Hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation

Abstract

The invention discloses a hybrid energy storage capacity optimal configuration method for stabilizing wind power fluctuation, and belongs to the technical field of wind power generation, and the method comprises the steps: carrying out the clustering of wind power output data based on a K-means clustering algorithm, and obtaining a plurality of wind power output scenes; the wind power output data is decomposed based on an empirical mode decomposition method to obtain a multi-order intrinsic mode function, the multi-order intrinsic mode function is reconstructed into a low-frequency component and a high-frequency component, the low-frequency component is directly connected to a grid, the high-frequency component serves as a power task of the hybrid super-capacitor energy storage system, and the hybrid super-capacitor energy storage system is defined as a target system; establishing a capacity configuration model of the target system, and respectively constructing an optimization objective function and a model constraint set of the capacity configuration model based on the cost input and the constraint condition of the target system; and solving the capacity configuration model based on a genetic algorithm to obtain an initial optimization result, introducing a variable reference line and a fluctuation penalty coefficient, and performing optimization again. According to the invention, the efficiency of stabilizing the wind power fluctuation is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations. Background Art

[0002] Hybrid supercapacitors are a new type of high-performance energy storage device that has attracted widespread attention in recent years. They have the dual characteristics of secondary batteries and electrostatic capacitors and are considered to be a new type of chemical power source between traditional capacitors and secondary batteries. Modern power systems have higher requirements for the capacity, power, response speed and cost of energy storage devices. Traditional energy storage technologies cannot meet all the requirements of power systems at the same time.

[0003] Compared with secondary batteries, supercapacitors have the characteristics of high power density, rapid charging, long cycle life, high energy utilization efficiency, high safety, clean and environmentally friendly, etc. They have been widely used in new energy vehicles, rail transportation, solar and wind power generation and storage, industrial machinery and equipment, energy-saving elevators, military aerospace, ships and other fields.

[0004] Existing methods for optimizing the configuration of energy storage capacity for smoothing wind power fluctuations, such as a Chinese patent application with publication number CN104600728A, propose a method for optimizing the configuration of hybrid energy storage capacity for smoothing wind power fluctuations, including: formulating a coordinated control strategy, constraining the allocation reference power of empirical mode decomposition in the time domain according to the energy state SOE1(t-1) of the power-type energy storage, using fuzzy control theory to formulate a correction adjustment coefficient Kc with SOE1(t-1) and ΔSOE1(t), and allocating the actual output power of the hybrid energy storage; establishing an economic model with the hybrid energy storage cost as the objective function and the constraints of the model; and optimizing the configuration scheme of the hybrid energy storage system using a genetic algorithm with chaotic perturbations and quantum computing based on the established economic model and the constraints of the model.

[0005] However, the chaos genetic algorithm in the above-mentioned prior art can improve the optimization calculation efficiency, but it may have the problem of early convergence, resulting in the inability to find the global optimal solution, resulting in poor effect in smoothing wind power fluctuations.

[0006] Another example is the Chinese patent application with publication number CN117175659A, which proposes a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations, and establishes a full life cycle cost model for a hybrid energy storage system composed of batteries and supercapacitors. It takes into account cost minimization while smoothing wind power fluctuations, and completes the capacity optimization configuration of the hybrid energy storage system composed of batteries and supercapacitors. First, the variational mode decomposition (VMD) method is used to decompose the original wind power, and on the basis of meeting the power fluctuation requirements, the grid-connected power and the compensation power of the hybrid energy storage system are obtained respectively; then, the full life cycle cost model of the hybrid energy storage system is constructed, which comprehensively considers the constraints such as the state of charge of the energy storage, the charging and discharging power, and completes the capacity optimization configuration of the hybrid energy storage system with the goal of optimal economic efficiency.

[0007] However, the above-mentioned prior art may not fully consider the impact of the loss caused by frequent charging and discharging of the energy storage system on the economic benefits and energy storage life, resulting in poor economic benefits.

[0008] Therefore, a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations is needed, which can comprehensively consider the wind power leveling power fluctuation ability and system economic benefits. Summary of the invention

[0009] In order to solve the above problems, the present invention provides a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations, so as to solve the problems in the prior art.

[0010] In order to achieve the above-mentioned invention object, the present invention proposes a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations, comprising:

[0011] S1: Obtain wind power output data within a target time period, and cluster the wind power output data based on a K-means clustering algorithm to obtain a variety of wind power output scenarios;

[0012] S2: Decomposing the wind power output data in the wind power output scenario based on the empirical mode decomposition method to obtain a multi-order intrinsic mode function, reconstructing the multi-order intrinsic mode function into a low-frequency component and a high-frequency component based on the wind power fluctuation quantity limitation, the low-frequency component is directly connected to the grid, the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system, and the hybrid supercapacitor energy storage system is defined as the target system;

[0013] S3: mathematically modeling the target system, establishing a capacity configuration model for the target system based on a capacity management strategy, and constructing an optimization objective function of the capacity configuration model based on the investment cost, operation and maintenance cost, and opportunity compensation cost of the target system;

[0014] S4: establishing a model constraint set of the capacity configuration model based on the fluctuation smoothing module boundary constraint, power balance constraint, charge and discharge power constraint and energy storage state constraint of the target system;

[0015] S5: Solve the constructed capacity configuration model based on the genetic algorithm to obtain the initial optimization result of the target system, introduce a variable baseline and a fluctuation penalty coefficient, optimize the initial optimization result again, and obtain the final capacity configuration optimization result.

