Lithium battery dynamic heat generation power calculation method for stabilizing new energy power fluctuation

Abstract

The invention discloses a lithium battery dynamic heat generation power calculation method for stabilizing new energy power fluctuation, and belongs to the technical field of power system energy storage. According to the method, clustering analysis is carried out on new energy historical output data based on an improved K-means algorithm of a contour coefficient method, and a typical output scene is extracted; taking the condition that the fluctuation rate of the new energy grid-connected power meets the fluctuation rate constraint as an optimization target, performing decomposition and reconstruction on the typical output scene by adopting an adaptive wavelet packet decomposition algorithm, and determining an expected grid-connected power instruction; based on the expected grid-connected power instruction, rated power configuration and rated capacity configuration are considered, and an energy storage configuration model is constructed and solved; and solving a charge state calculation formula at each moment in the operation cycle, respectively constructing a function relation expression of dynamic change of the direct current resistance and the working current with the charge state, and further calculating the dynamic heat generation power of the lithium battery through a Bernardi heat generation rate formula. And characterizing a characteristic mapping relation among the dynamic heat source, the charge state and the time through a lithium battery dynamic heat generation formula.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage in power systems, and particularly relates to a method for calculating the dynamic heat generation power of lithium batteries for suppressing the power fluctuations of new energy sources. Background Technique

[0002] With the large-scale and high-proportion grid connection of new energy sources, the randomness, volatility, and intermittency of their power generation pose severe challenges to the safe and stable operation of the power grid. As a key component of the new power system, energy storage technology has attracted much attention under this background. Among them, lithium iron phosphate energy storage (hereinafter referred to as "lithium battery energy storage") has become one of the fastest-developing energy storage technologies due to its high energy density and fast response ability.

[0003] Applying lithium battery energy storage to suppress the power fluctuations of new energy sources is its most typical application scenario. A reasonable energy storage configuration can effectively alleviate the volatility of new energy output and ensure the safe and stable operation of the power grid. However, a large amount of heat will be generated during the frequent charge and discharge process of lithium batteries. If not effectively managed, it will not only accelerate the degradation of battery performance and shorten the battery life, but also may cause safety accidents such as thermal runaway. Therefore, accurately characterizing the dynamic thermal behavior of lithium batteries has become the key to ensuring the safe operation of energy storage systems.

[0004] Most of the existing research focuses on the separate study of energy storage configuration optimization or thermal management strategies, lacking a comprehensive method that organically combines the two in actual application scenarios. Isolated research methods are difficult to comprehensively and accurately characterize the thermal behavior of lithium batteries under complex working conditions, restricting the overall safety and reliability of lithium battery energy storage systems. Summary of the Invention

[0005] The purpose of the present invention is to address the problem that the lack of an organic combination of energy storage configuration optimization and thermal management strategies in actual application scenarios restricts the overall safety and reliability of lithium battery energy storage systems. A method for calculating the dynamic heat generation power of lithium batteries for suppressing the power fluctuations of new energy sources is proposed. The present invention performs clustering analysis on the historical output data of new energy sources through the k-means algorithm to extract typical output scenarios. Secondly, the adaptive wavelet packet decomposition algorithm is used to decompose and reconstruct the typical scenarios to determine the high-frequency fluctuation components that need to be suppressed. Then, an energy storage configuration model is built and a dynamic heat generation power calculation model of energy storage lithium batteries considering electrochemistry-thermal coupling is established, and the dynamic heat generation power during the operation of lithium batteries is solved through numerical calculation. Compared with the existing methods, the present invention not only optimizes the energy storage configuration to enhance the stability of new energy access to the power grid, but also further quantifies the dynamic thermal behavior of lithium batteries in the scenario of suppressing the power fluctuations of new energy sources, significantly improving the safety and reliability of energy storage systems. The present invention provides technical support for the safe operation of lithium battery energy storage systems in the scenario of suppressing the power fluctuations of new energy sources.

[0006] In order to solve the above technical problems, the present invention provides a method for calculating the dynamic heat generation power of a lithium battery for smoothing the power fluctuation of new energy, comprising the following steps: S1. The improved K-means algorithm based on the silhouette coefficient method is used to cluster the historical output data of new energy sources and extract typical output scenarios; S2. Taking the new energy grid-connected power fluctuation rate satisfying the fluctuation rate constraint as the optimization goal, an adaptive wavelet packet decomposition algorithm is used to decompose and reconstruct the typical output scenario, and determine the expected grid-connected power command; S3. Based on the expected grid-connected power instruction, considering the rated power configuration and the rated capacity configuration, construct an energy storage configuration model and solve the energy storage configuration model to obtain an energy storage configuration result; S4, solving the initial state of charge based on the energy storage configuration result, and then combining the energy storage calculation formula of the energy storage system at any time to obtain the charge state calculation formula at each time in the operation cycle; S5. Construct the functional relationship between the dynamic change of DC resistance and working current with the state of charge respectively, and then combine it with the state of charge calculation formula at each moment in the operation cycle, calculate the dynamic heat generation power of the lithium battery through the Bernardi heat generation rate formula, and characterize the characteristic mapping relationship between the dynamic heat source and the state of charge and time through the dynamic heat generation formula of the lithium battery under energy storage to smooth fluctuations.

[0007] Preferably, the S1 comprises: S11, obtaining and statistically analyzing the historical output data of new energy sources within a set time interval, and verifying and supplementing abnormal values ​​and missing values ​​through a sliding average value; S12, historical output data of new energy sources after simplification through principal component analysis; S13, setting the number range of cluster centers and performing iterative solution through the silhouette coefficient method to obtain the optimal number of clusters; S14. Based on the optimal number of clusters, a typical scenario is obtained through a K-means algorithm and the new energy output power of the typical scenario is generated.

[0008] Preferably, S2 comprises: S21. Define the volatility of new energy and consider the volatility constraint, determine whether energy storage needs to be configured, and obtain the configuration result; S22. Based on the configuration result, the new energy grid-connected power and the expected grid-connected power command are obtained by using the optimization target that the new energy grid-connected power fluctuation rate satisfies the fluctuation rate constraint and the adaptive wavelet packet decomposition algorithm. The new energy grid-connected power fluctuation rate mentioned here is the new energy power fluctuation rate mentioned above. The specific value can be referred to in formula (9) and the specification "Technical Regulations for Wind Farm Access to Power System Part 1: Onshore Wind Power".

[0009] Preferably, the configuration result includes: if the new energy output power of the typical new energy scenario meets the grid connection volatility constraint, energy storage does not need to be configured and it can be directly connected to the grid for operation; if it does not meet the grid connection volatility constraint, energy storage is configured. The grid connection volatility constraint is that the new energy power volatility meets the specification "Technical Regulations for Wind Farms Connected to the Power System - Part 1: Onshore Wind Power", as shown in Equation (9).

