MEMD decomposition-based energy storage system configuration optimization method and system

Abstract

The invention provides an energy storage system configuration optimization method and system based on MEMD decomposition, and relates to the technical field of new energy output fluctuation suppression and energy storage optimization, and the energy storage system configuration optimization method comprises the steps: obtaining the active power outputted by various new energy units in a target area in a historical preset time period; generating a power matrix according to the active power generated in a historical preset time period; obtaining each feature vector corresponding to the power matrix, and generating a corresponding feature vector set; generating a typical feature vector set based on the feature vector set; decomposing the typical feature vector set by adopting a multivariate integrated empirical mode decomposition MEMD method to obtain a low-frequency feature component and a high-frequency feature component in the typical feature vector set; according to the low-frequency characteristic component and the high-frequency characteristic component, the configuration of the energy storage system is optimized, different energy output characteristics can be considered, the configuration precision of the energy storage system is improved, and the investment cost of the energy storage system is effectively reduced.

Description

Technical Field

[0001] This application generally relates to the fields of new energy output fluctuation suppression and energy storage optimization technology. More specifically, this application relates to a method, system, medium, and device for energy storage system configuration optimization based on MEMD decomposition. Background Technology

[0002] Among various types of new energy generating units, the output power varies significantly. Wind turbines are affected by random wind speeds and gusts; photovoltaic units are driven by irradiance and weather, exhibiting diurnal periodicity and high-frequency fluctuations under cloud cover; biomass units are relatively stable but constrained by fuel and maintenance; and hydropower units are constrained by hydrology and dispatching, possessing a certain degree of adjustability. The total output power resulting from the superposition of multiple sources will fluctuate across multiple time scales (minutes, hours, days, seasons). Directly using these for energy storage configurations can easily lead to problems such as insufficient representativeness of typical days, unclear energy storage characteristics, poor capacity economics, and mismatch with grid fluctuation standards.

[0003] Existing energy storage configuration methods are generally designed for single energy types and often use fixed fluctuation control standards, failing to consider the coordination characteristics of multiple energy sources and the standard requirements of different capacity levels. Furthermore, the number of clusters K in typical daily clustering is usually determined empirically, resulting in highly subjective results. Power decomposition methods such as empirical mode decomposition, ensemble empirical mode decomposition, variational mode decomposition, and adaptive noise-complete ensemble empirical mode decomposition have been widely applied to the decomposition of wind power output, but they suffer from limitations such as strict frequency division conditions, insufficient adaptability to time-varying characteristics, and reliance on manual experience for parameter settings. These limitations can easily lead to the mixing of low-frequency components with high-frequency intrinsic mode functions, increasing ineffective charging and discharging, affecting the configuration accuracy of the energy storage system, and increasing the investment cost of the energy storage system. To address these issues, an effective energy storage system configuration optimization method is needed to achieve a solution that can take into account the output characteristics of different energy sources and improve the configuration accuracy of the energy storage system. Summary of the Invention

[0004] To address at least one or more of the technical problems mentioned above, this application proposes a method, system, medium, and device for optimizing the configuration of an energy storage system based on MEMD decomposition in several aspects. The method, system, medium, and device for optimizing the configuration of an energy storage system based on MEMD decomposition provided in this application adopt the following technical solutions: In the first aspect, the energy storage system configuration optimization method based on MEMD decomposition provided in this application includes the following steps: Obtain the active power output of various new energy units within the target area within a historical preset time period; A power matrix is ​​generated based on the active power generated within a historical preset time period; Obtain each feature vector corresponding to the power matrix and generate a corresponding feature vector set, wherein the feature vector set includes at least the daily average power, standard deviation, peak-to-valley difference, peak occurrence time, intraday rise or fall steepness, and day-night ratio; Based on the aforementioned feature vector set, a typical feature vector set is generated; The typical feature vector set is decomposed using the Multivariate Empirical Mode Decomposition (MEMD) method to obtain the low-frequency feature components and high-frequency feature components in the typical feature vector set. The configuration of the energy storage system is optimized based on the low-frequency characteristic components and the high-frequency characteristic components.