[0016] Furthermore, obtaining multiple wind power output scenarios includes the following steps:

[0017] Traverse all the data in the wind power output data, and calculate the similarity distance δ between each of the data and the initial cluster centroid based on the first formula, where the first formula is: Among them, x i and i The p-dimensional data and the p-dimensional initial clustering centroids are respectively, the wind power output data is divided into multiple clusters based on the similarity distance, the position of the new centroid in each cluster and the similarity distance between each data in the cluster and the new centroid are recalculated, and this operation is repeated until the distance between the position of the new centroid and the position of the centroid in the previous round is less than a first threshold;

[0018] Calculate the average value of the wind power output data in each clustering result at this time, and take the sum of the differences between all the historical wind power data in the clustering result and the average value as the cumulative fluctuation of the clustering result, arrange all the cumulative fluctuations in order from small to large, select the cumulative fluctuation corresponding to the median, and take the day of the clustering result corresponding to the cumulative fluctuation as the corresponding wind power output scenario.

[0019] Further, reconstructing the plurality of intrinsic mode functions into low-frequency components and high-frequency components comprises the following steps:

[0020] Based on the top-down superposition of the intrinsic mode functions of each order, high-frequency components of each order are generated. The first formula of the specific reconstruction method is: Among them, I MFp is the pth intrinsic mode function, r es is the residual obtained based on the empirical mode decomposition, f 2c(P+1) is the high frequency component of the p+1th order;

[0021] Based on the bottom-up superposition of the intrinsic mode functions of each order to generate low-frequency components of each order, the second formula of the specific reconstruction method is: Among them, c 2f(p+1) is the low-frequency component of the p+1th order;

[0022] Based on the wind power allocation strategy process, the low-frequency component is directly connected to the grid, and the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system;

[0023] The wind power fluctuation amount of the reconstructed low-frequency component and the high-frequency component is calculated, and the wind power fluctuation amount is the difference between the maximum power and the minimum power within a preset time interval. If the wind power fluctuation amount satisfies the constraint limit, the reconstruction of the low-frequency component and the high-frequency component is completed, and the constraint limit is: Among them, P c2f(p)，m is the power value in the p-th order m period, Δn is the preset time interval, ΔP limit It is the preset fluctuation limit.

[0024] Furthermore, mathematical modeling of the target system includes the following steps:

[0025] The target system is modeled based on an equivalent circuit model, the self-discharge characteristics of the equivalent circuit model are ignored, and the parameters of the equivalent circuit model are calculated based on a third formula, where the third formula is: Among them, R SC and C SG are the equivalent series resistance and equivalent series capacitance, U t-sc is the terminal voltage of the supercapacitor group, C uc and R uc is the capacitance and resistance of the supercapacitor monomer, U t-uc is the terminal voltage of the supercapacitor monomer, M is the number of parallel modules, N is the number of series modules, and the target system is configured based on the parameters of the equivalent circuit model.

[0026] Furthermore, building a capacity configuration model includes the following steps:

[0027] The target system is optimized based on the method of the minimum working voltage of the supercapacitor, and the remaining energy storage ratio ω of the supercapacitor of the optimized target system is calculated based on the fourth formula, and the fourth formula is: Among them, U M is the starting working voltage of the supercapacitor, U min is the minimum operating voltage of the supercapacitor, W s is the residual energy stored in the supercapacitor that is difficult to reuse, W M is the total energy storage of the supercapacitor;

[0028] The energy W1 provided by the supercapacitor after power failure is calculated based on the fifth formula, and the fifth formula is: Wherein, P0 is the power provided by the supercapacitor after power failure, T w is the holding time, η is the conversion efficiency of the converter;

[0029] The capacity W2 provided by the supercapacitor is calculated based on the sixth formula, and the sixth formula is: W2 = 0.5C' SC (U M 2 -U min 2 ), where C′ SC The capacity of the super capacitor itself is calculated based on the seventh formula: SC , the seventh formula is: U0 is the output voltage of the super capacitor after power failure, and I0 is the current of the super capacitor after power failure.

[0030] Furthermore, constructing the optimization objective function includes the following steps:

[0031] An optimization objective function is constructed based on the hybrid supercapacitor energy storage cost and the opportunity compensation cost of wind power fluctuation, and the optimization objective function is: Among them, C is the annual comprehensive cost, C HSC is the energy storage cost of the hybrid supercapacitor, The hybrid supercapacitor energy storage cost C is calculated based on the eighth formula: HSC , the eighth formula is: in, and The investment cost and operation and maintenance cost of the target system are respectively, and the wind power fluctuation opportunity compensation cost is calculated based on the ninth formula The ninth formula is: Among them, C comp is the opportunity compensation cost coefficient, P P-uncomp，n and P N-uncomp，n are respectively the positive undercompensation and negative undercompensation at time n, N s The total time.

[0032] Furthermore, obtaining the investment cost and operation and maintenance cost of the target system includes the following steps:

[0033] The investment cost is calculated based on the tenth formula, which is: in, and are the power and capacity investment cost coefficients of the target system, respectively, and are the rated power and rated energy capacity of the target system respectively, r is the discount rate, Y is the system operation period, and the operation and maintenance cost is calculated based on the eleventh formula The eleventh formula is: Wherein, a is the ratio of the operation and maintenance cost of the target system to its investment cost.

[0034] Furthermore, establishing a model constraint set of the capacity configuration model includes the following steps:

[0035] The target system is used to smooth the fluctuating power Constraints The target system is used to smooth the capacity of fluctuations The constraints are: Based on the twelfth formula, the power task P of the target system at time n is calculated. HSC，n , the twelfth formula is: in, is the power required to charge the target system at time n, is the power that the target system needs to discharge at time n, and the charging and discharging power is constrained based on the thirteenth formula, which is:

[0036] Furthermore, establishing the model constraint set of the capacity configuration model further includes the following steps:

[0037] The energy storage state of the target system is constrained based on the fourteenth and fifteenth formulas, and the fourteenth formula is: Among them, S OCn is the energy storage state at time n, S OCn-1 is the energy storage state at time n-1, η HSC is the charge and discharge efficiency of the target system, the fifteenth formula is: S OCmin ≤S OCn ≤S OCmax , where S OCmin is the minimum value of the target system energy storage state, S OCmax is the maximum value of the target system energy storage state.

