device, and dynamically adjust the double-loop fuzzy power smoothing control strategy.

[0095] In an exemplary embodiment, S45 can be replaced with the following steps.

[0096] Real-time update the state of charge of the energy-type energy storage device and the power-type energy storage device using and dynamically adjust the double-loop fuzzy power smoothing control strategy; wherein SOC VRB (t+1) is the state of charge of the energy-type 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 a 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 storage device at time t; P LB (t) is the actual output power of the power storage device; C LB is the rated capacity of the power storage device.

[0097] In an exemplary embodiment, Figure 2 The hardware topology established in the foregoing includes high-altitude wind power stations, flexible transmission systems, HESSs, etc., which provide a physical basis for the double-loop fuzzy power smoothing control strategy, and the designed power control method is actually controlled by relying on these devices. The expected power curve P ref (t) provides a power target reference for the double-loop fuzzy power smoothing control strategy, guiding power distribution and charge-discharge management of the energy storage system.

[0098] In order to smooth the output power fluctuation of the high-altitude wind power station, while taking into account the use cost and life of the energy storage system, a double-loop fuzzy power smoothing control strategy based on genetic algorithm optimization suppression selection is designed as shown in Figure 4 As shown in Figure 4 Loop 1 is to smooth the total power of the wind power output, avoiding 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 of the demand for smoothing the wind farm power fluctuation. Loop 2 uses the SOC state of the energy storage system to reasonably distribute the output power of the hybrid energy storage energy management system. Since the charge-discharge times and service life of the vanadium flow battery are much higher than those of the lithium battery, the power distribution principle of this loop is to reduce the charge-discharge times of the lithium battery, and the vanadium flow battery is preferred to output power.

[0099] ΔP WF (t) and the lithium battery state of charge SOCLB(t) are taken as inputs of the fuzzy controller A to obtain the suppression coefficient Ka. According to the calculated output active power fluctuation ΔP WF (t), the maximum actual output power Ph(t) of the hybrid energy storage system can be obtained as:

[0100] P h (t) = K a (t) x ΔP WF (t)

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

[0102]

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

[0104] The allocation coefficient Kb(t) is obtained by using ΔSOCVRB(t) and the lithium battery's state of charge SOCVRB(t) as inputs to the fuzzy controller B. Therefore, the actual output power allocation between the vanadium redox 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] In the formula, P VRB (t) represents the output power of the vanadium redox flow battery at time t, P LB (t) represents the output power of the lithium battery at time t.

[0108] The SOC value of each battery is updated in real time based on the actual output power of the vanadium redox flow battery and the lithium battery to reflect the real-time state of the batteries in the energy storage system. This ensures that the control strategy can be dynamically adjusted according to the actual situation and provides an important basis for the design of fuzzy logic control strategies.

[0109]

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

[0111] To clarify the process by which fuzzy controllers A and B map input variables to output variables, this application also provides input-to-output mapping rules to adjust the 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), the membership function of the output variable Ka. Figure 5 As shown, ΔP WFIn the membership function of SOCLB(t), L (low), Z (small), M (medium), HM (medium high), and H (high) represent five fuzzy subsets, respectively, with a range of [0, 1]. In the membership function of Ka, L (low), LM (small), M (medium), HM (medium high), and H (high) represent five fuzzy subsets, respectively, with a range of [0, 1]. The reasoning result of the fuzzy controller A is shown in Fig. 3. Figure 6

[0112] Similarly, the fuzzy controller B is defined in a similar way to the fuzzy controller A. The membership functions of the input variables ΔSOCVRB(t) and SOCVRB(t) and the output variable Kb of the fuzzy controller B are shown in Fig. 4. In the membership function of ΔSOCVRB(t), NB (negative big), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), and PB (positive big) represent seven fuzzy subsets, respectively, with a range of [-∞, +∞]. In the membership function of SOCVRB(t), L (low), Z (small), M (medium), HM (medium high), and H (high) represent five fuzzy subsets, respectively, with a range of [0, 1]. In the membership function of Kb, L (low), LM (small), M (medium), HM (medium high), and H (high) represent five fuzzy subsets, respectively, with a range of [0, 1]. The reasoning result of the fuzzy controller B is shown in Fig. 5. Figure 7 Figure 8

[0113] As can be seen from Fig. 6, the vanadium flow battery is frequently charged and discharged, and undertakes part of the charging and discharging task of the lithium battery, so that the lithium battery runs smoothly, and frequent charging and discharging is avoided. Figure 9- Figure 10 As can be seen from Fig. 7, the output power fluctuation of the high-altitude wind power station is large when the hybrid energy storage control is not used, and after the hybrid energy storage system is added and the designed double-loop fuzzy power smoothing control strategy is applied, the output power of the high-altitude wind power station becomes smoother, which is conducive to improving the grid stability. At the same time, the overcharging and discharging of the hybrid energy storage system is avoided, the charging and discharging times of the lithium battery are reduced, the service life of the lithium battery is prolonged, and the output power of the wind power is effectively smoothed.

