Multi-element energy storage collaborative fluctuation stabilizing method

Abstract

The invention provides a multi-element energy storage collaborative fluctuation stabilizing method, which comprises the following steps of: 1, performing empirical mode decomposition on an original new energy grid-connected power signal to obtain a plurality of IMF components; 2, reconstructing the IMF component according to the grid-connected power and the amplitude-frequency response capability of each energy storage device, and dividing a low-frequency component, an intermediate-frequency component and a high-frequency component corresponding to different time scales respectively; step 3, establishing a charge-discharge efficiency linearization model of each energy storage device, performing linearization modeling based on an MLD theory, and processing a nonlinear problem through a piecewise linearization method; and 4, by taking the minimum grid-connected fluctuation and the highest energy utilization rate as targets, constructing a multi-element energy storage collaborative fluctuation stabilizing stochastic optimization model, and optimizing the output strategy of each energy storage device. Through EMD decomposition and reconstruction, separation of power fluctuation at different time scales is realized, so that different types of energy storage devices give full play to the frequency response advantage, and the cooperation efficiency and the energy utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy storage technology, and in particular to a method for synergistically mitigating volatility through multi-energy storage. Background Technology

[0002] With the accelerated global energy structure transformation, the consumption of fossil fuels and carbon emissions have become increasingly serious problems, driving the widespread application of clean, low-carbon, and renewable energy. In recent years, the proportion of new energy power generation in my country has been continuously increasing, and the power generation of renewable energy sources such as wind and solar power has also been steadily growing. However, due to the volatility and intermittency of renewable energy, it poses a severe challenge to the stable operation of the power grid. Existing wind, solar, and hydrogen storage systems often cannot respond to changes in grid demand in a timely manner, leading to difficulties in power absorption or excessive system load. For example, most of them adopt traditional independent control modes, lacking unified optimization and dispatch, resulting in low energy utilization efficiency and poor economic performance. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for synergistically mitigating volatility through multi-energy storage, which can solve the problems of low synergistic efficiency and poor energy utilization of multi-energy storage devices in new energy grid connection.

[0004] Therefore, the present invention adopts the following technical solution: A method for synergistically mitigating volatility through multi-element energy storage includes the following steps: Step 1: Perform empirical mode decomposition on the original grid-connected power signal of new energy sources to obtain multiple IMF components; Step 2: Based on the grid-connected power and the amplitude-frequency response capability of each energy storage device, the IMF components are reconstructed to divide them into low-frequency components, mid-frequency components, and high-frequency components corresponding to different time scales. Step 3: Establish a linearized model for the charging and discharging efficiency of each energy storage device. Based on MLD theory, perform linearization modeling and handle nonlinear problems through piecewise linearization. Step 4: With the goal of minimizing grid connection fluctuations and maximizing energy utilization, construct a stochastic optimization model for multi-energy storage to collaboratively mitigate fluctuations and optimize the output strategies of each energy storage device.

[0005] Based on the above technical solutions, the present invention may also employ the following further technical solutions, or combine these further technical solutions: The steps of the empirical mode decomposition are as follows: A. Determine all local maxima and local minima of the signal x(t); B. Constructing the upper and lower envelopes: The upper envelope of the signal, eupper(t), is obtained by interpolating all local maxima; similarly, the lower envelope, elower(t), is obtained by interpolating all local minima. This invention uses cubic spline interpolation to smoothly connect these extreme points. C. Calculate the mean line: Calculate the mean of the upper and lower envelope lines to obtain the mean line m(t) reflecting the low-frequency trend: (1); D. Extracting detail components: Subtracting the mean from the original signal yields a new signal h(t): (2); E. Determine the IMF condition: Check if h(t) satisfies the definition of IMF: In the entire data segment, the number of extreme points and the number of zero-crossing points must be equal or differ by at most one. At any time, the average of the upper envelope formed by the local maxima and the lower envelope formed by the local minima is zero. If h(t) satisfies the IMF condition, then h(t) is an IMF; otherwise, treat h(t) as a new signal and repeat the above steps. F. Iterative decomposition: The extracted IMF is removed from the original signal to obtain the residual signal. (3); Repeat the above steps for the residual signal until the residual signal becomes a monotonic function or meets the termination condition. G. Termination conditions: The termination conditions of the screening process usually include: stopping when the detail component h(t) meets the IMF condition; stopping when the mean m(t) is sufficiently small; Through the above steps, the original new energy grid-connected power signal is decomposed into several IMF components and a residual term.