[0010] Preferably, in S22, the spectrum analysis of the output power that needs to suppress power fluctuations is carried out by discrete Fourier transform to obtain the amplitude-frequency characteristic result of this output scenario. At a fixed scale, a wavelet packet function is defined to obtain the wavelet packet decomposition algorithm and the wavelet packet reconstruction algorithm and determine the optimal decomposition layer number. The new energy output power is decomposed into high-frequency components and low-frequency components, and at the same time, the low-frequency component is used as the expected grid-connected power. The difference between the expected grid-connected power and the original new energy output power is used as the expected grid-connected power command. This output scenario can be understood as a typical new energy output scenario that requires energy storage configuration. The amplitude-frequency characteristic result is convenient for understanding the high-frequency and low-frequency components of the new energy output and is used for subsequent wavelet packet decomposition. Equation (10) is the construction principle.

[0011] Preferably, in S3, solving the energy storage configuration model includes solving the rated power of the energy storage system and the rated capacity of the energy storage system; Solving the rated power of the energy storage system includes solving the expected power of the energy storage system based on the expected grid-connected power command, and at the same time considering the comprehensive cycle efficiency of the energy storage and the charge-discharge efficiency of the energy storage to obtain the primary power command of the energy storage system, and then considering to meet the charge balance constraint to obtain the rated power of the energy storage system; Solving the rated capacity of the energy storage system includes solving the energy fluctuation of the energy storage system compared with the initial state during the operation cycle based on the primary power command to obtain the calculation formula of the stored energy of the energy storage system at any time, and then obtaining the state of charge expression. At the same time, making it meet the upper and lower limits of charge and discharge of the energy storage system, calculating the ratio of the maximum value difference of the energy fluctuation of the energy storage system during the entire operation cycle to the difference of the upper and lower limits of its charge and discharge to obtain the rated capacity of the energy storage system.

[0012] Preferably, in S4, the initial charge state is specifically calculated through the following expression: ; In the formula, represents the initial charge state, is the rated capacity of the energy storage system, represents the upper limit of energy storage charging, represents the lower limit of energy storage discharging, is the stored energy of the energy storage system at any time.

[0013] Preferably, in S4, the specific expression of the DC resistance is: ; In the formula, is the polynomial coefficient; is the DC resistance, is the exponential function of the state of charge.

[0014] Preferably, in the step S4, the specific expression of the DC resistance is: ; In the formula, is the rated capacity of a single cell; when t = 1, ; is the sampling time interval of the new energy output data; is the data of parallel-connected cells; is the time corresponding to the sampling point; is the initial state of charge; Among them, when calculating the DC resistance, the series and parallel connection conditions of the energy storage cells are considered, and it is assumed that the states of charge of the cells in the energy storage system are consistent; it is assumed that the charge and discharge power of the lithium battery is constant during the sampling period.

[0015] Preferably, the calculation formula of the state of charge at each moment during the operation period is specifically expressed as follows: ; In the formula, is the rated capacity of the energy storage system; is the time point corresponding to the end of the sampling point; is the grid-connected power corresponding to the energy storage at time t.

[0016] Preferably, the dynamic heat generation formula of the lithium battery under the energy storage suppressing fluctuations is specifically expressed as follows: ; In the formula, X(·), Y(·), and Z(·) are abstract functions of the dynamic heat generation power, working current, and DC internal resistance respectively; F(·) is the abstract function of the dynamic heat generation power obtained after solving.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This solution considers the energy storage configuration and operation applied to suppressing the fluctuations of new energy, and fully calculates the dynamic heat generation power of the lithium battery. The reasonable configuration scale of the energy storage applied to this scenario and the dynamic heat generation power of the energy storage during the operation process can be obtained, providing an important basis for "new energy with energy storage", and at the same time serving as an important reference for guiding the adaptation of new energy to grid connection and the safe and stable operation of the battery. This method makes up for the deficiency in calculating the dynamic heat generation power of the lithium battery when the energy storage is applied to suppress the power fluctuations of new energy.

[0018] 2. This scheme fully considers the optimization of energy storage configuration and thermal management strategies and their combination, and innovatively expresses the dynamic heat source of lithium batteries as a function of the state of charge of the energy storage system and time in the scenario of smoothing the power fluctuation of new energy. This makes it feasible to quantitatively characterize the dynamic heat source of lithium batteries in the scenario of energy storage smoothing the power fluctuation of new energy.

[0019] 3. This solution solves the dynamic DC internal resistance and working current of the lithium battery, and then jointly solves the charge state at each moment in the operation cycle, which can more accurately predict the state parameters of the lithium battery and provide strong data support for the safe and stable operation of the energy storage system.

[0020] 4. This method is mainly used to smooth out fluctuations in renewable energy power and is suitable for the capacity configuration of renewable energy energy storage. On the basis of ensuring the safe grid connection of renewable energy power generation and reducing the problem of wind and solar power abandonment caused by power generation fluctuations, it can also be further used as a safety guidance plan for the planning, scheduling and operation of energy storage power stations on the power supply side to ensure a high proportion of renewable energy consumption.

[0021] 5. This solution quantitatively characterizes the dynamic heat source of lithium batteries in the scenario of energy storage to smooth out the power fluctuations of new energy sources because the specific analytical expression between the dynamic heat source and the state of charge and time is dynamically changing, which is difficult to accurately describe with a specific function expression. Therefore, we choose to use an abstract function to give the characteristic mapping relationship between the dynamic heat source and the state of charge and time, which is used to quantitatively characterize the dynamic heat source of lithium batteries in the scenario of energy storage to smooth out the power fluctuations of new energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the system flow of the present invention; Figure 2 It is the software interface for calculating the dynamic thermal power of the energy storage lithium battery of the embodiment of the present invention; Figure 3 Schematic diagram of the decomposition of the adaptive wavelet packet decomposition algorithm of the embodiment of the present invention Figure 4 The following is a schematic diagram of the solution results of typical scenarios in the implementation example of the present invention. Figure 1 ; Figure 5 The following is a schematic diagram of the solution results of typical scenarios in the implementation example of the present invention. Figure 2 ; Figure 6 A typical scene result diagram of an implementation example of the present invention; Figure 7 It is a schematic diagram of the calculation results of the output sequence of a typical scenario of an embodiment of the present invention; Figure 8 The results of the amplitude-frequency characteristic analysis in various typical scenarios of the implementation examples of the present invention are as follows: Figure 1 ; Figure 9 Amplitude-frequency characteristic analysis results under various typical scenarios of the embodiments of the present invention Figure 2 ; Figure 10 Amplitude-frequency characteristic analysis results under various typical scenarios of the embodiments of the present invention Figure 3 ; Figure 11 Amplitude-frequency characteristic analysis results under various typical scenarios of the embodiments of the present invention Figure 4 ; Figure 12 Comparison results of new energy output sequences before and after flattening of the embodiments of the present invention Figure 1 ; Figure 13 Comparison results of new energy output sequences before and after flattening of the embodiments of the present invention Figure 2 ; Figure 14 Comparison results of new energy output sequences before and after flattening of the embodiments of the present invention Figure 3 ; Figure 15 Schematic diagram of the output result window of the embodiments of the present invention; Figure 16 Power sequence curve graph of the energy storage system of the embodiments of the present invention; Figure 17 Energy change curve graph of the energy storage system of the embodiments of the present invention; Figure 18 Comparison graph of grid connection volatility before and after energy storage configuration of the embodiments of the present invention; Figure 19 Schematic diagram of measured data of lithium battery SOC - DC internal resistance of the embodiments of the present invention; Figure 20 Output window result information of Module 4 of the embodiments of the present invention; Figure 21 Optimal polynomial fitting relationship graph containing SOC - R of the embodiments of the present invention; Figure 22 Schematic diagram of dynamic heat generation power results of energy storage lithium batteries under typical scenarios of the embodiments of the present invention. Specific implementation manners