[0114] Figure 11

[0115] ​​​​​Based on the same inventive concept, the application further provides a high-altitude wind power station power fluctuation suppression control device for implementing the above-mentioned high-altitude wind power station power fluctuation suppression control method. The device provides a solution similar to the implementation solution described in the above-mentioned method, and therefore the specific limitations in one or more high-altitude wind power station power fluctuation suppression control device embodiments provided below can refer to the limitations of the high-altitude wind power station power fluctuation suppression control method described above, which will not be repeated here.

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

[0117] An output power fluctuation adjustment and smoothing module is configured to adjust and smooth the output power fluctuation of the high-altitude wind power station based on a high-altitude wind storage combined power generation system, wherein the high-altitude wind storage combined power generation system comprises a high-altitude wind power generation system, a hybrid energy storage system, and a control system; the high-altitude wind power generation system comprises a high-altitude wind power station and a flexible DC transmission system; and the hybrid energy storage system comprises an energy-type energy storage device and a power-type energy storage device.

[0118] A predicted fluctuation amount determination module is configured to determine a predicted fluctuation amount of the active power output by the high-altitude wind power station based on the real-time active power output by the high-altitude wind power station.

[0119] A grid-connection expected power curve determination module is configured to suppress the output power fluctuation of the high-altitude wind power station by using a weighted moving average filtering algorithm based on the predicted fluctuation amount of the active power output by the high-altitude wind power station, and determine a grid-connection expected power curve.

[0120] A double-closed-loop fuzzy power smoothing control strategy formulation module is configured to formulate a double-closed-loop fuzzy power smoothing control strategy by taking the grid-connection expected power curve as a power target reference for closed-loop control.

[0121] A suppression control module is configured to suppress 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 can be a server or a terminal. The computer device comprises a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store high-altitude wind power station power fluctuation suppression control data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a high-altitude wind power station power fluctuation suppression control method.

[0123] In an exemplary embodiment, a computer device is provided, which comprises a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above method.

[0124] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the above method.

[0125] In an exemplary embodiment, a computer program product is provided, which comprises a computer program. The computer program is executed by a processor to implement the above method.

[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. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, databases or other media 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 (ReadOnly Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0127] In the present application, all actions of obtaining signals, information or data are carried out in accordance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization of the owner of the corresponding device.

[0128] The database involved in each embodiment provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in each embodiment provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0129] Each technical feature of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0130] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for power fluctuation smoothing control of a high-altitude wind farm, characterized by, The high-altitude wind power station power fluctuation suppression 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 adjusted and smoothed; the high-altitude wind storage combined power generation system comprises a high-altitude wind power generation system, a hybrid energy storage system and a control system; the high-altitude wind power generation system comprises a high-altitude wind power station and a flexible direct-current power transmission system; the hybrid energy storage system comprises an energy-type energy storage device and a power-type energy storage device; According to the real-time output active power of the high-altitude wind power station, the estimated fluctuation amount of the output active power of the high-altitude wind power station is determined; Based on the estimated fluctuation amount of the output active power of the high-altitude wind power station, the output power fluctuation of the high-altitude wind power station is suppressed by using a weighted moving average filtering algorithm to determine a grid-connected expected power curve; The grid-connected expected power curve is taken as a power target reference of closed-loop control, a double-closed-loop fuzzy power smoothing control strategy is formulated, and specifically comprises: The estimated fluctuation amount and the state of charge of the power-type energy storage device are taken as inputs of a fuzzy controller A to determine a suppression coefficient; According to the suppression coefficient and the estimated fluctuation amount, the maximum actual power output of the hybrid energy storage system is determined; The state of charge variation amount and the state of charge of the energy-type energy storage device are taken as inputs of a fuzzy controller B to determine a distribution coefficient; According to the distribution coefficient and the maximum actual power, the actual output powers of the energy-type energy storage device and the power-type energy storage device are determined; The state of charge of the energy-type energy storage device and the power-type energy storage device is updated in real time according to the actual output powers of the energy-type energy storage device and the power-type energy storage device, and the double-closed-loop fuzzy power smoothing control strategy is dynamically adjusted; The output power of the high-altitude wind power station is suppressed according to the double-closed-loop fuzzy power smoothing control strategy.