[0038] Furthermore, obtaining the initial capacity configuration optimization result of the target system includes the following steps:

[0039] Based on the capacity configuration model, multiple capacity configuration results are obtained, the capacity configuration results are used as individuals of the genetic algorithm, and the mutation probability P of the individuals is calculated based on the sixteenth formula m (X i ), the sixteenth formula is: Where i = 1, 2, ..., M, X i is the i-th individual, is the average mutation probability, e(X i ) is the fitness of the ith individual, and the fitness e(X i), the seventeenth formula is: Where E(X i ) is individual X i The fitness evaluation function is based on the eighteenth formula to obtain the average mutation probability The eighteenth formula is: Excellent individuals are selected based on the mutation probability and fitness, and after crossover and mutation processing are performed on the selected excellent individuals, an initial capacity configuration optimization result is obtained.

[0040] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0041] The present invention comprehensively considers the limitations of chemical batteries in power regulation, proposes to use hybrid supercapacitors to smooth out fluctuations caused by large-scale wind power grid connection, and formulates a strategy for smoothing wind power fluctuations that considers the operating characteristics of hybrid supercapacitors. First, the K-means clustering algorithm is used to cluster the annual wind power output into several types of typical days; then, the original wind power signal is decomposed into direct grid-connected components and energy storage system work tasks using empirical mode decomposition; then, based on the electrochemical energy storage charging and discharging power constraints and storage state constraints, a hybrid supercapacitor energy management strategy is formulated; based on this strategy, an objective function is established with the minimum total cost of the hybrid energy storage system; finally, the genetic algorithm is used to solve the configuration result, and it is further optimized by changing the baseline and introducing a fluctuation penalty coefficient. By comparing the capacity configuration results under the traditional strategy, the effectiveness of the proposed strategy in smoothing fluctuations is verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of the steps of a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations according to the present invention;

[0043] Figure 2 This is a simulation diagram of clustering wind power 365-day 5-minute data using the K-means clustering algorithm of the present invention;

[0044] Figure 3 The power curve of the direct grid-connected component when the wind power fluctuation power limit value is 50 on a typical day 7 of the present invention;

[0045] Figure 4 The energy storage smoothing component power curve of the typical day 7 of the present invention when the wind power fluctuation power limit is 50;

[0046] Figure 5 The low-fluctuation direct grid-connected power curve for typical days 7 of the present invention when the wind power fluctuation power limit is 50;

[0047] Figure 6The power curve of the typical day 7 wind power fluctuation power limit of the present invention is 50, and the high fluctuation on the 8th typical day needs energy storage to smooth out;

[0048] Figure 7 It is an expanded diagram of the wind power grid-connected power fluctuation curve at 0 baseline after the hybrid supercapacitor of the present invention is smoothed on a typical day;

[0049] Figure 8 This is a high-frequency power fluctuation curve diagram of a typical day 7 hybrid supercapacitor of the present invention when the baseline is smoothed and the fluctuation penalty coefficient is increased. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0052] like Figure 1 As shown, a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations includes:

[0053] S1: Obtain wind power output data within the target time period, and cluster the wind power output data based on the K-means clustering algorithm to obtain multiple wind power output scenarios.

[0054] Specifically, current research on hybrid energy storage systems, which are used to smooth out wind power fluctuations, mostly uses chemical batteries as the main body, and the application scenarios are mostly limited to microgrids and distributed energy systems. There is little consideration of the application of hybrid energy storage systems with supercapacitors as the main energy storage in large power grid scenarios. In order to verify the reliability and effectiveness of the present invention, the present invention configures supercapacitor energy storage capacity based on the actual wind power data of a certain city in 2020. The total installed capacity of conventional units and wind turbines in the region is 5588MW, of which wind power installed capacity is 2348MW.

[0055] First, the K-means clustering algorithm is used to cluster the wind power output data for the whole year of 2020, and 8 classic wind power output scenarios are obtained, such as Figure 2As shown in the figure, it is a simulation diagram of the clustering of 365-day 5-minute data of wind power using the K-means clustering algorithm. The number of days and the probability corresponding to each scenario are shown in Table 1. When there is extreme wind power output data in the clustering scenario, the traditional K-means algorithm will cause a certain degree of distortion in the clustering center. The present invention uses the median of the cumulative fluctuation as an indicator to select typical days for various scenarios, that is, to calculate the cumulative fluctuation of all days in various scenarios, and arrange them in order from large to small, and take the days corresponding to the median as the typical days of this type of scenario. This method selects typical days that are not easily affected by extreme data and is more convincing.

[0056]

[0057] Table 1

[0058] S2: Based on the empirical mode decomposition method, the wind power output data in the wind power output scenario is decomposed to obtain multi-order intrinsic mode functions. Based on the wind power fluctuation quantity limitation, the multi-order intrinsic mode functions are reconstructed into low-frequency components and high-frequency components. The low-frequency components are directly connected to the grid, and the high-frequency components are used as the power task of the hybrid supercapacitor energy storage system. The hybrid supercapacitor energy storage system is defined as the target system.