[0023] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, 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 implementation manners described herein are only the best embodiments of the present invention, which are only used to explain the present invention and do not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0024] Embodiment 1: AsFigure 1 - Figure 22 As shown, the calculation method of dynamic heat generation power of lithium batteries for smoothing the power fluctuation of new energy includes: Step 1: Clustering and Reduction of New Energy Output Scenarios The output power of renewable energy represented by wind power and photovoltaic power usually has high temporal resolution and huge data volume. If each specific output curve is considered, the computational complexity will increase significantly. At the same time, the output of renewable energy is affected by factors such as weather and season, showing obvious spatiotemporal characteristics. Therefore, through clustering reduction, a large amount of wind and solar power output data can be simplified into a few representative typical scenarios, which can reduce the complexity of the model and the calculation time, and improve the computing efficiency.

[0025] The K-means algorithm is a classic clustering algorithm, which is widely used due to its high execution efficiency. However, the K-means algorithm needs to obtain the number of cluster centers in advance, otherwise it may lead to an increase in the error of the clustering results. To this end, the present invention uses an improved K-means algorithm based on the silhouette coefficient method to cluster and reduce historical operation scenarios. Before clustering, the silhouette coefficient method is used to analyze the historical operation scenarios of new energy to determine the optimal number of cluster centers, and then the K-means algorithm is used to extract typical operation scenarios. The specific steps are as follows: Step 1: Input a set of historical output data of new energy sources, and clean and verify the data to avoid null values ​​or wrong values ​​(such as negative numbers); Step 2: Use principal component analysis (PCA) to reduce dimensionality. The output power of renewable energy has high dimensions and time series correlation. PCA projects high-dimensional data into low-dimensional space, while retaining the main information of the data as much as possible, simplifying the data and revealing the output pattern and characteristics, thereby improving computational efficiency.

[0026] Step 3: Use the silhouette coefficient method to determine the optimal number of clusters. Preliminarily set a reasonable range for the number of cluster centers, iteratively solve the data after dimensionality reduction, and determine the optimal number of clusters using the silhouette coefficient method. Assume that each data point in the data set is i , the following is the silhouette coefficient S(i) The calculation process is: (1) Calculate the average distance within the cluster a ( i ) —— Measuring Data Points i The consistency with the cluster it belongs to.

[0027] Where: C i Yes i The cluster in which it is located; is the number of points in the cluster;d ( i, j ) is the distance between the point i and the point j .

[0028] (2) Calculate the average distance of the nearest cluster b ( i ) - - Measure the distance between the data point i and its nearest neighboring cluster.

[0029] In the formula: C k is different from the cluster i where C i is located; is the number of points in the cluster C k .

[0030] (3) Calculate the silhouette coefficient S ( i ) - - Used to evaluate the clustering quality, and its value ranges from [-1, 1]. The larger the value, the better the clustering effect.

[0031] (4) Overall silhouette coefficient S - - For the entire dataset, the overall silhouette coefficient is the average of the silhouette coefficients of all data points. By calculating the k under different numbers of clusters S , the k value that maximizes the silhouette coefficient can be found, which is usually considered the optimal number of clusters.

[0032] Step4: Obtain typical scenarios based on the K - means algorithm. The basic steps are as follows.

[0033] (1) Data preprocessing, including standardization and outlier filtering.

[0034] (2) Select k (the optimal number of clusters determined in the previous step) cluster centers, denoted as , ... .

[0035] (3) Define the loss function J ( c, μ ) - - The sum of the squared errors of the distances of each sample from the center point of its belonging cluster.

[0036] In the formula: represents the i th sample; C i is the x i cluster to which it belongs; represents the center point corresponding to the cluster; M is the total number of samples.

[0037] (4) The loss function iteratively converges.

[0038] Let t = 0, 1, 2... be the iteration steps, and repeat the following process until J converges. For each sample x i , assign it to the nearest center.

[0039] In the formula: argmin refers to the parameter value that obtains the minimum value in the domain.

[0040] For each class center k , recalculate the center of this class.

[0041] (5) Generate typical scenario data. When the loss function no longer changes significantly, the algorithm converges, and k typical scenarios are obtained. Each typical scenario is represented by the clustering center point, and k new energy output powers of typical scenarios are generated.

[0042] Second step: Adaptive wavelet packet decomposition Compared with the traditional low-pass filtering algorithm, wavelet decomposition has good localization and multi-resolution characteristics in processing non-stationary mutation signals, and is more suitable for new energy power fluctuation analysis. Wavelet packet transform is a further development based on wavelet transform, which can provide higher resolution than wavelet transform. It overcomes the disadvantages of wavelet transform in having poor frequency resolution in the high-frequency band and poor time resolution in the low-frequency band. Through multi-level frequency band division, according to the characteristics of the signal to be analyzed, it adaptively improves the time-frequency resolution, which helps to obtain more detailed information of the signal. The specific steps are as follows: Step1: New energy target grid-connected power constraint New energy power fluctuation is a key indicator to describe the grid connection safety of new energy. To ensure the stability and safety of new energy grid connection, the new energy power generation must meet the grid connection fluctuation constraint standard when accessing the grid.

[0043] (1) Definition of new energy volatility: The active power change (volatility) of new energy includes the volatility in 1 minute and the volatility in 10 minutes, which can be solved according to the following formula: In the formula: and represent the volatility within 1 minute and 10 minutes respectively. The specific mathematical meaning is the deviation between the maximum value ( , ) and the minimum value ( , ) of the new energy power fluctuation.

[0044] (2) Volatility constraint The volatility constraint of new energy is popularized and applied according to the specification "Technical Regulations for Wind Farms Connected to the Power System - Part 1: Onshore Wind Power" (GB / T 19963.1 - 2021). The volatility constraint is closely related to the installed capacity of new energy power stations. The new energy volatility limit can be solved according to the following formula based on different installed capacities.

[0045] In the formula: P N is the installed capacity of the new energy power station, in MW.

[0046] (3) Determine whether energy storage needs to be configured According to the above volatility definition, analyze the original output of typical scenarios. If the original output meets the grid connection constraints, there is no need to configure energy storage and it can be directly connected to the grid for operation; if it does not meet the constraints, energy storage needs to be configured to suppress power fluctuations and enter the next step of processing.

[0047] Step2: Adaptive wavelet packet decomposition method Wavelet packet decomposition can decompose the output of new energy into high - frequency and low - frequency components in different frequency bands, so as to capture the characteristics of new energy power at different time scales. The relationship between the high - frequency and low - frequency components mainly reflects the local changes and overall trends of new energy power. The main energy of new energy power is concentrated in the low - frequency part, and the high - frequency part that needs to be suppressed accounts for a relatively small proportion. Through the grid connection optimization goal and wavelet packet decomposition, the new energy grid - connected power that meets the optimization goal requirements can be obtained, as well as the deviation part that needs to be suppressed by energy storage, and this deviation is used as the research benchmark for subsequent energy storage configuration.

