A hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations

Abstract

The present invention discloses a hybrid energy storage capacity optimization configuration method for smoothing wind power fluctuations, which belongs to the field of wind power generation technology, including: clustering wind power output data based on the K-means clustering algorithm to obtain a variety of wind power output scenarios; decomposing wind power output data based on the empirical mode decomposition method to obtain multi-order intrinsic mode functions, and reconstructing them 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, and the hybrid supercapacitor energy storage system is defined as the target system; establishing a capacity configuration model for the target system, and constructing the optimization objective function and model constraint set of the capacity configuration model based on the cost input and constraint conditions of the target system; solving the capacity configuration model based on the genetic algorithm to obtain the initial optimization result, introducing a variable baseline and a fluctuation penalty coefficient, and performing further optimization. The present invention improves the efficiency of smoothing wind power fluctuations.

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 garnered widespread attention in recent years. They combine the characteristics of both secondary batteries and electrostatic capacitors and are considered a new chemical power source between traditional capacitors and secondary batteries. Modern power systems place higher demands on energy storage devices in terms of capacity, power, response speed, and cost, and traditional energy storage technologies are unable to simultaneously meet these requirements.

[0003] Compared to secondary batteries, supercapacitors offer high power density, rapid charging, long cycle life, high energy efficiency, high safety, and a clean, environmentally friendly design. They are widely used in new energy vehicles, rail transit, solar and wind power generation and storage, industrial machinery and equipment, energy-saving elevators, military aerospace, and shipbuilding.

[0004] Existing energy storage capacity configuration optimization methods for smoothing wind power fluctuations, such as the Chinese patent application with publication number CN104600728A, propose a hybrid energy storage capacity configuration optimization method for smoothing wind power fluctuations, including: formulating a coordinated control strategy, allocating reference power by 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 based on 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 based on the established economic model and the constraints of the model using a genetic algorithm with chaotic perturbation and quantum computing.

[0005] However, the chaotic genetic algorithm in the above-mentioned prior art can improve the optimization calculation efficiency, but it may have the problem of premature 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. It establishes a full life cycle cost model for a hybrid energy storage system consisting of batteries and supercapacitors. This method minimizes costs while smoothing wind power fluctuations, achieving capacity optimization for the hybrid energy storage system consisting of batteries and supercapacitors. First, the variational mode decomposition (VMD) method is used to decompose the original wind power. While meeting power fluctuation requirements, the grid-connected power and the compensation power of the hybrid energy storage system are obtained. A full life cycle cost model for the hybrid energy storage system is then constructed, taking into account constraints such as the state of charge and charge / discharge power of the energy storage, achieving capacity optimization for the hybrid energy storage system with the goal of achieving optimal economic efficiency.

[0007] However, the above-mentioned existing technologies may not fully consider the impact of the losses 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 suppression ability and system economic benefits. Summary of the Invention

[0009] 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 object of the invention, 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 multiple wind power output scenarios;

[0012] S2: Decomposing the wind power output data in the wind power output scenario based on an empirical mode decomposition method to obtain a multi-order intrinsic mode function. Based on the wind power fluctuation quantity limit, reconstructing the multi-order intrinsic mode function into a low-frequency component and a high-frequency component. 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 hybrid supercapacitor energy storage system is defined as a 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 for 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 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, where the first formula is: Among them, x i and y i The p-dimensional data and the p-dimensional initial clustering centroid are respectively divided into multiple clusters based on the similarity distance, and 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] Furthermore, 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, low-frequency components of each order are generated. 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 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;

[0023] Calculate the wind power fluctuation amount of the reconstructed low-frequency component and the high-frequency component, where 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 a constraint, the reconstruction of the low-frequency component and the high-frequency component is completed. The constraint 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 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, self-discharge characteristics of the equivalent circuit model are ignored, and 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 cell, 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 minimum operating 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 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, which 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, 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.

[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 wind power fluctuation opportunity compensation cost. 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 cost of wind power fluctuation opportunity compensation 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, 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 the model constraint set of the capacity configuration model includes the following steps:

[0035] The target system is used to smooth out fluctuating power Constraints The target system is used to smooth out the capacity 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 required to charge the target system at time n, is the power that the target system needs to discharge at time n, and the charge and discharge 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 in 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 i-th individual, and the fitness e(X i), the seventeenth formula is: Where E(X i ) is individual X i The fitness evaluation function is used to obtain the average mutation probability based on the eighteenth formula. 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 the use of hybrid supercapacitors to smooth out the fluctuations caused by large-scale wind power grid connection, and formulates a wind power fluctuation smoothing strategy 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 empirical mode decomposition is used to decompose the original wind power signal into direct grid connection components and energy storage system work tasks; then, based on the electrochemical energy storage charge and discharge power constraints and storage state constraints, a hybrid supercapacitor energy management strategy is formulated; based on this strategy, the 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 further optimized by changing the baseline and introducing a fluctuation penalty coefficient. By comparing with 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 This is a flowchart 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 This is the direct grid-connected component power curve for a typical day 7 of the present invention when the wind power fluctuation power limit is 50;

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

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

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

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

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

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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 herein may be used 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, 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 without departing from the scope of this application.