[0006] In step two, when classifying low-frequency components, C2F reconfiguration components with large amplitude, small frequency, and fluctuations that meet grid connection requirements are prioritized as low-frequency components. At the same time, it is necessary to ensure that the overall trend of medium / high frequency components within the scheduling cycle meets the capacity constraints of medium / high frequency energy storage devices, so as to avoid energy storage operating in a low / high charge state for a long time.

[0007] When dividing the low-frequency components, equations (4) and (5) should be satisfied, with equation (4) having higher priority than equation (5): (4) (5) In the formula: △(x) is the maximum fluctuation of x within 10 minutes; The limit for grid connection fluctuations is set at 5% of the installed wind power capacity. This represents the margin factor for the hybrid energy storage capacity. This refers to the overall capacity of the hybrid energy storage system.

[0008] In step two, when dividing the medium / high frequency components, the F2C reconstruction components with high frequency, small amplitude, and periodic oscillation are preferentially classified as high frequency components. At the same time, it is necessary to ensure that the fluctuation amplitude of the medium / high frequency components meets the real-time response capability of the corresponding energy storage devices, including batteries, flow batteries, and supercapacitors.

[0009] When dividing the mid / high frequency components, equations (6) and (7) should be satisfied, and equation (6) has higher priority than equation (7): (6) (7) In the formula: Kbat-H and Ksc are the power margin coefficients of the battery-flow battery and the supercapacitor, respectively; Pbat-Hmax and Pscmax are the maximum power of the battery-flow battery and the supercapacitor, respectively.

[0010] In step three, the linearization models for the charge and discharge efficiency of each energy storage device include the linearization models for the charge and discharge efficiency of batteries and the linearization models for the charge and discharge efficiency of flow batteries.

[0011] Construct a linearized model for the charge and discharge efficiency of the battery: (8) In the formula: η ba tc，i With η bat d，i Let μ be the charge / discharge efficiency of the battery in the i-th state of charge (SOC) interval. c t and μ d t These are 0-1 variables representing the charging and discharging states of the battery, respectively; vi is the signal representing the state of charge (SOC) range of the battery; P c i，t With P d i，t These represent the charging and discharging power of the battery within the corresponding range; a i With b i , respectively, are the left and right endpoints of the i-th soc interval; n is the total number of soc intervals. Since equation (8) contains the form of variable multiplication, the nonlinear element of equation (8) is further linearized according to MLD theory: (9) In the formula: v a i With v bi For intermediate 01 variables; Equation (9) will use variable v i The equivalent variables are treated as linear variables, which facilitates the solution of the model.

[0012] (10) In the formula: M and m are the maximum and minimum constants, respectively; Equation (10) uses the variable P c i，t Equivalently treated as a linear variable, similarly, in the same way, this invention treats P as a linear variable. d i，t It is equivalent to a linear variable.

[0013] Constructing a linearized model for the charge-discharge efficiency of a flow battery: (11) In the formula: η vrb c,i With η vrb d,i Let α be the charge / discharge efficiency of the flow battery in the i-th power range; c t and α d t , respectively, are 0-1 variables representing the charging and discharging states of the flow battery; βi is a signal representing the power range of the flow battery; W c i,t With W d i,t These represent the charge and discharge power of the flow battery within the corresponding range; A i With B i Let be the left and right endpoints of the i-th power interval, respectively; N is the total number of power intervals. The nonlinear element of equation (11) is linearized as follows: (12) Where: β a i With β b i It is an intermediate 01 variable.

[0014] = (13).

[0015] The objective function of the medium / high frequency hybrid energy storage fluctuation mitigation model is: (16) In the formula: Cun and Wun are the grid connection fluctuation penalty cost and fluctuation penalty coefficient, respectively; Pwi,t is the actual grid connection power of typical daily scenario i after optimization at time t; Let be the target grid-connected power of typical daily scenario i at time t; Closs is the energy loss cost; μloss is the energy loss penalty coefficient; πi is the probability of occurrence of typical daily scenario i. Since the charging / discharging power of energy storage during actual operation is a certain value, the energy storage charging / discharging power corresponding to all scenarios is equal at any time in the stochastic optimization control model. The constraints of this model are Equations (8) and (11) and the supercapacitor operation constraints.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: by decomposing and reconstructing EMD, the power fluctuations are separated at different time scales, enabling different types of energy storage devices to give full play to their frequency response advantages and improve collaborative efficiency and energy utilization. Attached Figure Description

[0017] Figure 1 This is the overall mind map of the present invention.