[0059] Specifically, in a wind power output scenario, for example, the wind power output data at multiple time points within a day is processed based on the EDM empirical mode decomposition to obtain a multi-order intrinsic mode function IMF. Each IMF represents a different frequency component. The wind power grid-connected fluctuation limit is used as the dividing line, and the multi-order intrinsic mode function is reconstructed into a low-frequency component and a high-frequency component. The low-frequency component usually contains the main trend and slower fluctuations of the wind power output, while the high-frequency component contains rapid changes and instantaneous fluctuations. Since the low-frequency component has a smaller fluctuation and has less impact on the power grid, the low-frequency component is directly connected to the grid, and the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system, and the energy storage system performs corresponding charging and discharging operations to smooth out the fluctuations.

[0060] As the fluctuation limit decreases, the direct grid-connected component becomes smoother, and the energy storage smoothing component becomes rougher and larger in amplitude, so the high-frequency component (rapid fluctuation) that needs to be smoothed by the energy storage system will become more and more. The present invention decides to select a wind power fluctuation power limit of 50MW. Taking a typical day 7 as an example, Figure 3 As shown in Figure 1, it is the direct grid-connected component power curve when the wind power fluctuation limit is 50 on a typical day. Figure 4 As shown in Figure 1, the energy storage smoothing component power curve when the wind power fluctuation power limit is 50 on typical day 7. The low fluctuation direct grid-connected power curves for all 8 typical days are shown in Figure 1. Figure 5 As shown in the figure, the power curve with high fluctuations requiring energy storage to smooth out is as follows Figure 6 shown.

[0061] S3: Conduct mathematical modeling of the target system, establish a capacity configuration model for the target system based on the capacity management strategy, and construct an optimization objective function of the capacity configuration model based on the investment cost, operation and maintenance cost, and opportunity compensation cost of the target system.

[0062] Specifically, the hybrid supercapacitor energy storage system is modeled based on the equivalent circuit model (RC model). The RC model is used to simulate the electrical characteristics of energy storage devices such as supercapacitors or batteries. The RC model can be used to predict the voltage and current response of supercapacitors in practical applications, thereby evaluating the performance of supercapacitors. Based on the characteristics of hybrid supercapacitor energy storage, an energy management strategy for the hybrid supercapacitor energy storage system is constructed. On the basis of the management strategy, an HSC capacity configuration model that takes into account both economy and wind power fluctuations is established. According to the various cost requirements of the hybrid supercapacitor energy storage system, an optimization objective function of the HSC capacity configuration model is established. By optimizing the objective function, it can help determine the most appropriate energy storage capacity and configuration, avoid waste of resources, and reduce the overall cost of ownership of the energy storage system, ensuring that the energy storage system can effectively smooth wind power fluctuations and improve the operating efficiency of wind farms and the stability of the power grid.

[0063] S4: A model constraint set of the capacity configuration model is established based on the target system's fluctuation smoothing module boundary constraints, power balance constraints, charging and discharging power constraints, and energy storage state constraints.

[0064] Specifically, the target system needs to have sufficient capacity to absorb and release the fluctuations of wind power, but it cannot exceed its physical and safety limits. The boundary constraints of the fluctuation smoothing module are set according to the maximum power and energy range that the supercapacitor energy storage system can handle. The power balance constraint is to ensure that at any given time, the charging and discharging operations of the supercapacitor energy storage system are balanced with the power output of the wind farm and the grid demand. The charging and discharging power constraints define the maximum and minimum powers at which the supercapacitor energy storage system can charge and discharge, ensuring the stable operation of the system while preventing overload of the equipment. The energy storage state constraint involves the limitation of the charge state of the supercapacitor, ensuring that the energy storage system will not be overcharged or over-discharged, thereby extending its service life and ensuring safe operation.

[0065] Through these constraints, a model constraint set can be constructed to determine the optimal capacity configuration of the supercapacitor energy storage system, helping to maximize cost-effectiveness while ensuring the stability and reliability of wind power output.

[0066] S5: Based on the genetic algorithm, the constructed capacity configuration model is solved to obtain the initial optimization result of the target system. The variable baseline and fluctuation penalty coefficient are introduced to optimize the initial optimization result again to obtain the final capacity configuration optimization result.

[0067] Specifically, the present invention uses the Matlab platform and genetic algorithm to solve the capacity configuration model configured by optimizing the objective function and the model constraint set. The algorithm will generate a set of potential solutions (called a population), each of which represents a possible capacity configuration scheme. The algorithm will evaluate the fitness of these schemes, that is, their performance in satisfying the model constraints and optimizing the objective function. Through the iterative process of the genetic algorithm, the initial optimization result of the target system can be obtained, that is, a preliminary optimal capacity configuration scheme. In order to further improve the quality of the optimization results, a variable baseline and a fluctuation penalty coefficient can be introduced. The variable baseline is a dynamically adjusted reference value used to provide more accurate guidance during the optimization process. The fluctuation penalty coefficient is used to quantify the impact of wind power output fluctuations on system performance and is added to the objective function to ensure that the optimization results can better smooth wind power fluctuations. Using the variable baseline and the fluctuation penalty coefficient, the initial optimization results are adjusted and improved to obtain the final capacity configuration optimization results.

[0068] like Figure 7 As shown in the figure, it is the expansion diagram of the wind power grid-connected power fluctuation curve at the 0 baseline after the hybrid supercapacitor is smoothed on a typical day 7. The traditional 0 baseline may not be able to effectively handle the negative fluctuation of the high-frequency component, resulting in the superposition of the fluctuation after smoothing and the fluctuation of the EMD low-frequency component, resulting in a larger fluctuation, thereby increasing the uncontrollable risk. On this basis, the baseline variable is introduced to replace the 0 baseline, and the penalty coefficient is added to the capacity configuration model. The smoothing result is as follows Figure 8 As shown in the figure, it is a high-frequency power fluctuation curve of a typical day 7 hybrid supercapacitor after the baseline is changed and the fluctuation penalty coefficient is increased. In the variable baseline mode, the value of the optimal baseline and the optimal value of the capacity configuration are obtained, and the grid-connected power fluctuation of the final result is the same as the low-frequency component fluctuation of the EMD, that is, it is completely controllable, avoiding the superposition of the fluctuation after high-frequency smoothing under the 0 baseline and the fluctuation of the low-frequency component of the EMD, resulting in a larger fluctuation, that is, the risk of uncontrollable.