[0005] In some examples, generating the power matrix for the preset time period based on the active power generated within a historical preset time period includes: The sum of active power output by various new energy units within the same time period within a preset time cycle is calculated to generate a power matrix.

[0006] In some examples, after obtaining the eigenvectors corresponding to the power matrix and generating the corresponding eigenvector set, the method further includes: Calculate the similarity between different feature vectors in the feature vector set to generate a similarity matrix; The similarity matrix is ​​reduced in dimensionality using a multidimensional scaling method to obtain a set of morphological feature vectors.

[0007] In some examples, generating a typical feature vector set based on the aforementioned feature vector set includes: The morphological feature vector is fused with the feature vector set to obtain a fused feature vector set; The K-means clustering algorithm is used to perform clustering operations on the fused feature vector set to generate a typical feature vector set, wherein the typical feature vector set includes class center feature vectors and class center power functions.

[0008] In some examples, the decomposition of the typical feature vector set using the multivariate empirical mode decomposition method includes: The typical eigenvectors are decomposed into multiple intrinsic mode functions at different time scales; Based on the center frequencies of the multiple intrinsic mode functions, the low-frequency and high-frequency feature components in the typical eigenvectors are determined respectively.

[0009] In some examples, optimizing the energy storage system configuration based on the low-frequency characteristic components and the high-frequency characteristic components includes: Based on the low-frequency characteristic components and the high-frequency characteristic components, the required energy storage system type, as well as the rated capacity and rated power of each type of energy storage system, are determined.

[0010] In some examples, after generating a power matrix based on the active power generated within a historical preset time period, the method further includes: Detect noise data in the power matrix and repair the noise data.

[0011] In the second aspect, the energy storage system configuration optimization system based on MEMD decomposition provided in this application includes: The acquisition module is configured to acquire the active power output of various new energy units within a historical preset time period in the target area; The generation module is configured to generate a power matrix based on the active power generated within a historical preset time period; The generation module is further configured to obtain each feature vector corresponding to the power matrix and generate a corresponding feature vector set, wherein the feature vector set includes at least daily average power, standard deviation, peak-to-valley difference, peak occurrence time, intraday rise or fall steepness, and day-night ratio; The generation module is also configured to generate a typical feature vector set based on the feature vector set; The decomposition module is configured to use the multivariate empirical mode decomposition (MEMD) method to decompose the typical feature vector set to obtain the low-frequency feature components and high-frequency feature components in the typical feature vector set.

[0012] In a third aspect, this application provides a computer-readable storage medium containing program instructions that, when executed by a processor, cause the method described in the first aspect to be implemented.

[0013] In a fourth aspect, this application provides an electronic device, comprising: Processor; and A memory that stores computer instructions that, when executed by the processor, cause the electronic device to perform the method described in the first aspect above.

[0014] Compared with the prior art, this application has the following beneficial effects: By employing the multivariate empirical mode decomposition method, the typical feature vector set is decomposed to obtain the low-frequency and high-frequency feature components in the typical feature vector set. Based on the low-frequency and high-frequency feature components, the configuration of the energy storage system is optimized, which can take into account the output characteristics of different energy sources, improve the configuration accuracy of the energy storage system, and effectively reduce the investment cost of the energy storage system. Attached Figure Description

[0015] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein: Figure 1 This paper illustrates an exemplary flowchart of the energy storage system configuration optimization method based on MEMD decomposition provided in an embodiment of this application. Figure 2 This illustration shows the clustering effect on the first day after performing clustering on the fused feature vector set using the K-means clustering algorithm in an embodiment of this application. Figure 3 This illustration shows the decomposition effect obtained by using the multivariate empirical mode decomposition method to decompose a typical feature vector set according to an embodiment of this application. Figure 4 The diagram shows the smoothing effect of the energy storage system configuration optimization method provided in this application embodiment; Figure 5 An exemplary structural block diagram of an electronic device according to some embodiments of this application is shown. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0019] As used in this specification and claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0020] The specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0021] Example 1 like Figure 1 As shown, the energy storage system configuration optimization method based on MEMD decomposition provided in this application includes: S101, obtain the active power output of various new energy units within the target area within a historical preset time period.