[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 fluctuations in wind power energy, mostly uses chemical batteries as the main method, and the application scenarios are mostly limited to microgrids and distributed energy systems. Few consider 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 the 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 the installed capacity of wind power 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 Figure 1, a simulation diagram of the K-means clustering algorithm clustering 365-day 5-minute wind power data is shown. The number of days and probability corresponding to each scenario are shown in Table 1. When the traditional K-means algorithm has extreme wind power output data in the clustering scenario, the cluster center will be distorted to a certain extent. The present invention uses the median of the cumulative fluctuation as an indicator to select typical days for each scenario. That is, the cumulative fluctuation of all days in each scenario is calculated and arranged in order from large to small. The days corresponding to the median are taken as the typical days of this 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 limit, 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, wind power output data at multiple time points within a day is processed based on EDM empirical mode decomposition to obtain multi-order intrinsic mode functions (IMFs). Each IMF represents a different frequency component. The wind power grid-connected fluctuation limit is used as a dividing line to reconstruct the multi-order intrinsic mode functions into low-frequency components and high-frequency components. The low-frequency component usually contains the main trend and slower fluctuations of wind power output, while the high-frequency component contains rapid changes and instantaneous fluctuations. Since the low-frequency component has smaller fluctuations and 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. 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 smoother, while the energy storage smoothing component becomes rougher and larger in amplitude. 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, this is the power curve of the direct grid-connected component when the wind power fluctuation limit is 50 on a typical day. Figure 4 The following is the power curve of the energy storage smoothing component when the wind power fluctuation limit is 50 on typical day 7. The power curves of all 8 typical days with low fluctuation and direct grid connection are as follows: Figure 5 As shown, the power curve for high fluctuations that require 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 for 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 actual 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 smoothing 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 total cost of ownership of the energy storage system, ensuring that the energy storage system can effectively smooth wind power fluctuations, improve the operating efficiency of the wind farm and the stability of the power grid.

[0063] S4: Establish a model constraint set for the capacity configuration model 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 fluctuations in 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 means ensuring 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 power at which the supercapacitor energy storage system can perform charging and discharging operations, ensuring the stable operation of the system while preventing equipment overload. 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 out wind power fluctuations. The initial optimization results are adjusted and improved using the variable baseline and fluctuation penalty coefficient to obtain the final capacity configuration optimization results.

[0068] like Figure 7 As shown in the figure, 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 smoothed fluctuation and the EMD low-frequency component fluctuation, 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 shown in the figure below. Figure 8 The figure below shows the high-frequency power fluctuation curve for a typical day 7 hybrid supercapacitor after smoothing, with a variable baseline and an increased fluctuation penalty factor. In this variable baseline mode, the optimal baseline value and optimal capacity configuration are determined, and the resulting grid-connected power fluctuations are identical to the low-frequency component fluctuations of the EMD, indicating complete controllability. This avoids the superposition of high-frequency smoothing fluctuations below the zero baseline with the low-frequency component fluctuations of the EMD, resulting in greater fluctuations and the risk of uncontrollable fluctuations.

[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 operating 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 the use of hybrid supercapacitors to smooth out the fluctuations caused by large-scale wind power grid connection, and formulates a wind power fluctuation smoothing strategy 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 empirical mode decomposition is used to decompose the original wind power signal into direct grid connection components and energy storage system work tasks; then, based on the electrochemical energy storage charge and discharge power constraints and storage state constraints, a hybrid supercapacitor energy management strategy is formulated; based on this strategy, the 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 further optimized by changing the baseline and introducing a fluctuation penalty coefficient. By comparing with 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 y i The p-dimensional data and the p-dimensional initial clustering centroids are respectively used. 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 new centroid position 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 amount 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, the low-frequency components of each order are generated. 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 and high-frequency components. The wind power fluctuation is the difference between the maximum power and the minimum power within the preset time interval. If the wind power fluctuation satisfies the constraints, the reconstruction of the low-frequency and high-frequency components is completed. The constraints are: 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 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. The fluctuations of these components are small 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 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 meets the preset fluctuation limit, then the reconstruction of the low-frequency and high-frequency components is considered successful, which means that the energy storage system can effectively smooth out 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 cell, 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 supercapacitor's ability 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 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 for 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 minimum operating voltage of supercapacitors, and the remaining energy storage ratio ω of the supercapacitors 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, W 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. The fifth formula 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: 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 minimum operating voltage of the supercapacitor 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 there is sufficient energy 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 wind power fluctuation opportunity compensation cost. The optimization objective function is: Among them, C is the annual comprehensive cost, C HSC is the energy storage cost of hybrid supercapacitor, The cost of wind power fluctuation opportunity compensation 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. By minimizing the annual comprehensive cost in the objective function, the most economically favorable energy storage system configuration is achieved. 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, other resources may need to be called upon 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] Obtaining 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, and Y is the system operation period. The operation and maintenance cost is calculated based on the eleventh formula The eleventh formula is: Where a is the ratio of the target system's operating and maintenance costs to its investment costs.