[0018] Figure 2 This is a diagram of the signal reconstruction model of the present invention. Figure 3 This is a diagram of the original wind power grid connection data for this invention.

[0019] Figure 4 This is a low-frequency signal reconstruction diagram of the present invention.

[0020] Figure 5 This is a diagram showing the wind power signal reconstruction result of the present invention.

[0021] Figure 6 This is a graph showing the charge and discharge efficiency of the battery according to the present invention.

[0022] Figure 7 This is a linearization graph of the battery charging and discharging efficiency of the present invention.

[0023] Figure 8 This is a diagram showing the charge and discharge efficiency of the flow battery of the present invention.

[0024] Figure 9 This is a linearization graph of the charge and discharge efficiency of the flow battery of the present invention.

[0025] Figure 10 The flowchart shows the benchmark model for charging and discharging vanadium redox flow batteries based on constant current charge and discharge control.

[0026] Figure 11 This is a flowchart of the high-frequency hybrid energy storage model for smoothing fluctuations in this invention.

[0027] Figure 12 This is a diagram showing the operation of the battery in this invention.

[0028] Figure 13 This is a diagram showing the operation of the flow battery of the present invention.

[0029] Figure 14 This is a diagram showing the operation of the supercapacitor of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the technical solutions of the present invention, preferred embodiments of the present invention are described below in conjunction with specific examples. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote elements with the same or similar functions throughout. However, it should be understood that the drawings are for illustrative purposes only and should not be construed as limiting the present invention. To better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size. It is understandable for those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings. The positional relationships described in the drawings are for illustrative purposes only and should not be construed as limiting the present invention.

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0032] The construction idea of ​​the method for synergistically mitigating volatility using multiple energy storage systems in this invention is as follows: Figure 1 As shown.

[0033] It includes the following three technical points.

[0034] Technical Point 1: Signal Reconstruction Method Based on Differentiated Amplitude-Frequency Response Characteristics of Energy Storage To fully consider the differentiated amplitude-frequency characteristics of various energy storage components, this invention proposes a differentiated signal reconstruction theory based on Empirical Mode Decomposition (EMD). This theory, based on the amplitude-frequency characteristics of energy storage components, reconstructs the original grid-connected signal from both capacity and frequency dimensions to determine the pre-scheduled target power for various energy storage devices. This method enables efficient coordinated scheduling of energy storage devices, fully leveraging their response advantages in different frequency bands, thereby effectively improving the overall system performance and stability.

[0035] 1.1 EMD Decomposition Theory Empirical Mode Decomposition (EMD) is a data-driven adaptive signal processing method. Its core lies in decomposing signals based on their inherent time-scale characteristics, without relying on any pre-defined basis functions, thus enabling time-frequency domain analysis. This method effectively reveals the intrinsic structure and characteristics of signals by decomposing complex signals into several Intrinsic Mode Functions (IMFs). EMD exhibits significant advantages in processing non-stationary and nonlinear data, and is particularly suitable for analyzing nonlinear and non-stationary signal sequences. Its adaptability and local feature analysis capabilities enable it to effectively handle complex signal variations, resulting in a high signal-to-noise ratio in noise suppression and feature extraction. The EMD signal reconstruction steps are as follows: Identify all local maxima and local minima in the signal x(t).

[0036] Constructing the upper and lower envelopes: The upper envelope eupper(t) of the signal is obtained by interpolating all local maxima; similarly, the lower envelope elower(t) is obtained by interpolating all local minima. This invention uses cubic spline interpolation to smoothly connect these extreme points.

[0037] Calculate the mean line: Calculate the mean of the upper and lower envelope lines to obtain the mean line m(t) reflecting the low-frequency trend. (1) Extracting detail components: Subtracting the mean from the original signal yields a new signal h(t): (2) Determine the IMF condition: Check if h(t) satisfies the definition of IMF: Throughout the entire data segment, the number of extreme points and the number of zero-crossing points must be equal or differ by at most one. At any given time, the average of the upper envelope formed by local maxima and the lower envelope formed by local minima is zero. If h(t) satisfies the IMF condition, then h(t) is an IMF; otherwise, treat h(t) as a new signal and repeat the above steps.