[0069] The economic costs and smoothing effects of various capacity configuration methods are shown in Table 2. After changing the baseline and increasing the penalty coefficient, compared with capacity configuration based on the annual operation curve and capacity configuration based on the 0 baseline, the economic cost increases slightly but the fluctuation smoothing effect is greatly enhanced.

[0070]

[0071] Table 2

[0072] The present invention comprehensively considers the limitations of chemical batteries in power regulation, proposes to use hybrid supercapacitors to smooth out fluctuations caused by large-scale wind power grid connection, and formulates a strategy for smoothing wind power fluctuations that considers the operating characteristics of hybrid supercapacitors. First, the K-means clustering algorithm is used to cluster the annual wind power output into several types of typical days; then, the original wind power signal is decomposed into direct grid-connected components and energy storage system work tasks using empirical mode decomposition; then, based on the electrochemical energy storage charging and discharging power constraints and storage state constraints, a hybrid supercapacitor energy management strategy is formulated; based on this strategy, an objective function is established with the minimum total cost of the hybrid energy storage system; finally, the genetic algorithm is used to solve the configuration result, and it is further optimized by changing the baseline and introducing a fluctuation penalty coefficient. By comparing the capacity configuration results under the traditional strategy, the effectiveness of the proposed strategy in smoothing fluctuations is verified.

[0073] As a preferred technical solution of the present invention, obtaining multiple wind power output scenarios includes the following steps:

[0074] Traverse all the data in the wind power output data, and calculate the similarity distance δ between each data and the initial cluster centroid based on the first formula. The first formula is: Among them, x i and i They are p-dimensional data and p-dimensional initial clustering centroids respectively. The wind power output data is divided into multiple clusters based on the similarity distance. The position of the new centroid in each cluster and the similarity distance between each data in the cluster and the new centroid are recalculated. This operation is repeated until the distance between the position of the new centroid and the centroid position of the previous round is less than the first threshold.

[0075] Calculate the average value of the wind power output data in each clustering result at this time, and take the sum of the differences between all historical wind power data in the clustering result and the average value as the cumulative fluctuation of the clustering result. Arrange all the cumulative fluctuations in order from small to large, select the cumulative fluctuation corresponding to the median, and take the day of the clustering result corresponding to the cumulative fluctuation as the corresponding wind power output scenario.

[0076] Specifically, by analyzing wind power output data through the K-means clustering algorithm and updating the centroid position, the data points of each cluster can be better represented and the accuracy of clustering can be improved. By selecting the cumulative fluctuation corresponding to the median, a representative fluctuation level can be found to define the wind power output scenario. This method can reduce the impact of extreme values ​​and improve the representativeness of the scenario.

[0077] Reconstructing multiple intrinsic mode functions into low-frequency components and high-frequency components includes the following steps:

[0078] Based on the top-down superposition of each order of intrinsic mode functions, each order of high frequency components is generated. The first formula of the specific reconstruction method is: Among them, I MFP is the pth intrinsic mode function, r es is the residual obtained based on empirical mode decomposition, f 2c(P+1) is the p+1th order high frequency component.

[0079] Based on the bottom-up superposition of each order of intrinsic mode functions to generate each order of low-frequency components, the second formula of the specific reconstruction method is: Among them, c 2f(p+1) is the p+1th order low-frequency component.

[0080] Based on the wind power distribution strategy process, the low-frequency component is directly connected to the grid, and the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system.

[0081] Calculate the wind power fluctuation of the reconstructed low-frequency component and high-frequency component. The wind power fluctuation is the difference between the maximum power and the minimum power in the preset time interval. If the wind power fluctuation meets the constraints, the reconstruction of the low-frequency component and the high-frequency component is completed. The constraints are: Among them, P c2f(p)，m is the power value in the pth order m period, Δn is the preset time interval, ΔP limit It is the preset fluctuation limit.

[0082] Specifically, real-time output data of wind farms are collected. These data represent the power generation capacity of wind farms at different time points. Low-frequency components usually contain the main trends and slower fluctuations of wind power output. These components have smaller fluctuations and can be directly integrated into the power grid without the need for additional energy storage equipment to smooth them. High-frequency components contain rapid changes and instantaneous fluctuations. These components need to be smoothed by the energy storage system. The hybrid supercapacitor energy storage system (HSC) is used as a power task to absorb and release these high-frequency fluctuations, thereby reducing the impact on the power grid. Wind power fluctuation amplitude refers to the difference between the maximum and minimum wind power output within a preset time interval. This indicator is used to evaluate the volatility of wind power output and the fluctuation amplitude that the energy storage system needs to handle. If the calculated wind power fluctuation amplitude meets the preset fluctuation amplitude limit, then the reconstruction of the low-frequency component and the high-frequency component is considered to be successful, which means that the energy storage system can effectively smooth wind power fluctuations and ensure the stable operation of the power grid.

[0083] Mathematical modeling of the target system includes the following steps:

[0084] The target system is modeled based on the equivalent circuit model, the self-discharge characteristics of the equivalent circuit model are ignored, and the parameters of the equivalent circuit model are calculated based on the third formula. The third formula is: Among them, R SC and C SC are the equivalent series resistance and equivalent series capacitance, U t-SCis the terminal voltage of the supercapacitor group, C uc and R uc is the capacitance and resistance of the supercapacitor monomer, U t-uc is the terminal voltage of the supercapacitor monomer, M is the number of parallel modules, N is the number of series modules, and the target system is configured based on the parameters of the equivalent circuit model.