[0022] Specifically, the active power output of various new energy generating units in the target area is obtained from the power grid energy management system every 5 minutes for one year, including the active power P output of wind turbines every 5 minutes. wind (t) Active power P output by the photovoltaic unit within 5 minutes pv (t) Active power P output by the biomass generator unit within 5 minutes bio (t) and the active power P output by the hydropower unit within 5 minutes hydro (t), where t is a preset time period (e.g., 5 minutes), and the data length is one year (365 days, 60÷5×24=288 sampling points per day).

[0023] S102, Generate a power matrix based on the active power generated within a historical preset time period.

[0024] In some examples, this step specifically includes: The sum of active power output by various new energy units within the same time period within a preset time cycle is calculated to generate a power matrix.

[0025] Specifically, taking a historical preset time period as an example, the active power output of various new energy units is obtained every 5 minutes per day, forming a power matrix: ,in, , ... These represent the sum of active power output by various types of new energy generating units within the first five minutes of the first day of a year, the sum of active power output within the first five minutes of the second day of a year, and so on, within the first five minutes of the 365th day of a year. , ... These represent the sum of active power output in the second five-minute period of the first day of the year, the sum of active power output in the second five-minute period of the second day of the year, and so on, up to the sum of active power output in the second five-minute period of the 365th day of the year. , ... These represent the sum of active power output within the 288th five-minute period on the first day of a year, the sum of active power output within the 288th five-minute period on the second day of a year, and so on, up to the sum of active power output within the 288th five-minute period on the 365th day of a year.

[0026] In some examples, after generating a power matrix based on the active power generated within a historical preset time period, the method further includes: Detect noise data in the power matrix and repair the noise data.

[0027] Specifically, since there may be extreme data points in the actual operation data caused by measurement equipment failure, communication anomalies or sudden weather changes, this application uses the density-based DBSCAN algorithm to detect outliers.

[0028] Define the feature vector of the sampling point: DBSCAN uses two main parameters, the neighborhood radius ε and the minimum number of neighboring points MinPts, as its judgment formula: like If the point is not identified as noise or an outlier, then the point is considered noise or an outlier.

[0029] Noise repair methods: Linear interpolation was used to repair individual outliers within the day. For noise points located at the beginning or end of the sequence and without preceding or following valid values, the most recent valid value is used as the replacement: .

[0030] Samples with fewer than the threshold number of valid points for the whole day (e.g., missing test rate > 20%) can be removed or marked as low confidence.

[0031] After the above steps, a complete, noise-free, matrixed power matrix is ​​obtained. This power matrix will serve as input for subsequent feature extraction and cluster analysis.

[0032] S103, obtain each feature vector corresponding to the power matrix and generate a corresponding feature vector set, wherein the feature vector set includes at least the daily average power, standard deviation, peak-to-valley difference, peak occurrence time, daily rise or fall steepness, and day-night ratio.

[0033] Specifically, for the annual data matrix For each day d∈{1, 2, 3, ..., 365}, the following statistical feature vector is extracted. : (1) Daily average power (2) Standard deviation (a measure of volatility) (3) Peak-valley difference (4) Peak occurrence time (5) Daily rise or fall steepness Where ∆t = 5 minutes.

[0034] (6) Day-night ratio (average daytime power / average nighttime power).

[0035] Where, N day With N night These represent the number of time points during the day and night, respectively.

[0036] The above indicators are combined to form a set of feature vectors.