[0038] Iterative decomposition: The extracted IMFs are removed from the original signal to obtain the residual signal. (3) Repeat the above steps for the residual signal until the residual signal becomes a monotonic function or the termination condition is met. Termination conditions: The termination conditions of the screening process usually include: stopping when the detail component h(t) meets the IMF condition; stopping when the mean m(t) is sufficiently small.

[0039] Through the above steps, the original complex signal is decomposed into several IMF components and a residual term, with each IMF component representing local features at different time scales in the signal.

[0040] 1.2 Signal Reconstruction Method Based on Amplitude-Frequency Characteristics Based on the above signal decomposition, when reconstructing the low / medium / high frequency components of the original grid-connected power signal, it is necessary to fully consider the grid-connected power requirements and the amplitude-frequency response capabilities of each energy storage device. Therefore, this invention establishes differentiated reconstruction standards for the low / medium / high frequency components: 1) When classifying low-frequency components, C2F reconfiguration components with large amplitude, small frequency, and fluctuations that meet grid connection requirements should be prioritized as low-frequency components. Simultaneously, the overall trend of medium / high-frequency components within the scheduling cycle must meet the capacity constraints of medium / high-frequency energy storage devices to avoid long-term operation of energy storage in a low / high charge state. Therefore, when classifying low-frequency components, equations (4) and (5) should be satisfied: (4) (5) In the formula: △(x) is the maximum fluctuation of x within 10 minutes; The limit for grid connection fluctuations is set at 5% of the installed wind power capacity. This represents the margin factor for the hybrid energy storage capacity. This refers to the overall capacity of the hybrid energy storage system.

[0041] 2) When classifying the medium / high frequency components, the F2C reconstruction components with high frequency, small amplitude, and periodic oscillations should be prioritized as high-frequency components; at the same time, the fluctuation amplitude of the medium / high frequency components must be ensured to meet the real-time response capability of the corresponding energy storage equipment. Therefore, when classifying the medium / high frequency components, equations (6) and (7) should be satisfied: (6) (7) In the formula: Kbat-H and Ksc are the power margin coefficients of the battery-flow battery and the supercapacitor, respectively; Pbat-Hmax and Pscmax are the maximum power of the battery-flow battery and the supercapacitor, respectively.

[0042] This invention is based on the signal reconstruction model proposed by EMD, as follows: Figure 2 As shown; like Figure 2As shown, when the present invention performs signal reconstruction based on EMD, it incorporates grid-connected power and the differentiated response characteristics of various energy storage systems into the signal reconstruction strategy through equations (4)-(7). Considering that the low-frequency power of the reconstruction must be strictly less than the grid-connected power limit, and that the battery-flow battery is the main component of the present invention for smoothing wind and solar fluctuations, the present invention sets the priority of equations (4) and (6) to be higher than that of equations (5) and (7) respectively. When it is impossible to identify a reconstruction signal that meets all conditions, the reconstruction model of the present invention prioritizes satisfying equations (4) and (6). A simulation was conducted on a wind farm with an installed capacity of 1500MW, and the study duration T was 24 hours. The wind power characteristic data and its decomposition results for a typical day are as follows. Figure 3 , Figure 4 , Figure 5 As shown; For the raw wind power grid-connected data, the EMD signal reconstruction model of this invention filters out 2nd and 3rd order c2f low-frequency reconstructed signals that meet the grid-connection requirements. Compared with the 3rd order c2f signal, the 2nd order c2f signal has higher smoothness; therefore, this invention selects the 2nd order c2f signal as the target grid-connected power. Figure 4 As shown, the model further decomposes the 5th-order f2c signal based on the ability of medium / high-frequency energy storage to smooth fluctuations and the operating frequency. Ultimately, the 5th-order IMF component is determined to be the medium-frequency component, and the 4th-order f2c high-frequency reconstructed signal is determined to be the high-frequency component. The signal reconstruction results are as follows: Figure 5 As shown, the low-frequency reconfiguration component of the wind power grid-connected power exhibits good smoothness without significant fluctuations, meeting the stability requirements of the grid-connected power. The mid- and high-frequency components have small oscillation amplitudes and strong periodicity, consistent with the charging and discharging characteristics of energy storage devices, and show no overall positive or negative trend. This helps reduce the likelihood of energy storage devices failing to operate normally when reaching their upper or lower capacity limits, thereby improving the operating efficiency and utilization rate of energy storage devices.