[0048] (1) Spectrum analysis First of all, it is necessary to determine the sampling time of the data T s (the minimum sampling time in this invention should be less than 1 minute, with the unit of s), and the sampling frequency is f s = 1 / T s, the total number of sampling points is N = 86400 / T s . Use the discrete Fourier transform (DFT) to perform spectral analysis on the output data that needs to suppress power fluctuations, and obtain the amplitude-frequency characteristic results in this scenario. The formula of DFT is as follows: In the formula: X(k) represents the data after DFT transformation, k is the frequency index in the frequency domain, P(n) represents the power signal of new energy. is the complex form of Euler's formula, equivalent to . This formula realizes the conversion of the time-domain signal P (n) to the frequency-domain signal X(k) .

[0049] (2) Wavelet packet decomposition and reconstruction Use wavelet packet decomposition to decompose the new energy power signal into high-frequency and low-frequency signals. Wavelet packet decomposition is obtained by further decomposing the high-frequency part obtained by wavelet decomposition. Its decomposition result maps the original signal to 2 n (n represents the decomposition level here) wavelet packet subspaces, forming a complete binary tree in structure.

[0050] The mathematical definition of wavelet packet is as follows: In the formula: is the scaling function, used to generate the low-frequency (approximate) component of the signal, { h n} n∈k is the low-pass filter coefficient; is the wavelet function, used to generate the high-frequency (detail) component of the signal, { g n} n∈k is the high-pass filter coefficient, with g n = (-1) n h 1-n ; Z represents the set of integers, and the coefficients are usually finite terms in practical applications; through the scaling factor 2 t and the translation factor k , the wavelet function can analyze the signal at different time and frequency scales. For the convenience of representing the wavelet packet function, new function symbols are introduced, = , = , substituting into Equation (11) gives: At a fixed scale, it is determined by the orthogonal scaling function = and the wavelet packet function is defined as follows through 、 、 h n 、 g n : Based on the above wavelet packet function, the wavelet packet decomposition algorithm is shown as follows: where 、 are the decomposition coefficients of the low-frequency and high-frequency components at the nth layer respectively.

[0051] The wavelet packet reconstruction algorithm is defined as follows: (3)Calculation of the optimal number of layers for wavelet packet decomposition Perform n-layer wavelet packet decomposition on the new energy output power of typical scenarios, and reconstruct the power components of 2 n frequency bands at the nth layer to obtain the low-frequency component and the high-frequency component ( ), where the bandwidth of each signal frequency band is f o . Specifically, as shown in Figure 3 . As the core method in the time-frequency analysis field, this algorithm can perform multi-scale layer-by-layer decomposition on both high-frequency and low-frequency signals simultaneously. Its decomposition process strictly satisfies the orthogonality and completeness conditions, ensuring full coverage of signal features while eliminating information redundancy. Compared with the traditional wavelet transform, this algorithm has better time-frequency localization ability and is especially suitable for data analysis scenarios with strong randomness and non-stationarity characteristics such as new energy output, providing high-precision time-frequency domain representations for subsequent feature extraction.

[0052] Deepen the decomposition layer in a loop n , and use the proposed grid connection volatility constraint to judge the low-frequency component . When the amplitude fluctuation of first satisfies the grid connection fluctuation constraint, the optimal decomposition layer of the wavelet packet decomposition can be determined. At the same time, take as the expected grid connection power , and take the difference between and as the expected grid connection power command of the energy storage system 。

[0053] Step 3: Energy storage configuration model The energy storage configuration model needs to fully consider the operating characteristics of the energy storage system during the operation cycle, including constraints such as charge and discharge upper and lower limits, comprehensive cycle efficiency, and power balance. The steps to construct the energy storage configuration model are as follows: Step1: Rated power configuration (1)Desired power of the energy storage system Solve the desired grid-connected power command of the energy storage system according to the results of the previous step 。

[0054] In the formula: When taking a positive value, it indicates that the energy storage discharges; when taking a negative value, it indicates that the energy storage charges; is the output power of new energy.

[0055] (2)Energy storage cycle efficiency constraint During the charge and discharge process of the energy storage system, the comprehensive cycle efficiency of the energy storage should be considered 。 、 respectively represent the charge efficiency and discharge efficiency. Assuming that the charge and discharge efficiencies are equal, then: 。

[0056] (3)Primary power command of the energy storage system Considering the charge and discharge efficiency of the energy storage, obtain the primary power command of the energy storage system 。

[0057] (4)Power balance constraint To ensure the continuous and stable operation of the energy storage system, the power balance constraint should be satisfied, that is, the net charge and discharge amount is zero during the operation cycle. And since the comprehensive cycle efficiency of the energy storage system is always less than 100%, the charge amount of the energy storage system is always less than the discharge amount. Therefore, to meet the power balance constraint condition, it is necessary to intervene in the power command of the energy storage system and shift the primary power command downward as a whole. The constraint condition and the power downward shift amount The mathematical definition is: (5)Rated power of the energy storage system After determining the power downward shift amount, the grid-connected target power of the energy storage system is the difference between the primary power command of the energy storage system and the power downward shift amount, and the rated power of the energy storage system is the maximum value of the grid-connected target power of the energy storage system.

[0058] Step2: Rated Capacity Configuration To meet the demand for suppressing the power fluctuations of new energy, the energy storage system should be configured with sufficient capacity. The steps for configuring the rated capacity of the energy storage system are as follows: (1) Energy Fluctuation Solve the energy fluctuation of the energy storage system compared with the initial state during the operation cycle to obtain the stored energy of the energy storage system at any time.

[0059] (2)Constraints on the upper and lower limits of charge and discharge of the energy storage system To avoid overcharging and over-discharging of the energy storage system and ensure the safe operation of the energy storage system, the upper and lower limits of charge and discharge of the energy storage system should be defined. For the sake of easy expression, the state of charge (SOC - representing the percentage of the remaining power of the energy storage system in the rated capacity is used to describe the energy state of the energy storage system.

[0060] In the formula: represents the upper limit of energy storage charging, represents the lower limit of energy storage discharging.

[0061] (3)Rated Capacity of the Energy Storage System Calculate the ratio of the difference between the maximum and minimum values of the energy fluctuation of the energy storage system during the entire operation cycle to the difference between the upper and lower limits of its charge and discharge, which is the rated capacity configured for the energy storage system.

[0062] Step3: Initial State of Charge The initial state of charge is represented by and the initial state of charge should meet the upper limit constraint during the charge and discharge overcharge of the energy storage system (to avoid exceeding the limit), so it can be solved by the following formula.