[0037] In some examples, after step S103, the method further includes: Calculate the similarity between different feature vectors in the feature vector set to generate a similarity matrix; The similarity matrix is ​​reduced in dimensionality using a multidimensional scaling method to obtain a set of morphological feature vectors.

[0038] Specifically, in addition to statistical characteristics, the shape information of the power function is also crucial for clustering. This embodiment introduces a Dynamic Time Warping (DTW) distance metric to characterize the similarity of curves from different dates: Two-day power sequence and The DTW distance is defined as Where Π is the set of spatial paths, representing the allowed nonlinear time matching pairs.

[0039] To reduce computational dimensionality and clustering complexity, this embodiment employs a multidimensional scaling analysis method to reduce the dimensionality of the DTW distance matrix, obtaining a low-dimensional morphological feature vector. , where m is much smaller than the dimension of the original sequence (288).

[0040] By integrating statistical and morphological features, a typical daily clustering input vector is obtained: The fused feature matrix of all dates is represented as follows: The matrix F is used as input data for the adaptive K-means clustering algorithm.

[0041] S104. Based on this set of feature vectors, generate a set of typical feature vectors.

[0042] Specifically, this step includes: The morphological feature vector is fused with the feature vector set to obtain a fused feature vector set; The K-means clustering algorithm is used to perform clustering operations on the fused feature vector set to generate a typical feature vector set, wherein the typical feature vector set includes class center feature vectors and class center power functions.

[0043] Specifically, in order to identify representative typical daily curves, the present invention employs an adaptive K-means clustering method in step S4 to automatically determine the optimal number of clusters and output the typical power function for each category.

[0044] Set the search range for the number of clusters: Among them, Kmin is the minimum number of candidate clusters (set according to the seasonality of the whole year or operational experience, for example, 3), and Kmax is the maximum number of candidate clusters (set according to the power pattern difference of the four seasons of the year, for example, 12).

[0045] For a given value of K, running K-means clustering yields the class labels ci∈{1,…,K} and the class centers. The silhouette coefficient is used to evaluate the clustering effect.

[0046] Define sample i as belonging to class A, and its intra-class average distance as: Where d() is the function for calculating Euclidean distance or weighted distance.

[0047] Define the average inter-class distance between sample i and the nearest other class B: The silhouette coefficient of sample i is: The average silhouette coefficient of the entire clustering result is: Where N=365 is the total number of samples.

[0048] Iterate through K within the candidate range and select the cluster number that maximizes the average silhouette coefficient S(K): If multiple K values ​​S(K) are close and the difference is less than the preset threshold δs, then a smaller K is selected to reduce the number of typical days and enhance representativeness.

[0049] Under the optimal clustering number Kopt, K-means clustering is performed on the fused feature matrix F for the whole year to obtain the class label and class center curve for each date: Class center feature vector Class center power function Where C is the set of samples of the same category.

[0050] Class center power function This is the typical daily curve for this category, which will be used as input for multivariate empirical mode decomposition in the next step for high- and low-frequency fluctuation segmentation and energy storage system matching.

[0051] S105, the typical feature vector set is decomposed using the multivariate empirical mode decomposition (MEMD) method to obtain the low-frequency feature components and high-frequency feature components in the typical feature vector set.

[0052] In some examples, this step specifically includes: The typical eigenvectors are decomposed into multiple intrinsic mode functions at different time scales; Based on the center frequencies of the multiple intrinsic mode functions, the low-frequency and high-frequency feature components in the typical eigenvectors are determined respectively.

[0053] Specifically, the various center power functions obtained from the above steps As an input signal, it enters the Multivariate Ensemble Empirical Mode Decomposition (MEMD) step to decompose the intrinsic mode functions (IMFs) of the curve at different time scales.

[0054] Assume a typical daily curve It can be represented by n IMFs and a residual r(t): Wherein, IMFj(t) is the j-th intrinsic mode function (reflecting fluctuations within a specific frequency range), r(t) is the low-frequency trend residual part, and t is the sampling duration.