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

[0097] The target system is used to smooth out 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 required to charge the target system at time n, is the power that the target system needs to discharge at time n. The charging and discharging power is constrained based on the thirteenth formula, which is:

[0098] Specifically, the constraints of the target system include the configuration boundary constraint of the supercapacitor energy storage fluctuation smoothing module, power balance constraint, charge and discharge power constraint, and energy storage state constraint. The configuration boundary constraint of the fluctuation smoothing module refers to the capacity constraint of the target system used to smooth fluctuations, and the power balance constraint refers to the power constraint of the target system used to smooth 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 the model constraint set for the capacity configuration model also includes the following steps:

[0100] The energy storage state of the target system is constrained based on the fourteenth and fifteenth formulas. 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 charge and discharge states helps extend the service life of the supercapacitor, reduce replacement frequency, and lower maintenance costs.

[0102] Obtaining the initial capacity configuration optimization results for 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 in 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 i-th individual, and the fitness e(X i ), the seventeenth formula is: Where E(X i ) is individual X i The fitness evaluation function is used to obtain the average mutation probability based on the eighteenth formula. The eighteenth formula is: Excellent individuals are selected based on mutation probability and fitness, and after crossover and mutation processing, the initial capacity configuration optimization results are obtained.

[0104] Specifically, multiple capacity configuration results are generated based on the capacity configuration model. These results serve as the initial population of individuals in the genetic algorithm. The model is solved using a genetic algorithm in Matlab software, and the fitness of each individual is calculated based on the objective function. Fitness reflects the individual's performance under the optimization goal. Based on the individual's fitness, excellent individuals are selected to participate in the next generation of reproduction. The selected individuals are randomly paired, and new offspring individuals are generated through a crossover operation. The crossover operation can be single-point crossover, multi-point crossover, or uniform crossover. The newly generated offspring individuals are mutated to introduce new genetic diversity. The mutation operation can 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 stopping conditions are met. During the iteration process, the best individual and its fitness value are recorded and updated, and the optimal solution for capacity configuration is finally obtained. The genetic algorithm automatically searches for the optimal solution, reducing the cost of manual intervention and trial and error.

[0105] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, 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 will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many 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 merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended 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 scope of protection 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 multiple wind power output scenarios; S2: Decomposing the wind power output data in the wind power output scenario based on an empirical mode decomposition method to obtain a multi-order intrinsic mode function. Based on the wind power fluctuation quantity limit, reconstructing the multi-order intrinsic mode function into a low-frequency component and a high-frequency component. 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 hybrid supercapacitor energy storage system is defined as a target system. Reconstructing the multi-order intrinsic mode function into low-frequency components and high-frequency components includes 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, low-frequency components of each order are generated. 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 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; Calculate the wind power fluctuation amount of the reconstructed low-frequency component and the high-frequency component, where 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 a constraint, the reconstruction of the low-frequency component and the high-frequency component is completed. The constraint 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 is the preset fluctuation limit; 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 for 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 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, where the first formula is: Among them, x i and y i The p-dimensional data and the p-dimensional initial clustering centroid are respectively divided into multiple clusters based on the similarity distance, and 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 wind power 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 Mathematically modeling the target system includes the following steps: The target system is modeled based on an equivalent circuit model, self-discharge characteristics of the equivalent circuit model are ignored, and 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 cell, 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.

4. The method according to claim 3, characterized in that Building a capacity configuration model includes the following steps: The target system is optimized based on the method of minimum operating 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 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, which 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, 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.

5. The method according to claim 4, 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 wind power fluctuation opportunity compensation cost. 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 cost of wind power fluctuation opportunity compensation 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.

6. The method according to claim 5, characterized in that Obtaining 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, 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.

7. The method according to claim 6, characterized in that Establishing the model constraint set of the capacity configuration model includes the following steps: The target system is used to smooth out fluctuating power The constraints are: The target system is used to smooth out the capacity 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 required to charge the target system at time n, is the power that the target system needs to discharge at time n, and the charge and discharge power is constrained based on the thirteenth formula, which is:

8. The method according to claim 7, characterized in that Establishing the model constraint set of the capacity configuration model further 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.

9. 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 in 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 i-th 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 is performed on the selected excellent individuals, an initial capacity configuration optimization result is obtained.

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