[0063] Fourth Step: Calculation Model of Dynamic Heat Generation Power of Energy Storage Lithium Battery Considering Electrochemical-Thermal Coupling The lithium-ion energy storage battery itself is the primary factor affecting the safety of energy storage. As the core component of the energy storage system, the battery has potential thermal safety hazards under various complex working conditions, which is a safety problem faced in practical applications. Therefore, to fundamentally solve the thermal safety problem of lithium-ion batteries, research needs to be carried out from the aspect of battery intrinsic safety. Therefore, this invention takes the single cell as the research object to build a calculation model of the dynamic heat generation power of energy storage lithium batteries.

[0064] Step1: Heat Generation Rate Model of Lithium Battery The heat generation rate of the lithium battery can be solved by the heat generation equation proposed by Bernardi.

[0065] In the formula: q represents the heat generation power of the lithium battery, with the unit of W / m 3 ; V b represents the volume of the lithium battery, with the unit of m 3 ; U ocv 、 U respectively represent the open-circuit voltage and the working voltage of the lithium battery; I represents the working current of the lithium battery, with the unit of A; R represents the DC internal resistance of the lithium battery, with the unit of Ω; T represents the ambient temperature of the lithium battery, with the unit of K; represents the entropy heat coefficient, with the value range of 1 to 2.8×10 -4 , with the unit of mV / K.

[0066] It can be seen from the above formula that to accurately obtain the heat generation rate of the lithium battery, it is necessary to solve the DC internal resistance and the working current of the lithium battery.

[0067] Step2: DC Internal Resistance of Lithium Battery During the charge and discharge process of the lithium battery, the DC internal resistance ( R ) will fluctuate with the change of SOC. To refine the heat generation power of the standard lithium battery, it is necessary to fully consider the change of the lithium battery R . For different types of lithium batteries R The trend of change with SOC is generally the same, but there will be differences in numerical values. In the present invention, the measured data of the fluctuation of the internal resistance of the lithium battery given by the manufacturer with the change of SOC is fitted into a function expression by numerical fitting. Since the fluctuation data of the lithium battery R with the change of SOC has the best fitting relationship with the polynomial function, its function expression can be numerically fitted by the polynomial function.

[0068] In the formula: are polynomial coefficients, and the value of n can be determined according to the minimum deviation of numerical fitting.

[0069] Step3: Working Current of Lithium Battery The present invention selects the heat generation power of a single cell as the research object. Considering the series and parallel connection of energy storage cells, to solve the working current, reasonable assumptions need to be made: (1) Assume that the charge state of each battery cell in the energy storage system remains consistent; (2) Assume that the charging and discharging power of the lithium battery is constant during the sampling period.

[0070] Therefore, the working current of the single cell at each sampling moment in the operating cycle can be solved based on the following formula: Where: C N Indicates the rated capacity of a single cell, Ah; p Indicates the parallel connection of energy storage batteries.

[0071] Step 4: Solve the dynamic heat generation power of lithium battery From equations (26) and (27), it can be seen that the DC resistance of the energy storage lithium battery has an accurate functional relationship with the working current and the state of charge, so it is necessary to characterize the state of charge of the energy storage at any time. According to equation (24), the initial state of charge of the energy storage system can be solved, and the state of charge at each moment in the operation cycle can be solved by combining equation (21).

[0072] Substituting the solved state of charge at each moment of the operation cycle into equations 26 and 27, the DC internal resistance and working current at any moment in the operation cycle can be obtained. Then, equation 25 can be used to obtain the dynamic heat generation power of lithium batteries in this scenario, which can not only satisfy the requirements of smoothing the fluctuation of new energy power, but also accurately quantify the characterization of the dynamic heat generation power of lithium batteries in this scenario. Different from the conventional steady-state solution of the heat generation power of the battery cell, the dynamic heat generation power of the battery cell solved by this method is more representative and practical, and has certain practical engineering significance.

[0073] This method innovatively expresses the dynamic heat source of lithium batteries as a function of the state of charge (SOC) and time (t) of the energy storage system in the scenario of smoothing the power fluctuation of new energy, as shown in equation (29), making it feasible to quantitatively characterize the dynamic heat source of lithium batteries in the scenario of energy storage smoothing the power fluctuation of new energy.

[0074] Where: X( · ) , Y( · ) , Z( · ) are the abstract functions of dynamic heat generation power, working current and DC internal resistance respectively; F( · ) is the abstract function of the dynamic heat generation power obtained after solution.

[0075] Here, the charge state, DC internal resistance, and working current are all dynamically changing. Specifically, the charge state at different times considered in this application determines the dynamic DC internal resistance and dynamic working current at different times, and then the dynamic heat generation power is calculated by using the dynamic DC internal resistance and dynamic working current at different times as variables. By simplifying the above process, it can be considered that the fundamental reason for determining the dynamic heat generation power is the charge state at different times; finally, the above whole process is described by mathematical formula to obtain formula (29). This formula, as the core formula of this invention patent, further jointly expresses the heat generation power of formula 25, the DC internal resistance of formula 26, the working current of formula 27, and the charge state of formula 28, completing the presentation of the dynamic heat source from an abstract concept to an actual concrete formula.

[0076] Example 2: Figures 1 - 22 As shown, the overall idea is as follows Figure 1 As shown in the figure, the dynamic heat generation power calculation method of lithium batteries used to smooth out the fluctuation of new energy power includes the following four steps: cluster reduction of new energy output scenarios, adaptive wavelet packet decomposition, energy storage configuration model and energy storage battery dynamic heat generation power calculation model taking into account electrochemical-thermal coupling. Due to the large amount of calculation and complex process, computer program software is written to realize the automation of energy storage configuration optimization and dynamic heat generation power calculation in the scenario of smoothing new energy power fluctuation, so as to improve calculation efficiency and simplify daily analysis work.

[0077] The software is developed in Python. The software uses the output sequence of new energy stations as input conditions, and can quickly realize cluster analysis of typical scenarios of new energy at different time scales, adaptive discrete wavelet decomposition, energy storage configuration, dynamic thermal power calculation of lithium batteries, and visual drawing.

[0078] Hardware environment: CPU: i5-8300H 2.30GHz; Memory: 16GB; Hard disk: 1TB Software environment: Operating system: Windows 10; Supported software: Microsoft Office 2016.

[0079] Software composition: The software mainly consists of two parts, including the calculation module button on the left and the output result window on the right. Users can click the modules in order to perform calculations according to the software prompts in the output result window on the right, and enter the parameters according to the software prompts to complete the calculation. The output result window on the right will generate the corresponding conclusion information and result saving path. Users can check or modify the calculation results according to the saving path, such as Figure 2 For the convenience of reproduction, the author built a calculation software based on Python, which is divided into four steps and has a strong logical connection with the system flow chart of the present invention.

[0080] The aforementioned method for calculating the dynamic heat generation power of lithium batteries for suppressing the power fluctuations of new energy sources has the first step of clustering and reducing the new energy output scenarios, and the specific steps are as follows: Collect sample data: In the embodiment of the present invention, the annual second-level output data of an 80 MW wind farm in a certain area in 2022 is used as the sample data, and the sampling time is 10 s, with a total of 8,640 sampling points per day; The data input file contains two parts: (1) It is made by Excel software with the format of "csv", and the stored data is the output sequence of the new energy power station; (2) It is provided by the user according to the core parameters of the new energy power station and the energy storage lithium battery, such as the rated installed capacity of the new energy power station, the rated capacity of a single lithium battery cell, the internal resistance parameter, etc.