[0055] Step S211: Construct a multidimensional signal set (if there are multiple typical days or multiple energy components, they can be combined into a multi-channel input). Assume there are m input curves in total, such as power functions for different typical days or different energy components: Where, x l (t) is the input power function of the l-th channel (unit: MW), where t is the sampling duration and m is the number of channels.

[0056] Step S212: Introduce an equally spaced sequence of direction vectors and project the signal to extract the envelope. Take Q direction vectors on a unit sphere. : Satisfy the normalization condition: These vectors are uniformly distributed on a unit sphere and are used for signal projection in MEMD mode.

[0057] Step S213: In direction u q Project x(t) as follows: Find the projection signal p q The set of all local extrema of (t) Γq is used to generate the direction u through spline interpolation. q The lower envelope e q (t).

[0058] Step S214: Take the mean value of the envelope to obtain the local mean curve and iteratively peel it off until the remaining signal satisfies the IMF condition (the number of local extrema is equal to or differs from the number of zero crosses by no more than 1).

[0059] By averaging the envelope values ​​in all directions, we obtain a local mean curve: Repeated iterations are used to obtain all IMFs.

[0060] Step S215: Iterative stripping and IMF conditions Update signal: Repeat steps S213–S215 until the IMF criterion is met: Among them, Next is the number of local extrema, and Nzero is the number of zero crossovers. .

[0061] Then, using the remaining signal as a new input, repeat the above process to obtain subsequent IMFs until the remaining signal is a monotonic trend term.

[0062] Hilbert transform and center frequency calculation: To distinguish between high and low frequency components, this embodiment performs a Hilbert transform on each IMF to obtain its instantaneous frequency, letting... in, Let represent the Hilbert transform, where i is the imaginary unit.

[0063] Instantaneous phase: Instantaneous frequency: The center frequency of the IMF is defined as the time average of its instantaneous frequencies: In this case, T=24h, energy weighting ensures that frequencies with larger amplitudes contribute more significantly.

[0064] High and low frequency split power value settings: In a preferred embodiment, a center frequency threshold f corresponding to the split power is set. cut This threshold is determined based on grid connection fluctuation standards and the dynamic response capability of the energy storage system.

[0065] High-frequency set: Low-frequency set: High-frequency component power sequence: Low-frequency component power sequence: To distinguish between high and low frequency characteristic components, this embodiment performs a Hilbert transform on each IMF to obtain its instantaneous frequency, letting... in, Let represent the Hilbert transform, where i is the imaginary unit.

[0066] Instantaneous phase: Instantaneous frequency: The center frequency of the IMF is defined as the time average of its instantaneous frequencies: In this case, T=24h, energy weighting ensures that frequencies with larger amplitudes contribute more significantly.

[0067] In a preferred embodiment, a center frequency threshold f corresponding to the split power is set. cut This threshold is determined based on grid connection fluctuation standards and the dynamic response capability of the energy storage system.

[0068] High-frequency set: Low-frequency set: High-frequency component power sequence: Low-frequency component power sequence: .

[0069] S106 optimizes the configuration of the energy storage system based on low-frequency and high-frequency characteristic components.

[0070] In some examples, this step specifically includes: Based on the low-frequency and high-frequency characteristic components, determine the required type of energy storage system and the rated capacity and rated power of each type of energy storage system.

[0071] Specifically, in obtaining the high-frequency feature component P HF (t) and low-frequency characteristic component P LFFollowing (t), this embodiment allocates energy according to the characteristics of the energy storage system: the high-frequency component is allocated to the flywheel energy storage system (FESS), which is characterized by high power density, millisecond-level response time, and a cycle life of over one million cycles, making it suitable for smoothing fluctuations from seconds to minutes. The low-frequency component is allocated to the electrochemical energy storage system (BESS), which is characterized by large capacity and is suitable for handling hourly fluctuations.