2. The high-altitude wind farm power fluctuation smoothening control method according to claim 1, characterized in that, Based on the estimated fluctuation amount of the output active power of the high-altitude wind power station, the output power fluctuation of the high-altitude wind power station is suppressed by using a weighted moving average filtering algorithm to determine a grid-connected expected power curve, and specifically comprises: Based on the estimated fluctuation amount of the output active power of the high-altitude wind power station, a target function and a constraint condition are constructed; 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 by using a genetic algorithm to determine the fitness value of each candidate solution; the candidate solution is a filtering coefficient; It is evaluated whether the fitness value of each candidate solution meets the constraint condition, and the fitness value meeting the constraint condition is selected; A roulette wheel selection strategy is adopted, and individuals are selected according to the selected fitness values, and the individuals are subjected to a crossover operation and a mutation operation to determine a new population; The new population is taken as the original population, and the process of "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 by using a genetic algorithm to determine the fitness value of each candidate solution" is returned, and the fitness of the new population is evaluated again until the iteration process meets a termination condition, and the best filtering coefficient is determined; According to the best filtering coefficient, the output power fluctuation of the high-altitude wind power station is suppressed to determine a grid-connected expected power curve.

3. The high-altitude wind farm power fluctuation smoothening control method according to claim 2, characterized in that, construct a target function and constraint conditions based on the estimated fluctuation of the active power output of the high-altitude wind power station, specifically including: Utilizing constructing an objective function; wherein P HESS (t) is the output power of the hybrid energy storage system; T is the maximum time step of sampling; t is the time step index; f(γ) is the fitness value; λ is the penalty coefficient; v1(t) is the violation degree of the fluctuation constraint within 1 minute; v 10 (t) is the violation degree of the fluctuation constraint within 10 minutes; Utilizing constructing constraint conditions; wherein, Q 1min is the maximum ramping amount allowed for power fluctuation within 1 minute; P ref (t) is the grid-connected expected power output target at time t; P ref (t-1) is the grid-connected expected power output target at time t-1; P ref (θ) is the grid-connected expected power output target at time 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 high-altitude wind farm power fluctuation smoothening control method according to claim 3, characterized in that, According to the optimal filter coefficient, the output power fluctuation of the high-altitude wind power station is suppressed, and the grid-connected expected power curve is determined, specifically including: Utilizing P ref (t) = γP WF (t) + (1 - γ)P ref (t - 1) determines the grid-connection expected power curve; wherein, γ is the optimal filtering 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 high-altitude wind turbine power fluctuation smoothening control method according to claim 1, characterized in that, According to the actual output power of the energy-type storage device and the power-type storage device, the state of charge of the energy-type storage device and the power-type storage device is updated in real time, and the double-loop fuzzy power smoothing control strategy is dynamically adjusted, specifically including: Utilizing and updating the state of charge of the energy storage device and the power storage device in real time, dynamically adjusting 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 t+1 time; SOC VRB (t) is the state of charge of the energy storage device at t time; 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 t+1 time; SOC LB (t) is the state of charge of the power storage device at t time; P LB (t) is the actual output power of the power storage device; C LB is the rated capacity of the power storage device.

6. A high-altitude wind farm power fluctuation smoothing control device, characterized by, The high-altitude wind power station power fluctuation suppression control device executes the high-altitude wind power station power fluctuation suppression control method of any one of claims 1-5, and the high-altitude wind power station power fluctuation suppression control device includes: The output power fluctuation adjustment and smoothing module is used for adjusting and smoothing 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 AC transmission system; the hybrid energy storage system includes an energy-type storage device and a power-type storage device; The estimated fluctuation determination module is used for determining the estimated fluctuation of the active power output of the high-altitude wind power station according to the real-time output active power of the high-altitude wind power station; The grid-connected expected power curve determination module is used for suppressing 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, and determining the grid-connected expected power curve by using a weighted moving average filter algorithm; The double-loop fuzzy power smoothing control strategy formulation module is used for formulating a double-loop fuzzy power smoothing control strategy by taking the grid-connected expected power curve as a power target reference for closed-loop control; The suppression control module is used for suppressing the output power of the high-altitude wind power station according to the double-loop fuzzy power smoothing control strategy.

7. A computer device comprising: The memory, the processor, and the computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the high-altitude wind power station power fluctuation suppression control method of any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the high-altitude wind power station power fluctuation suppression control method of any one of claims 1-5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the high-altitude wind power station power fluctuation suppression control method of any one of claims 1-5.

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