[0043] Technical Point 2: Linearization of Charge and Discharge Efficiency To fully consider the differences in charge and discharge efficiency characteristics between storage batteries and flow batteries, and to improve energy utilization while smoothing out power fluctuations, this study uses mixed integer linear programming (MLD) theory to linearize the charge and discharge efficiency characteristics of storage batteries and flow batteries respectively. By using piecewise linearization to handle nonlinear problems, the study achieves accurate modeling of the charge and discharge efficiency characteristics of energy storage devices.

[0044] 2.1 Battery Efficiency Model The charging and discharging efficiency of a battery is closely related to its state of charge (SOC), a relationship influenced by a combination of factors. First, changes in SOC affect the electrochemical reactions within the battery. During charging, as the SOC increases, the internal chemical reactions gradually approach saturation, leading to a decrease in charging efficiency. For example, when the SOC approaches 100%, the charging current is primarily used for side reactions such as water electrolysis, rather than for effective energy storage, thus reducing charging efficiency. Furthermore, discharge efficiency also varies with SOC; generally, lower SOC results in lower discharge efficiency, while higher SOC leads to higher discharge efficiency. Figure 6 As shown, in order to accurately characterize the charge and discharge efficiency characteristics of the battery, this study is based on Figure 6 The efficiency curves shown employ a stepped linearization method to process the battery's charge-discharge efficiency curves. By decomposing the complex nonlinear efficiency curves into several linear segments, this study constructs a linearized model for battery charge-discharge. This model effectively simplifies computational complexity while retaining the key characteristics of charge-discharge efficiency variations with state of charge (SOC), providing a precise mathematical description and theoretical support for subsequent optimized scheduling of energy storage systems. Figure 7 As shown, this invention employs a stepped linearization approach, simplifying the battery charge-discharge efficiency curve by establishing a one-to-one correspondence between the SOC interval and the charge-discharge efficiency. This transforms the complex nonlinear problem into a linear model for solution. Therefore, this invention constructs a linearized model for battery charge-discharge efficiency: (8) In the formula: η ba tc，i With η bat d，i Let μ be the charge / discharge efficiency of the battery in the i-th state of charge (SOC) interval. c t and μ d t These are 0-1 variables representing the charging and discharging states of the battery, respectively; vi is the signal representing the state of charge (SOC) range of the battery; P c i，t With P d i，t These represent the charging and discharging power of the battery within the corresponding range; a i With b i , respectively, are the left and right endpoints of the i-th soc interval; n is the total number of soc intervals. Since equation (8) contains the form of variable multiplication, the nonlinear element of equation (8) is further linearized according to MLD theory: (9) In the formula: v ai With v b i For intermediate 01 variables; Equation (9) will use variable v i The equivalent variables are treated as linear variables, which facilitates the solution of the model.

[0045] (10) In the formula: M and m are the maximum and minimum constants, respectively; Equation (10) uses the variable P c i，t The equivalent treatment is to treat it as a linear variable. Similarly, in the same way, this invention treats P as a linear variable. d i，t The variables are treated as linear. In summary, this invention constructs a linearized model for the charging and discharging efficiency of batteries.

[0046] 2.2 Efficiency Model of Flow Battery Vanadium redox flow batteries, as a novel type of electrochemical energy storage battery, have attracted widespread attention due to their advantages such as independent power and capacity, flexible design, fast response, good safety, and long lifespan. Unlike traditional battery technologies, the power rating and energy capacity of vanadium batteries are independent. The energy capacity is related to the electrolyte concentration and volume; the number of cells and the current density determine the power rating of the vanadium battery. Figure 8 The energy efficiency curves of flow batteries for different output energies are shown below. Figure 8 As shown, the energy efficiency of a flow battery is correlated with its output power. When the output power is below 3MW, the energy efficiency increases with increasing power; when the power is between 4 and 7MW, the energy efficiency tends to stabilize at around 85%; and when the power exceeds 7.5MW, the energy efficiency drops rapidly. To accurately characterize the charge-discharge efficiency characteristics of the flow battery, this study continues to use a stepped linearization method to process the charge-discharge efficiency curves of the flow battery, such as... Figure 9 As shown, this invention constructs a linearized model for the charge and discharge efficiency of a flow battery: (11) In the formula: η vrb c,i With η vrb d,i Let α be the charge / discharge efficiency of the flow battery in the i-th power range; c t and α d t , respectively, are 0-1 variables representing the charging and discharging states of the flow battery; βi is a signal representing the power range of the flow battery; W c i,t With W d i,t These represent the charge and discharge power of the flow battery within the corresponding range; Ai With B i Let be the left and right endpoints of the i-th power interval, respectively; N is the total number of power intervals. The nonlinear element of equation (11) is linearized as follows: (12) Where: β a i With β b i It is an intermediate 01 variable.