[0072] In the application, PHF(t) can be further processed by moving average filtering to remove ultra-high frequency spikes and reduce flywheel power fluctuation pressure: Where M is the length of the filter window (number of sampling points).

[0073] In this application, the high-frequency power sequence P FESS (t) and low-frequency power sequence P BESS (t) Perform joint capacity optimization. The goal is to minimize the total lifecycle cost while meeting national or industry standards for grid-connected power fluctuations and constraints on energy storage system operation.

[0074] Hybrid Energy Storage Capacity Optimization Model and Solution Optimize objective function definition Total lifecycle cost includes: (1) Initial investment cost: Among them, C E,FESS C represents the unit capacity cost of flywheel energy storage. E,BESS For the unit power cost of electrochemical energy storage, C P,BESS C represents the cost per unit power of flywheel energy storage. P,BESS Cost per unit power of electrochemical energy storage.

[0075] (2) Operation and maintenance costs: Where Y is the operating period and r is the discount rate.

[0076] Operation and maintenance costs for flywheel energy storage: in, The unit capacity operation and maintenance cost of flywheel energy storage Operating and maintenance costs per unit power of flywheel energy storage.

[0077] Operating and maintenance costs for electrochemical energy storage: in, The unit capacity operation and maintenance cost of electrochemical energy storage Operating and maintenance costs per unit power of electrochemical energy storage.

[0078] (3) Equipment replacement cost (applicable only to electrochemical energy storage with limited lifespan): in, The cost per replacement of the electrochemical energy storage is denoted as Nrep, which represents the number of replacement cycles. The lifespan of the electrochemical energy storage is mainly affected by the depth of charge / discharge and the charge / discharge cycle, and is 6 years. This invention uses an advanced composite material flywheel with magnetic levitation bearings and vacuum chamber technology. Unlike bearings that rely on mechanical wear, the magnetic levitation bearings achieve contactless rotation, and the vacuum environment eliminates wind resistance losses, enabling millions of charge / discharge cycles throughout its lifespan. Therefore, it can be considered that the flywheel energy storage does not need to be replaced throughout its lifespan.

[0079] The final objective function for the entire lifecycle: The set of decision variables is: Constraints (1) Grid-connected power fluctuation constraints The grid-connected power Pgrid is defined as: The power change ∆Pgrid in each interval is required to satisfy... Where Pcap is the system installed capacity (MW), α is the standard value for the percentage fluctuation, and ∆t represents the time interval. α and ∆t are selected according to national technical standards.

[0080] The "Technical Specifications for Wind Farm Connection to Power System Part 1: Onshore Wind Power" (GB / T19963—2011) specifies the active power variation limits based on the installed capacity of wind farms (see Table 1 for details).

[0081] Table 1 Wind farm rated capacity / MW Maximum limit of active power change in 10 mins Maximum limit of active power change in 1 mins ＜30 10 3 30~150 Rated capacity / 3 Rated capacity / 10 ＞150 50 15 For photovoltaic power plants, according to the "Technical Regulations for Photovoltaic Power Plant Grid Connection" (Q / GDW1617—2015), the maximum change in active power within one minute shall not exceed 10% of the installed capacity.

[0082] (2) SOC constraint SOC range of flywheel energy storage: State of charge (SOC) range for electrochemical energy storage: SOC dynamic calculation formula: Among them, P ch(t) The charging power of the energy storage system, P dis(t) E represents the discharge power of the energy storage system. rated η is the rated capacity of the energy storage system. cv For the conversion efficiency of the energy storage converter, η ch η dis These represent the charging efficiency and discharging efficiency of the energy storage system, respectively. ΔT is the charging and discharging time interval of the energy storage system, and φ is the total number of sampling times.

[0083] (3) Power limit constraints (4) Capacity calculation After obtaining the time-based charging and discharging power of the energy storage system, the cumulative remaining capacity at each sampling point can be calculated.