[0081] Data preprocessing: The sample data is statistically analyzed, and the data dimension is 8,640 rows × 364 columns. The outliers (such as negative values) and missing values (null values) are verified and supplemented through the moving average to complete data cleaning; Data dimensionality reduction processing based on principal component analysis (PCA): PCA is a commonly used dimensionality reduction technique. Its main purposes are to eliminate redundancy and correlation, improve computational efficiency, avoid the "curse of dimensionality", etc., and to retain the core features of the source data to a large extent, making the subsequent model training and analysis more efficient and accurate. In the embodiment of the present invention, the source data is subjected to dimensionality reduction processing based on PCA. By retaining the principal components that can explain 95% of the variance, the data dimension is reduced while the important information of the source data is retained as much as possible.

[0082] Solving for the optimal number of clusters: For the dimensionality-reduced data, the silhouette coefficient method is used for iterative solution, and the overall silhouette coefficient is used as the recommendation basis to obtain the optimal number of clusters. The optimal number of clusters in the embodiment of the present invention: k = 4, that is, the typical scenarios after clustering and reduction of the source data should be 4 categories.

[0083] Clustering and reduction: Based on the k-means algorithm, the original output data of the wind power is clustered and reduced; After preparing the input file and inputting the corresponding parameters according to the prompt requirements, click the calculation button of the corresponding module in sequence to complete the calculation task. Finally, the required key parameters and the corresponding output files will be generated. This software includes 4 calculation results, and each calculation result includes two types: calculation tables and pictures. The table format is "csv" and the picture format is "png".

[0084] Module 1: Clustering and reduction of new energy output scenarios There are two types of pictures, including the optimal clustering iteration result diagrams and the typical scenario result diagrams of the elbow method and the silhouette coefficient method. They are respectively Figure 4 and 5 The solution results of the number of typical scenarios, Figure 6Typical scenario result diagram, divided into 4 typical scenarios, with the abscissa being time and the ordinate being new energy output; The calculation table is the output sequence (output power) of typical scenarios, mainly the time series of typical scenarios after clustering reduction and the new energy output power sequence at corresponding moments. Such as Figure 7 Calculation results of the output sequence of typical scenarios.

[0085] The clustering reduction results are as Figure 4 and 5 shown, and a total of 4 types of typical scenarios are included. Based on the optimization analysis results of double-criterion clustering, it can be seen from the elbow method analysis that when the number of clusters increases to 4, a significant "elbow" inflection point appears in the curve of the sum of squared distances from samples to centroids, and its subsequent changes tend to be gentle; the silhouette coefficient method verification shows that when k = 4, the system obtains the peak silhouette coefficient, and the coefficient attenuation amplitude between adjacent numbers of clusters is relatively large. The consistency verification of the two methods shows that when the number of new energy output scenarios is determined to be 4, the optimal balance can be achieved between intra-class compactness and inter-class separation. It can be seen from the figure that the power fluctuations of each typical scenario are relatively obvious, among which the fluctuation of typical scenario 4 is the most obvious, the fluctuations of typical scenarios 1 - 2 are relatively weak, and the fluctuation of typical scenario 3 is the weakest. The new energy typical original output scenarios are obtained through clustering reduction, and the following calculations are based on typical scenarios.

[0086] The aforementioned calculation method for the dynamic heating power of lithium batteries used to suppress new energy power fluctuations, the second step is adaptive wavelet packet decomposition, and the specific steps are as follows: Judgment of the necessity of energy storage configuration: According to the volatility constraint (Equation 9), judge whether energy storage needs to be configured for each typical scenario. Based on the aforementioned definition of new energy volatility and volatility limit values, calculate the volatility of each typical scenario, and judge whether energy storage needs to be configured to meet the grid connection power requirements. The judgment results of the necessity of energy storage configuration for each scenario include the optimal wavelet basis function and the best decomposition level.

[0087] The results show that typical scenario 3 does not need to configure energy storage to meet the new energy grid connection volatility limit. Typical scenarios 1, 2, and 4 all need to configure energy storage to meet the new energy grid connection volatility limit.

[0088] Module 2: Adaptive discrete wavelet transform Spectrum analysis: Use DFT to perform spectrum analysis on typical scenarios that require energy storage configuration. The results show that the energy of the wind power output mainly concentrates in the low-frequency part, and the energy in the high-frequency part is relatively low. This is consistent with the wind speed characteristics. The amplitude of high-frequency changing wind speed is very small, while the amplitude of low-frequency changing wind speed is relatively large. Therefore, the low-frequency signal is used as the expected power value for new energy grid connection, and the high-frequency power signal is suppressed by the energy storage system, which can not only meet the smoothness of grid connection power but also take into account the impact on the performance of the energy storage system.

[0089] There are two categories of pictures, such as Figures 8 - 11 The figure showing the amplitude-frequency characteristic analysis results under each typical scenario; the amplitude-frequency characteristic spectrum of typical scenarios (including the full-frequency domain distribution and local refinement of the low-frequency section). In the coordinate system, the abscissa represents frequency and the ordinate represents amplitude. Spectrum analysis shows that new energy output presents significant energy aggregation characteristics in the low-frequency band; while in the high-frequency band, the amplitude distribution tends to be stable.

[0090] such as Figures 12 - 14 The figure showing the comparison results of new energy output sequences before and after suppression. In the coordinate system, the vertical axis represents power (MW) and the horizontal axis represents time. They are the reconstructed diagrams of typical scenario 1, typical scenario 2, and typical scenario 4 - the comparison results of new energy output sequences before and after suppression (energy storage does not need to be configured for suppression in typical scenario 3). Among them, the yellow line is the result after suppression and the blue line is the result before suppression. It can be clearly found that the change trend of new energy output after suppression is much smoother than that before suppression.

[0091] Adaptive wavelet packet decomposition: Due to space limitations, in this embodiment, typical scenario 1 is taken as an example for illustration, and the specific steps for other scenarios are similar. Through adaptive wavelet packet decomposition, the optimal decomposition level is determined. When the fluctuation characteristics of the low-frequency components meet the grid connection constraint conditions, the appropriate decomposition level can be determined. In this embodiment, for typical scenario 1, the optimal wavelet basis function is selected as 'db25' and the optimal decomposition level is determined to be 4 layers. At this step, the expected grid connection power of new energy and the expected grid connection power command of the energy storage system .

[0092] The calculation table has the amplitude-frequency characteristic calculation results and the output sequence calculation results after discrete wavelet decomposition and reconstruction under each typical scenario. The amplitude-frequency characteristic result diagram has the calculation results of frequency and amplitude, which are for typical scenarios 1, 2, and 4 (scenarios that require energy storage configuration). The first column is the frequency data and the second column is the amplitude data.

[0093] The calculation results of discrete wavelet decomposition include the original power of new energy, the expected power of the reconstructed new energy and energy storage output. They are for typical scenarios 1, 2, and 4. The first column is the original output data of new energy, the second column is the output data of new energy after suppression, and the third column is the power to be compensated by the energy storage (corresponding to the expected power of the energy storage).