[0047] (13) In summary, this invention constructs a linearized model for the charge and discharge efficiency of flow batteries.

[0048] Compared to existing benchmark models for vanadium redox flow batteries based on constant current charge-discharge control, this model primarily focuses on the multi-valence characteristics of vanadium ions. It clarifies the electrochemical reactions and charge states of the positive and negative electrode pairs, stack voltage, internal losses, and other core parameters. An equivalent circuit model of an "ideal voltage source + linear components" is constructed, and mathematical relationships between parameters are established using equations such as the Nernst equation. Finally, it focuses on constant current charge-discharge characteristics, quantitatively calculates charge-discharge efficiency, and determines the optimal charge-discharge current range. This approach is commonly used in the design and optimization of large-scale new energy storage systems. The model defines coulombic efficiency as follows: (14) In the formula: Q1 and Q2 are the charge input and discharge capacity, respectively.

[0049] The charge / discharge efficiency is defined as follows: (15) In the formula: Ecell and Estack represent the input and output energy of the battery pack and the energy absorbed and released by the battery, respectively.

[0050] The construction idea of ​​this benchmark model is as follows: Figure 10 As shown, under the same background of this invention, this model demonstrates its ability to support system stability analysis by simulating the ability of energy storage batteries to smooth out fluctuations. Its characteristic is that it calculates the optimal charging current through macroscopic indicators. However, since it only covers constant current charging and discharging and does not consider the nonlinear effects of variables such as SOC and power on efficiency, this model suffers from low accuracy, a limited scope, and lacks consideration of multi-device collaboration, making it difficult to accurately address the uncertainties of power fluctuations during grid connection.

[0051] The main difference between this invention and others lies in the addition of signal processing and power allocation stages, covering diverse energy storage devices and considering various constraints. The main improvements are that it overcomes the limitation of the benchmark model focusing only on individual flow battery cells, abandons the constant current assumption of the benchmark model, and adopts the MLD linearization method to handle the nonlinear efficiency characteristics of energy storage devices. This makes it suitable for scenarios with frequent power fluctuations and varied operating conditions in new energy grid connection, and the modeling is more closely aligned with the actual operating environment. Furthermore, based on the benchmark model's "optimal efficiency," it adds the objective of "minimum grid connection fluctuation," achieving a balance between stability and economy by quantifying the fluctuation penalty cost and energy loss cost.

[0052] Technical Point 3: Optimized Output Model for Medium / High Frequency Energy Storage This invention, through step-by-step optimization of the original output power of a wind farm, and based on a medium / high frequency energy storage power frequency characteristic model, constructs a scenario-based stochastic optimization model for smoothing fluctuations using a hybrid medium / high frequency energy storage system, aiming to minimize grid connection fluctuations and maximize energy utilization. The framework of this invention is as follows: Figure 11 As shown, The objective function of the medium / high frequency hybrid energy storage fluctuation mitigation model of this invention is: (16) In the formula: Cun and Wun are the grid connection fluctuation penalty cost and fluctuation penalty coefficient, respectively; Pwi,t is the actual grid connection power of typical daily scenario i after optimization at time t; Let be the target grid-connected power of typical daily scenario i at time t; Closs be the energy loss cost; μloss be the energy loss penalty coefficient; and πi be the probability of occurrence of typical daily scenario i. Since the charging / discharging power of energy storage during actual operation is a fixed value, the energy storage charging / discharging power corresponding to all scenarios at any time is equal in the stochastic optimization control model. The constraints of this model are Equations (8) and (11) and the supercapacitor operation constraints.

[0053] The optimized model of this invention performs as follows: Figure 12 and Figure 13 As shown, in Figure 12 In this invention, the model effectively regulates the state of charge (SOC) of the battery. During the period when the battery's SOC is 48.3%, its SOC value is strictly maintained within the range of [0.45, 0.6]. Under this condition, the battery's charging and discharging efficiency remains above 80%, thus achieving high energy conversion efficiency during energy absorption and release, effectively improving energy utilization. Furthermore, during other time periods, the battery's SOC never exceeds 0.65, effectively avoiding the aggravated energy loss caused by excessively low discharge efficiency. Figure 13During the test, the charge and discharge power of the flow battery remained between [-8MW, 8MW] for 72.6% of the time period, thus effectively avoiding the high loss problem caused by the high power operation of the flow battery.