[0085] Specifically, the equivalent series resistance represents the internal resistance of the supercapacitor, which affects the charging and discharging efficiency and thermal management. The equivalent series capacitance represents the ability of the supercapacitor to store energy. The terminal voltage of the supercapacitor group is used to calculate the energy storage state of the entire system. The equivalent circuit model can accurately simulate the charging and discharging behavior of the supercapacitor, which helps to predict its performance in practical applications. By understanding the various parameters of the supercapacitor, the configuration of the energy storage system can be optimized to improve its performance and efficiency. Understanding the ESR and ESC of the supercapacitor is crucial to predicting its life and maintenance cycle.

[0086] Building a capacity configuration model includes the following steps:

[0087] The target system is optimized based on the method of the minimum working voltage of the supercapacitor, and the remaining energy storage ratio ω of the supercapacitor of the optimized target system is calculated based on the fourth formula. The fourth formula is: Among them, U M is the starting working voltage of the supercapacitor, U min is the minimum operating voltage of the supercapacitor, W S The residual energy stored in the supercapacitor is difficult to reuse. M is the total energy storage of the supercapacitor.

[0088] The energy W1 provided by the supercapacitor after power failure is calculated based on the fifth formula, which is: Among them, P0 is the power provided by the supercapacitor after power failure, T w is the holding time, and η is the conversion efficiency of the converter.

[0089] The capacity W2 that the supercapacitor can provide is calculated based on the sixth formula, which is: W2 = 0.5C' SC (U M 2 -U min 2 ), where C′ SC The capacity of the supercapacitor itself is calculated based on the seventh formula: SC , the seventh formula is: U0 is the output voltage of the supercapacitor after power failure, and I0 is the current of the supercapacitor after power failure.

[0090] Specifically, optimizing the target system based on the method of supercapacitor minimum operating voltage can ensure that the supercapacitor operates above its minimum operating voltage, avoid performance degradation caused by voltage below the design threshold, and improve the reliability of the entire system. By calculating the remaining energy storage ratio of the supercapacitor, the energy storage of the capacity configuration model can be more effectively managed to ensure that sufficient energy is available at critical moments. By calculating the energy provided by the supercapacitor after power failure, energy resources can be better planned and allocated to ensure the continuity of critical operations.

[0091] Constructing the optimization objective function includes the following steps:

[0092] The optimization objective function is constructed based on the hybrid supercapacitor energy storage cost and the opportunity compensation cost of wind power fluctuation. The optimization objective function is: Among them, C is the annual comprehensive cost, C HSC is the hybrid supercapacitor energy storage cost, The opportunity compensation cost for wind power fluctuation is calculated based on the eighth formula: HSC , the eighth formula is: in, and The investment cost and operation and maintenance cost of the target system are respectively, and the opportunity compensation cost of wind power fluctuation is calculated based on the ninth formula The ninth formula is: Among them, c comp is the opportunity compensation cost coefficient, P P-uncomp，n and P N-uncomp，n are respectively the positive undercompensation and negative undercompensation at time n, N S The total time.

[0093] Specifically, the capacity configuration strategy optimization goal of the hybrid supercapacitor energy storage system is to minimize the energy storage cost and opportunity compensation cost. Based on this, an objective function considering economic cost is constructed. The most economically favorable energy storage system configuration is achieved by minimizing the annual comprehensive cost in the objective function. The annual comprehensive cost may include the hybrid supercapacitor energy storage cost and the opportunity compensation cost caused by wind power fluctuations. The hybrid supercapacitor energy storage cost involves the initial investment cost of the HSC system, including the cost of purchasing and installing supercapacitors, as well as their maintenance costs throughout their service life. The opportunity compensation cost for wind power fluctuations refers to the additional cost that may need to be paid due to wind power fluctuations. If the HSC system cannot fully smooth out wind power fluctuations, it may need to call on other resources to supplement, which will incur additional costs. Through optimization, the overall cost of ownership of the energy storage system can be reduced, ensuring that the energy storage system can operate stably under various conditions, reducing the fluctuation of wind power output, and improving the stability of the power grid.

[0094] The acquisition of the investment cost and operation and maintenance cost of the target system includes the following steps:

[0095] The investment cost is calculated based on the tenth formula, which is: in, and are the power and capacity investment cost coefficients of the target system, and are the rated power and rated energy capacity of the target system, r is the discount rate, Y is the system operation period, and the operation and maintenance cost is calculated based on the eleventh formula The eleventh formula is: Among them, a is the proportion of the target system's operating and maintenance costs to its investment cost.

[0096] Establishing the model constraint set for the capacity configuration model includes the following steps:

[0097] The target system is used to smooth the fluctuating power Constraints The target system's capacity to smooth fluctuations The constraints are: Calculate the power task P of the target system at time n based on the twelfth formula HSC，n , the twelfth formula is: in, is the power that the target system needs to charge at time n, is the power that the target system needs to discharge at time n. Based on the thirteenth formula, the charging and discharging power is constrained. The thirteenth formula is:

[0098] Specifically, the constraints of the target system include the configuration boundary constraint of the supercapacitor energy storage module for smoothing fluctuations, power balance constraint, charge and discharge power constraint, and energy storage state constraint. The configuration boundary constraint of the smoothing fluctuation module refers to the capacity constraint of the target system for smoothing fluctuations, and the power balance constraint refers to the power constraint of the target system for smoothing fluctuations. The supercapacitor energy storage must reserve some power and capacity space to smooth wind power fluctuations, so the power and capacity of this module need to be kept within the power and capacity of the supercapacitor energy storage. The hybrid energy storage power task is divided into two parts: positive fluctuations and negative fluctuations. When the hybrid energy storage power task is positive fluctuations, the hybrid supercapacitor energy storage needs to charge to absorb the fluctuations; when it is negative fluctuations, the hybrid supercapacitor energy storage needs to discharge to compensate for the fluctuations.