[0094] Module three: Energy storage configuration model The aforementioned calculation method for the dynamic heat generation power of lithium batteries used to suppress the power fluctuation of new energy. The third step is to construct an energy storage configuration model, and the specific steps are as follows: Optimization objective of energy storage configuration model: Construct a two-objective optimization model. The specific requirement is that on the 1-minute and 10-minute time scales (using sliding values), the combined grid-connected power volatility of "wind power + energy storage" is lower than the limit value. Since the rated capacity of wind power in the embodiment of the present invention is 80 MW, there is .

[0095] Constraints of energy storage configuration model: To meet the safe and stable operation of the energy storage system, the constraints of the model in the embodiment of the present invention are as follows.

[0096] (1) Energy storage cycle efficiency constraint. Considering the actual comprehensive cycle efficiency of the lithium battery energy storage system, the value of the energy storage cycle efficiency is ; (2) Charge balance constraint, the power downshift amount is ; (3) Charge and discharge upper and lower limit constraints take values of ; (4) Initial state of charge constraint is .

[0097] Output results of energy storage configuration model: Solve the model to obtain the energy storage configuration results under typical scenario 1.

[0098] (1) The rated power of the configured energy storage system is ; (2) The rated capacity of the configured energy storage system is ; (3) Comparison of grid-connected power fluctuations: It can be seen from Figure 18 that the results obtained by solving based on this model meet the optimization objective - , , and the grid-connected fluctuations after configuring energy storage through the model built by the present invention have been significantly improved compared with the original fluctuations; For example, Figure 18As shown in the figure, it is a comparison of the grid connection volatility of new energy before and after configuring energy storage for 1 minute and 10 minutes respectively. The red line is the maximum volatility specified by the standard. The gray line is the volatility without energy storage configuration. It can be clearly seen that the maximum volatilities of 1 minute / 10 minutes are 42.5% and 58.4% respectively, and there are some moments when it exceeds the red line range, indicating that it does not meet the standard and may lead to curtailment of electricity. The blue line is the volatility after configuring energy storage. It can be clearly seen that the maximum volatilities of 1 minute / 10 minutes are reduced to 9.2% and 29.6% respectively, meeting the standard requirements. The maximum volatilities of 1 minute and 10 minutes without energy storage configuration are 42.5% and 58.4% respectively, and the maximum volatilities of 1 minute and 10 minutes after configuring energy storage are 9.2% and 29.6% respectively. The improvement amplitudes of the maximum volatilities of 1 minute and 10 minutes are 78.4% and 49.3% respectively, verifying the effectiveness of the model. Therefore, this solution can be applied to the energy storage configuration in the scenario of suppressing the power fluctuation of new energy.

[0099] The information in the output result window includes charge-discharge efficiency, rated power of energy storage, rated capacity of energy storage, and initial state of charge, etc. As Figure 20 shown. It includes the best polynomial order, the fitted polynomial equation, the saving path of the dynamic heat generation power calculation result, etc.

[0100] There are two types of pictures, including the power sequence curve graph of the energy storage system (desired power of the energy storage system, primary power command, and grid connection power command) as Figure 16 shown. In the coordinate system, the vertical axis represents power (MW), and the horizontal axis represents time (hours). The desired power command is connected with the result obtained by discrete wavelet decomposition. The primary power command of the energy storage system is the energy storage output sequence obtained after considering the charge-discharge efficiency of the energy storage system. The grid connection power command of the energy storage system is the energy storage output sequence after considering the charge balance.

[0101] The energy change curve graph of the energy storage system (cumulative energy curve of the energy storage system and SOC curve) as Figure 17 shown. In the cumulative energy curve of the energy storage system, the vertical axis represents energy (MWH), and the horizontal axis represents time (hours); in the SOC curve of the energy storage system, the vertical axis represents SOC (%), and the horizontal axis represents time (hours). The cumulative energy curve is the actual energy change, with the unit of MWh, while the SOC curve is the per-unit value of the energy change. Therefore, the change trends of the two curves are the same.

[0102] Module Four: Calculation of Lithium Battery Dynamic Heat Power The aforementioned method for calculating the dynamic heat generation power of lithium batteries for suppressing the power fluctuation of new energy. The fourth step is the calculation model of the dynamic heat generation power of the energy storage battery considering the electrochemical-thermal coupling. The specific steps are as follows: Cell parameter input: In the embodiments of the present invention, a lithium iron phosphate energy storage battery is used. The parameters of the energy storage system are as follows: the rated capacity of a single cell is 314 Ah, the rated voltage is 3.2 V, and the cell volume is 173 mm × 54 mm × 200 mm (length × width × height) = 186840 mm 3 , and the series-parallel connection method of the energy storage system is 12P416S, and the operating environment temperature is 25 °C.

[0103] According to the experimental results of the DC internal resistance and SOC measured by the manufacturer, it is necessary for the user to provide the measured data of SOC-DC internal resistance according to the actual situation of the configured energy storage lithium battery. As Figure 19 shown. It shows that the DC internal resistance changes with the change of SOC, rather than being constant.

[0104] The calculation information of the output window result includes the best-fitting polynomial order - polynomial function relationship - calculation result storage information, etc. As Figure 20 shown.

[0105] The functional relationship between the DC internal resistance and SOC is fitted by numerical fitting. The best fit is a 7th-order polynomial function, and its functional relationship is as follows: ; The best polynomial fitting relationship diagram containing SOC-R is as Figure 21 shown.

[0106] Reasons for using polynomial fitting for cell internal resistance: According to the change trend of the internal resistance of lithium battery cells and SOC, the academic and industrial circles usually use polynomial fitting to numerically describe their relationship. Therefore, the same method is also used for data verification in this invention patent.

[0107] Determination of polynomial coefficient values: The determination method is relatively conventional. Methods such as the least squares method, Lagrange interpolation method, Newton interpolation method, and programming languages can be used for polynomial fitting; among them, the least squares method finds the best function match for the data by minimizing the sum of the squares of the errors; the Lagrange interpolation method constructs a polynomial function through known data points and makes predictions at unknown points; there are also many ways to implement it in programming languages. MATLAB provides the polyfit function, which can conveniently perform polynomial fitting.

[0108] In the embodiments mentioned in the present invention, the operation is based on python. The specific polynomial order is determined through iterative experiments, that is, 2nd order, 3rd order... 7th order... 10th order. The specific order is determined by judging the error between the experimental points and the curve, and the polynomial coefficients are obtained by fitting using np.polysit in the data processing numpy library of python.

[0109] Electrochemical-thermal coupling model: Based on the aforementioned formula (25), an electrochemical-thermal coupling model is established. To solve the dynamic heat generation power of the lithium battery, it is necessary to first solve the SOC, DC internal resistance, and working current at any time.