[0054] like Figure 14 As shown, under the control of the model constructed in this invention, the state of charge of the supercapacitor is stably maintained within the range of [0.2, 0.8] for 92.1% of the time period. This result indicates that the supercapacitor used in this invention has sufficient charge margin during operation and can provide reliable energy support for the energy storage system in case of emergencies, thereby significantly improving the reliability and stability of the energy storage system.

[0055] Based on the description and accompanying drawings of this invention, those skilled in the art can readily manufacture or use the method of multi-energy storage synergistically mitigating volatility according to this invention, and can produce the positive effects described in this invention.

[0056] It should be noted that the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover non-exclusive inclusion. The terms "installed," "set," "equipped with," "connected," "linked," and "sleeve" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral construction; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium, or an internal connection between two mechanisms, elements, or components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] In the description of this invention, it should be understood that the terms "one end," "the other end," "outer side," "inner side," "horizontal," "end," "length," "outer end," "left," and "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the mechanism or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. The terms "first" and "second" are also used only for the sake of brevity in description and do not indicate or imply relative importance.

[0058] Furthermore, in practicing the claims of this invention, those skilled in the art can understand and influence variations to the disclosed embodiments through a study of the drawings, the disclosure, and the appended claims. Additionally, in the claims and description, words such as "comprising" and "containing" do not exclude other elements or steps, and non-plural nouns do not exclude their plural forms.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes and modifications made in accordance with the present invention are covered by the scope of the claims of the present invention, and will not be listed here.

Claims

1. A method for synergistically mitigating volatility through multi-element energy storage, characterized in that, Includes the following steps: Step 1: Perform empirical mode decomposition on the original grid-connected power signal of new energy sources to obtain multiple IMF components; Step 2: Based on the grid-connected power and the amplitude-frequency response capability of each energy storage device, the IMF components are reconstructed to divide them into low-frequency components, mid-frequency components, and high-frequency components corresponding to different time scales. Step 3: Establish a linearized model for the charging and discharging efficiency of each energy storage device. Based on MLD theory, perform linearization modeling and handle nonlinear problems through piecewise linearization. Step 4: With the goal of minimizing grid connection fluctuations and maximizing energy utilization, construct a stochastic optimization model for multi-energy storage to collaboratively mitigate fluctuations and optimize the output strategies of each energy storage device.

2. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 1, characterized in that, The steps of the empirical mode decomposition are as follows: A. Determine all local maxima and local minima of the signal x(t); B. Constructing the upper and lower envelopes: The upper envelope of the signal, eupper(t), is obtained by interpolating all local maxima; similarly, the lower envelope, elower(t), is obtained by interpolating all local minima. This invention uses cubic spline interpolation to smoothly connect these extreme points. C. Calculate the mean line: Calculate the mean of the upper and lower envelope lines to obtain the mean line m(t) reflecting the low-frequency trend: （1）； D. Extracting detail components: Subtracting the mean from the original signal yields a new signal h(t): （2）； E. Determine the IMF condition: Check if h(t) satisfies the definition of IMF: In the entire data segment, the number of extreme points and the number of zero crossings must be equal or differ by at most one. At any time, the average of the upper envelope formed by the local maxima and the lower envelope formed by the local minima is zero. If h(t) satisfies the IMF condition, then h(t) is an IMF; otherwise, treat h(t) as a new signal and repeat the above steps. F. Iterative decomposition: The extracted IMF is removed from the original signal to obtain the residual signal. （3）； Repeat the above steps for the residual signal until the residual signal becomes a monotonic function or meets the termination condition. G. Termination conditions: The termination conditions of the screening process usually include: stopping when the detail component h(t) meets the IMF condition; stopping when the mean m(t) is sufficiently small; Through the above steps, the original new energy grid-connected power signal is decomposed into several IMF components and a residual term.

3. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 1, characterized in that, In step two, when classifying low-frequency components, C2F reconfiguration components with large amplitude, small frequency, and fluctuations that meet grid connection requirements are prioritized as low-frequency components. At the same time, it is necessary to ensure that the overall trend of medium / high frequency components within the scheduling cycle meets the capacity constraints of medium / high frequency energy storage devices, so as to avoid energy storage operating in a low / high charge state for a long time.

4. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 3, characterized in that, When dividing the low-frequency components, equations (4) and (5) should be satisfied, with equation (4) having higher priority than equation (5): （4） （5） In the formula: △(x) is the maximum fluctuation of x within 10 minutes; The limit for grid connection fluctuations is set at 5% of the installed wind power capacity. This represents the margin factor for the hybrid energy storage capacity. This refers to the overall capacity of the hybrid energy storage system.

5. The method for synergistically mitigating volatility using multiple energy storage systems as described in claim 1, characterized in that, In step two, when dividing the medium / high frequency components, the F2C reconstruction components with high frequency, small amplitude, and periodic oscillation are preferentially classified as high frequency components. At the same time, it is necessary to ensure that the fluctuation amplitude of the medium / high frequency components meets the real-time response capability of the corresponding energy storage devices, including batteries, flow batteries, and supercapacitors.

6. The method for synergistically mitigating volatility using multiple energy storage systems as described in claim 5, characterized in that, When dividing the mid / high frequency components, equations (6) and (7) should be satisfied, and equation (6) has higher priority than equation (7): （6） （7） In the formula: Kbat-H and Ksc are the power margin coefficients of the battery-flow battery and the supercapacitor, respectively; Pbat-Hmax and Pscmax are the maximum power of the battery-flow battery and the supercapacitor, respectively.

7. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 1, characterized in that, In step three, the linearization models for the charge and discharge efficiency of each energy storage device include the linearization models for the charge and discharge efficiency of batteries and the linearization models for the charge and discharge efficiency of flow batteries.

8. The method for synergistically mitigating volatility using multiple energy storage systems as described in claim 7, characterized in that, Construct a linearized model for the charge and discharge efficiency of the battery: （8） In the formula: η bat c，i With η bat d，i Let μ be the charge / discharge efficiency of the battery in the i-th state of charge (SOC) interval. c t and μ d t These are 0-1 variables representing the charging and discharging states of the battery, respectively; v i The signal P represents the state of charge (SOC) range of the battery. c i，t With P d i，t These represent the charging and discharging power of the battery within the corresponding range; a i With b i , respectively, are the left and right endpoints of the i-th soc interval; n is the total number of soc intervals. Since equation (8) contains the form of variable multiplication, the nonlinear element of equation (8) is further linearized according to MLD theory: （9） In the formula: v a i With v b i For intermediate 01 variables; Equation (9) will use variable v i The equivalent variables are treated as linear variables, which facilitates model solving; （10） In the formula: M and m are the maximum and minimum constants, respectively; Equation (10) uses the variable P c i，t Equivalently treated as a linear variable, similarly, in the same way, this invention treats P as a linear variable. d i，t It is equivalent to a linear variable.

9. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 7, characterized in that, Constructing a linearized model for the charge-discharge efficiency of a flow battery: （11） In the formula: η vrb c,i With η vrb d,i Let α be the charge / discharge efficiency of the flow battery in the i-th power range; c t and α d t β are 0-1 variables representing the charging and discharging states of the flow battery, respectively; i The signal representing the power range of the flow battery; W c i,t With W d i,t These represent the charge and discharge power of the flow battery within the corresponding range; A i With B i Let be the left and right endpoints of the i-th power interval, respectively; N is the total number of power intervals. The nonlinear element of equation (11) is linearized as follows: （12） In the formula: βai and βbi are intermediate 01 variables; （13）。 10. The method for synergistically mitigating volatility through multi-element energy storage as described in claim 1, characterized in that, The objective function of the medium / high frequency hybrid energy storage fluctuation mitigation model is: （16） In the formula: C un and W un These are the grid connection fluctuation penalty cost and the fluctuation penalty coefficient, respectively; P w i,t This represents the optimized actual grid-connected power at time t for a typical daily scenario; C represents the target grid-connected power at time t in a typical daily scenario; loss Cost of energy loss; μ loss π is the energy loss penalty coefficient. i Let i be the probability of occurrence of a typical day scenario. Since the charging / discharging power of the energy storage is a fixed value during actual operation, the charging / discharging power of the energy storage in all scenarios is equal at any time in the stochastic optimization control model. The constraints of this model are Equations (8) and (11) and the supercapacitor operation constraints.

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