[0084] The relationship between the amount of electricity accumulated by the flywheel energy storage system at time t and its charging and discharging power is as follows: in, The amount of electricity accumulated by the flywheel energy storage system at time t; Δt represents the initial capacity of the flywheel energy storage system; Δt represents the time interval between sampling points of the energy storage system.

[0085] The relationship between the amount of electricity accumulated by the electrochemical energy storage system at time t and the charge / discharge power is as follows: in, Let t be the amount of electricity accumulated by the electrochemical energy storage system at time t. Δt represents the initial capacity of the electrochemical energy storage system, and Δt represents the time interval between sampling points of the energy storage system.

[0086] Considering the range of changes in the state of charge of the energy storage system, the rated capacity of the flywheel energy storage system can be calculated as follows: The rated capacity of the electrochemical energy storage system is: This application preferably uses mixed-integer linear programming (MILP) to solve the problem. Specifically, based on the objective function and constraints, an iterative solution is obtained using a commercial optimizer (CPLEX, GUROBI). The final outputs are the rated capacity and rated power of the flywheel energy storage system and the electrochemical energy storage system, and ensure that the system meets the grid-connected power fluctuation threshold and energy storage operation limits.

[0087] On the other hand, embodiments of this application also provide an electronic device, see [link to relevant documentation]. Figure 5 , Figure 5 This is an exemplary structural block diagram of an electronic device according to an embodiment of this application, such as... Figure 5 As shown, the electronic device includes a processor and a memory, the memory storing computer instructions, and the processor executing the computer instructions to perform the method provided in this application.

[0088] Specifically, processor 601 may include a central processing unit (CPU) or a graphics processing unit (GPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application. Memory 602 may include memory for data or instructions. For example, memory 602 may be at least one of the following: a hard disk drive (HDD), read-only memory (ROM), random access memory (RAM), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, universal serial bus (USB) drive, or other physical / tangible memory storage device. Alternatively, memory 602 may include removable or non-removable (or fixed) media. Furthermore, memory 602 may be internal or external to the integrated gateway disaster recovery device. Memory 602 may be non-volatile solid-state memory. In other words, typically memory 602 includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with executable instructions, wherein the stored executable instructions, when executed by processor 601 (e.g., by one or more processors), can implement the methods in the embodiments of this application.

[0089] In one example Figure 5The illustrated electronic device may also include a communication interface 603 and a bus 610. The processor 601, memory 602, and communication interface 603 are connected via bus 610 and communicate with each other. Communication interface 603 is primarily used to enable communication between modules, devices, units, and / or equipment within the electronic device. Bus 610, including hardware, software, or both, couples components of the online data flow metering device together. For example, the bus may include at least one of the following: Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) Interconnect, Industry Standard Architecture (ISA) bus, Infinite Bandwidth Interconnect, Low Pin Count (LPC) bus, memory bus, Microchannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus, or other suitable buses. Bus 610 may include one or more buses. Although specific buses are described or illustrated in the embodiments of this application, any suitable bus or interconnection method may be considered in the embodiments of this application.

[0090] In another aspect, embodiments of this application also provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the aforementioned method. The computer-readable storage medium may be, for example, a classic computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), disk storage media, optical storage media, flash memory, or other electrical, optical, or other physical / tangible memory storage devices.

[0091] In another aspect, embodiments of this application also provide a computer program product, which includes computer program instructions that, when executed by a processor, implement the method provided in embodiments of this application. This computer program product may be, for example, a software installation package, a plug-in compatible with a related software system, etc.