[0099] Establishing a model constraint set for the capacity configuration model also includes the following steps:

[0100] Based on the fourteenth and fifteenth formulas, the energy storage state of the target system is constrained. The fourteenth formula is: Among them, SOCn is the energy storage state at time n, S OCn-1 is the energy storage state at time n-1, P HSC is the charge and discharge efficiency of the target system, the fifteenth formula is: S OCmin ≤S OCn ≤S OCmax , where S OCmin is the minimum value of the target system energy storage state, S OCmax It is the maximum value of the target system energy storage state.

[0101] Specifically, by limiting the energy storage state within a safe range, the supercapacitor can be prevented from being damaged by overcharging or over-discharging, ensuring the safe operation of the system. Avoiding extreme charging and discharging states helps extend the service life of the supercapacitor, reduce replacement frequency, and reduce maintenance costs.

[0102] Obtaining the initial capacity configuration optimization result of the target system includes the following steps:

[0103] Based on the capacity configuration model, multiple capacity configuration results are obtained. The capacity configuration results are used as individuals of the genetic algorithm in Matlab, and the mutation probability P of the individuals is calculated based on the sixteenth formula. m (X i ), the sixteenth formula is: Where i = 1, 2, ..., M, X i is the i-th individual, is the average mutation probability, e(X i ) is the fitness of the ith individual, and the fitness e(X i ), the seventeenth formula is: Where E(X i ) is individual X i The fitness evaluation function is based on the eighteenth formula to obtain the average mutation probability The eighteenth formula is: Excellent individuals are selected based on mutation probability and fitness, and the initial capacity configuration optimization results are obtained after crossover and mutation processing on the selected excellent individuals.

[0104] Specifically, multiple capacity configuration results are generated based on the capacity configuration model. These results are used as the initial population of individuals in the genetic algorithm. The model is solved by using the genetic algorithm in Matlab software, and the fitness of each individual is calculated according to the objective function. The fitness reflects the performance of the individual under the optimization goal. According to the fitness of the individual, excellent individuals are selected to participate in the reproduction of the next generation, and the selected individuals are randomly paired to generate new offspring individuals through crossover operations. The crossover operation can be single-point crossover, multi-point crossover or uniform crossover, etc. The newly generated offspring individuals are mutated to introduce new genetic diversity. The mutation operation can be to randomly change the value of certain genes of the individual. The selection, crossover and mutation steps are repeated until the number of iterations is met or other stop conditions are reached. During the iteration process, the optimal individual and its fitness value are recorded and updated, and the optimal solution of the capacity configuration is finally obtained. The optimal solution is automatically searched by the genetic algorithm, which reduces the cost of manual intervention and trial and error.

[0105] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0106] 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 above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0107] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0108] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations, characterized in that: The method comprises the following steps: S1: Obtain wind power output data within a target time period, and cluster the wind power output data based on a K-means clustering algorithm to obtain a variety of wind power output scenarios; S2: Decomposing the wind power output data in the wind power output scenario based on the empirical mode decomposition method to obtain a multi-order intrinsic mode function, reconstructing the multi-order intrinsic mode function into a low-frequency component and a high-frequency component based on the wind power fluctuation quantity limitation, the low-frequency component is directly connected to the grid, the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system, and the hybrid supercapacitor energy storage system is defined as the target system; S3: mathematically modeling the target system, establishing a capacity configuration model for the target system based on a capacity management strategy, and constructing an optimization objective function of the capacity configuration model based on the investment cost, operation and maintenance cost, and opportunity compensation cost of the target system; S4: establishing a model constraint set of the capacity configuration model based on the fluctuation smoothing module boundary constraint, power balance constraint, charge and discharge power constraint and energy storage state constraint of the target system; S5: Solve the constructed capacity configuration model based on the genetic algorithm to obtain the initial optimization result of the target system, introduce a variable baseline and a fluctuation penalty coefficient, optimize the initial optimization result again, and obtain the final capacity configuration optimization result.

2. The method according to claim 1, characterized in that Obtaining multiple wind power output scenarios includes the following steps: Traverse all the data in the wind power output data, and calculate the similarity distance δ between each of the data and the initial cluster centroid based on the first formula, where the first formula is: Among them, x i and i The p-dimensional data and the p-dimensional initial clustering centroids are respectively, the wind power output data is divided into multiple clusters based on the similarity distance, the position of the new centroid in each cluster and the similarity distance between each data in the cluster and the new centroid are recalculated, and this operation is repeated until the distance between the position of the new centroid and the position of the centroid in the previous round is less than a first threshold; Calculate the average value of the wind power output data in each clustering result at this time, and take the sum of the differences between all the historical output data in the clustering result and the average value as the cumulative fluctuation of the clustering result, arrange all the cumulative fluctuations in order from small to large, select the cumulative fluctuation corresponding to the median, and take the day of the clustering result corresponding to the cumulative fluctuation as the corresponding wind power output scenario.