[0110] (1) SOC at any time: According to the energy storage configuration result and based on the aforementioned formula (28), solve the SOC at any time; (2) DC internal resistance at any time: Substitute the SOC at any time into the polynomial function to solve the DC internal resistance at any time; (3) Working current at any time: Based on the aforementioned formula (27), solve the working current at any time; (4) Dynamic heat generation power: Substitute the existing parameters into the electrochemical-thermal coupling model to solve the dynamic heat generation power during the operation period of the energy storage lithium battery. As Figure 22 shown. According to the solution steps, the dynamic heat generation power of the lithium battery in the scenario of suppressing new energy power fluctuations can be solved, as shown in the figure. The vertical coordinate is the heat generation power, and the horizontal coordinate is the time. It can be clearly seen that with the change of time, the time-varying characteristics of the heat generation power are more obvious, and the results also verify the necessity of solving the dynamic heat generation power in this paper.

[0111] Results of dynamic heat generation power: The results of the dynamic heat generation power of the lithium battery are as Figure 22 shown. Combining Figure 18 with the analysis of the new energy output fluctuation, the trend analysis of the dynamic heat generation power of the Figure 22 lithium battery shows that from 4h to 14h, due to the large volatility of wind power output at the corresponding time in this scenario and the increasing energy storage demand, the heat generation power is relatively large; at other times, the wind power output is relatively stable, the energy storage demand is low, and the heat generation power is also small.

[0112] This method not only optimizes the energy storage configuration to enhance the stability of new energy access to the power grid, but also further quantifies the dynamic thermal behavior of lithium batteries in the scenario of suppressing new energy power fluctuations, effectively improving the safety and reliability of the energy storage system, and providing a data basis for lithium battery temperature change prediction and thermal management.

Claims

1. A method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy, characterized in that The steps include: S1. The improved K-means algorithm based on the silhouette coefficient method is used to cluster the historical output data of new energy sources and extract typical output scenarios; S2. Taking the fluctuation rate of the grid-connected power of new energy sources satisfying the fluctuation rate constraint as the optimization goal, an adaptive wavelet packet decomposition algorithm is used to decompose and reconstruct the typical output scenario, and determine the expected grid-connected power command; S3. Based on the expected grid-connected power instruction, considering the rated power configuration and the rated capacity configuration, construct an energy storage configuration model and solve the energy storage configuration model to obtain an energy storage configuration result; S4, solving the initial state of charge based on the energy storage configuration result, and then combining the energy storage calculation formula of the energy storage system at any time to obtain the charge state calculation formula at each time in the operation cycle; S5. Construct the functional relationship between the dynamic change of DC resistance and working current with the state of charge respectively, and then combine it with the state of charge calculation formula at each moment in the operation cycle, calculate the dynamic heat generation power of the lithium battery through the Bernardi heat generation rate formula, and characterize the characteristic mapping relationship between the dynamic heat source and the state of charge and time through the dynamic heat generation formula of the lithium battery under energy storage to smooth fluctuations.

2. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein The S1 includes: S11, obtaining and statistically analyzing the historical output data of new energy sources within a set time interval, and verifying and supplementing abnormal values ​​and missing values ​​through a sliding average value; S12, historical output data of new energy sources after simplification through principal component analysis; S13, setting the number range of cluster centers and performing iterative solution through the silhouette coefficient method to obtain the optimal number of clusters; S14. Based on the optimal number of clusters, a typical scenario is obtained through a K-means algorithm and the new energy output power of the typical scenario is generated.

3. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein The S2 includes: S21. Define the volatility of new energy and consider the volatility constraint, determine whether energy storage needs to be configured, and obtain the configuration result; S22. Based on the configuration result, the grid-connected power of new energy and the expected grid-connected power instruction are obtained by using the optimization target that the grid-connected power fluctuation rate of new energy meets the fluctuation rate constraint and the adaptive wavelet packet decomposition algorithm.

4. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 3, wherein The configuration results include: if the output power of new energy in a typical new energy scenario meets the grid-connected volatility constraint, there is no need to configure energy storage and the system can be directly connected to the grid for operation; if the grid-connected volatility constraint is not met, energy storage is configured.

5. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 4, wherein In the S22, the output power that needs to be smoothed is spectrally analyzed by discrete Fourier transform to obtain the amplitude-frequency characteristic results under the output scenario. At a fixed scale, a wavelet packet function is defined to obtain a wavelet packet decomposition algorithm and a wavelet packet reconstruction algorithm. The new energy output power is decomposed into a high-frequency component and a low-frequency component. The low-frequency component is judged using the grid-connected fluctuation rate constraint. When the amplitude fluctuation of the low-frequency component meets the grid-connected fluctuation constraint for the first time, the optimal decomposition layer is determined. At the same time, the low-frequency component is used as the expected grid-connected power, and the difference between the expected grid-connected power and the original output power of the new energy is used as the expected grid-connected power instruction.

6. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein In S3, solving the energy storage configuration model includes solving the rated power and rated capacity of the energy storage system; The solution of the rated power of the energy storage system includes solving the expected power of the energy storage system based on the expected grid connection power command, considering the comprehensive cycle efficiency and charge-discharge efficiency of the energy storage, obtaining the primary power command of the energy storage system, and then considering the satisfaction of the charge balance constraint to obtain the rated power of the energy storage system; The solution of the rated capacity of the energy storage system includes solving the energy fluctuation of the energy storage system compared with the initial state during the operation period based on the primary power command, obtaining the calculation formula of the stored energy at any time of the energy storage system, and then obtaining the state of charge expression. At the same time, making it satisfy the upper and lower limits of charge and discharge of the energy storage system, calculating the ratio of the difference between the maximum and minimum values of the energy fluctuation of the energy storage system during the entire operation period to the difference between the upper and lower limits of its charge and discharge to obtain the rated capacity of the energy storage system.

7. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein In step S4, the initial state of charge is specifically calculated through the following expression: ； Wherein, represents the initial charge state, is the rated capacity of the energy storage system, represents the upper limit of energy storage charging, represents the lower limit of energy storage discharging, is the stored energy of the energy storage system at any time.

8. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, characterized in that In step S4, the specific expression of the DC resistance is: ； In the formula, is the polynomial coefficient; is the DC resistance, is the exponential function of the state of charge.

9. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein In step S4, the specific expression of the DC resistance is: ； Wherein, is the rated capacity of a single battery cell; when t = 1, ; is the sampling time interval of new energy output data; is the battery cell parallel connection data; is the time corresponding to the sampling point; is the initial state of charge; Among them, when calculating the DC resistance, the series and parallel situations of the energy storage battery cells are considered, assuming that the state of charge of each battery cell in the energy storage system remains the same; it is assumed that the charge and discharge power of the lithium battery is constant during the sampling period.

10. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 8, wherein The calculation formula of the state of charge at each moment during the operation period is specifically expressed as follows: ； Wherein, is the rated capacity of the energy storage system; is the time point corresponding to the end period of the sampling point; is the grid-connected power corresponding to the energy storage at time t.

11. The method for calculating the dynamic heat generation power of a lithium battery for suppressing the power fluctuation of new energy according to claim 1, wherein The dynamic heat generation formula of the lithium battery under the energy storage fluctuation suppression is specifically expressed as follows: ； In the formula, X(·), Y(·), and Z(·) are abstract functions of the dynamic heat generation power, working current, and DC internal resistance respectively; F(·) is the abstract function of the dynamic heat generation power obtained after solution.

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