[0092] The flowcharts and / or block diagrams of the methods and systems of embodiments of this application have been described above by way of example, and related aspects have been described. It should be understood that each block or combination thereof in the flowcharts and / or block diagrams can be implemented by computer program instructions, by dedicated hardware performing a specified function or action, or by a combination of dedicated hardware and computer instructions. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in memory or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0093] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] While this application has shown and described numerous embodiments, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will arise for those skilled in the art without departing from the spirit and intent of this application. It should be understood that various alternatives to the embodiments of this application described herein may be employed in the practice of this application. The appended claims are intended to define the scope of protection of this application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for optimizing the configuration of an energy storage system based on MEMD decomposition, comprising: Obtain the active power output of various new energy units within the target area within a historical preset time period; A power matrix is ​​generated based on the active power generated within a historical preset time period; Obtain each feature vector corresponding to the power matrix and generate a corresponding feature vector set, wherein the feature vector set includes at least the daily average power, standard deviation, peak-to-valley difference, peak occurrence time, intraday rise or fall steepness, and day-night ratio; Based on the aforementioned feature vector set, a typical feature vector set is generated; The typical feature vector set is decomposed using the multivariate empirical mode decomposition (MEMD) method to obtain the low-frequency feature components and high-frequency feature components in the typical feature vector set. The configuration of the energy storage system is optimized based on the low-frequency characteristic components and the high-frequency characteristic components.

2. The energy storage system configuration optimization method according to claim 1, characterized in that, Generating a power matrix for the preset time period based on the active power generated within a historical preset time period includes: The sum of active power output by various new energy units within the same time period within a preset time cycle is calculated to generate a power matrix.

3. The energy storage system configuration optimization method according to claim 1, characterized in that, After obtaining the eigenvectors corresponding to the power matrix and generating the corresponding eigenvector set, the method further includes: Calculate the similarity between different feature vectors in the feature vector set to generate a similarity matrix; The similarity matrix is ​​reduced in dimensionality using a multidimensional scaling method to obtain a set of morphological feature vectors.

4. The energy storage system configuration optimization method according to claim 3, characterized in that, Based on the aforementioned feature vector set, generating a typical feature vector set includes: The morphological feature vector is fused with the feature vector set to obtain a fused feature vector set; The K-means clustering algorithm is used to perform clustering operations on the fused feature vector set to generate a typical feature vector set, wherein the typical feature vector set includes class center feature vectors and class center power functions.

5. The energy storage system configuration optimization method according to claim 1, characterized in that, The decomposition of the typical feature vector set using the multivariate empirical mode decomposition method includes: The typical eigenvectors are decomposed into multiple intrinsic mode functions at different time scales; Based on the center frequencies of the multiple intrinsic mode functions, the low-frequency and high-frequency feature components in the typical eigenvectors are determined respectively.

6. The energy storage system configuration optimization method according to claim 1, characterized in that, Optimizing the energy storage system configuration based on the low-frequency characteristic components and the high-frequency characteristic components includes: Based on the low-frequency characteristic components and the high-frequency characteristic components, the required energy storage system type, as well as the rated capacity and rated power of each type of energy storage system, are determined.

7. The energy storage system configuration optimization method according to claim 1, characterized in that, After generating a power matrix based on the active power generated within a historical preset time period, the method further includes: Detect noise data in the power matrix and repair the noise data.

8. A configuration optimization system for an energy storage system based on MEMD decomposition, comprising: The acquisition module is configured to acquire the active power output of various new energy units within a historical preset time period in the target area; The generation module is configured to generate a power matrix based on the active power generated within a historical preset time period; The generation module is further configured to obtain each feature vector corresponding to the power matrix and generate a corresponding feature vector set, wherein the feature vector set includes at least daily average power, standard deviation, peak-to-valley difference, peak occurrence time, intraday rise or fall steepness, and day-night ratio; The generation module is also configured to generate a typical feature vector set based on the feature vector set; The decomposition module is configured to use the multivariate empirical mode decomposition (MEMD) method to decompose the typical feature vector set to obtain the low-frequency feature components and high-frequency feature components in the typical feature vector set.

9. A computer-readable storage medium, characterized in that, It includes program instructions that, when executed by a processor, cause the method according to any one of claims 1-7 to be implemented.

10. An electronic device, characterized in that, include: processor; as well as A memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method according to any one of claims 1-7.

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