3. The method according to claim 1, characterized in that Reconstructing the plurality of intrinsic mode functions into low-frequency components and high-frequency components comprises the following steps: Based on the top-down superposition of the intrinsic mode functions of each order, high-frequency components of each order are generated. The first formula of the specific reconstruction method is: Among them, I MFp is the pth intrinsic mode function, r es is the residual obtained based on the empirical mode decomposition, f 2c(P+1) is the high frequency component of the p+1th order; Based on the bottom-up superposition of the intrinsic mode functions of each order to generate low-frequency components of each order, the second formula of the specific reconstruction method is: Among them, c 2f(p+1) is the low-frequency component of the p+1th order; Based on the wind power allocation strategy process, the low-frequency component is directly connected to the grid, and the high-frequency component is used as the power task of the hybrid supercapacitor energy storage system; The wind power fluctuation amount of the reconstructed low-frequency component and the high-frequency component is calculated, and the wind power fluctuation amount is the difference between the maximum power and the minimum power within a preset time interval. If the wind power fluctuation amount satisfies the constraint limit, the reconstruction of the low-frequency component and the high-frequency component is completed, and the constraint limit is: Among them, P c2f(p)，m is the power value in the p-th order m period, Δn is the preset time interval, ΔP limit It is the preset fluctuation limit.

4. The method according to claim 1, characterized in that: Mathematical modeling of the target system includes the following steps: The target system is modeled based on an equivalent circuit model, the self-discharge characteristics of the equivalent circuit model are ignored, and the parameters of the equivalent circuit model are calculated based on a third formula, where the third formula is: Among them, R SC and C SC are the equivalent series resistance and equivalent series capacitance, U t-sc is the terminal voltage of the supercapacitor group, C uc and R uc is the capacitance and resistance of the supercapacitor monomer, U t-uc is the terminal voltage of the supercapacitor monomer, M is the number of parallel modules, N is the number of series modules, and the target system is configured based on the parameters of the equivalent circuit model.

5. The method according to claim 4, characterized in that Building a capacity configuration model includes the following steps: The target system is optimized based on the method of the minimum working voltage of the supercapacitor, and the remaining energy storage ratio ω of the supercapacitor of the optimized target system is calculated based on the fourth formula, and the fourth formula is: Among them, U M is the starting working voltage of the supercapacitor, U min is the minimum operating voltage of the supercapacitor, W S is the residual energy stored in the supercapacitor that is difficult to reuse, W M is the total energy storage of the supercapacitor; The energy W1 provided by the supercapacitor after power failure is calculated based on the fifth formula, and the fifth formula is: Wherein, P0 is the power provided by the supercapacitor after power failure, T W is the holding time, η is the conversion efficiency of the converter; The capacity W2 provided by the supercapacitor is calculated based on the sixth formula, and the sixth formula is: W2 = 0.5C' SC (U M 2 -U min 2 ) where C′ SC The capacity of the super capacitor itself is calculated based on the seventh formula: SC , the seventh formula is: U0 is the output voltage of the super capacitor after power failure, and I0 is the current of the super capacitor after power failure.

6. The method according to claim 5, characterized in that Constructing the optimization objective function includes the following steps: An optimization objective function is constructed based on the hybrid supercapacitor energy storage cost and the opportunity compensation cost of wind power fluctuation, and the optimization objective function is: Among them, C is the annual comprehensive cost, C HSC is the energy storage cost of the hybrid supercapacitor, The hybrid supercapacitor energy storage cost C is calculated based on the eighth formula: HSC , the eighth formula is: in, and The investment cost and operation and maintenance cost of the target system are respectively, and the wind power fluctuation opportunity compensation cost is calculated based on the ninth formula The ninth formula is: Among them, c comp is the opportunity compensation cost coefficient, P p-uncomp，n and P N-uncomp，n are respectively the positive undercompensation and negative undercompensation at time n, N s The total time.

7. The method according to claim 6, characterized in that The acquisition of the investment cost and operation and maintenance cost of the target system includes the following steps: The investment cost is calculated based on the tenth formula, which is: in, and are the power and capacity investment cost coefficients of the target system, respectively, and are the rated power and rated energy capacity of the target system respectively, r is the discount rate, Y is the system operation period, and the operation and maintenance cost is calculated based on the eleventh formula The eleventh formula is: Wherein, a is the ratio of the operation and maintenance cost of the target system to its investment cost.

8. The method according to claim 7, characterized in that Establishing the model constraint set of the capacity configuration model includes the following steps: The target system is used to smooth the fluctuating power Constraints The target system is used to smooth the capacity of fluctuations The constraints are: Based on the twelfth formula, the power task P of the target system at time n is calculated. HSC，n , the twelfth formula is: in, is the power required to charge the target system at time n, is the power that the target system needs to discharge at time n, and the charging and discharging power is constrained based on the thirteenth formula, which is:

9. The method according to claim 8, characterized in that Establishing the model constraint set of the capacity configuration model also includes the following steps: The energy storage state of the target system is constrained based on the fourteenth and fifteenth formulas, and the fourteenth formula is: Among them, S OCn is the energy storage state at time n, S OCn-1 is the energy storage state at time n-1, η HSC is the charge and discharge efficiency of the target system, the fifteenth formula is: S OCmin ≤S OCn ≤S OCmax , where S OCmin is the minimum value of the target system energy storage state, S OCmax is the maximum value of the target system energy storage state.

10. The method according to claim 1, characterized in that Obtaining the initial capacity configuration optimization result of the target system includes the following steps: Based on the capacity configuration model, multiple capacity configuration results are obtained, the capacity configuration results are used as individuals of the genetic algorithm, and the mutation probability P of the individuals is calculated based on the sixteenth formula m (X i ), the sixteenth formula is: Where i = 1, 2, ..., M, X i is the i-th individual, is the average mutation probability, e(X i ) is the fitness of the ith individual, and the fitness e(X i ), the seventeenth formula is: where e(X i ) is individual X i The fitness evaluation function is based on the eighteenth formula to obtain the average mutation probability The eighteenth formula is: Excellent individuals are selected based on the mutation probability and fitness, and after crossover and mutation processing are performed on the selected excellent individuals, an initial capacity configuration optimization result is obtained.

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