Hybrid energy storage capacity optimization configuration method

Abstract

The invention provides a hybrid energy storage capacity optimal configuration method, and relates to the technical field of hybrid energy storage, and the method comprises the steps: building a hybrid energy storage system, obtaining the unbalanced power of the hybrid energy storage system, and determining a preset unbalanced power distribution strategy of the hybrid energy storage system; carrying out power distribution on the unbalanced power according to a preset unbalanced power distribution strategy, obtaining the total reference power of the hybrid energy storage system, carrying out output calibration on the actual output power, and determining the rated power and capacity configuration of the hybrid energy storage system; verifying the validity of the power distribution of the hybrid energy storage system according to the actual operation data of the wind power plant; according to the method, reasonable distribution of the total reference power of the hybrid energy storage system is achieved, the mode component aliasing phenomenon is improved, meanwhile, advantage complementation of multiple types of energy storage characteristics is achieved through the hybrid energy storage system, and the requirements of a power grid for multiple performances are met.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid energy storage, and in particular to a hybrid energy storage capacity optimization configuration method. Background Art

[0002] As the proportion of wind power generation in my country's power system increases year by year, the strong randomness of wind power output has an increasingly greater impact on the power grid; how to reduce the volatility of wind power output and reduce the wind abandonment rate has become one of the current research hotspots; the hybrid energy storage system has the advantages of both energy-type energy storage and power-type energy storage, which can more effectively smooth out wind power fluctuations, and to achieve complementary advantages of the hybrid energy storage system, it is necessary to reasonably allocate the reference power of the hybrid energy storage system, and the current allocation method has defects such as modal aliasing.

[0003] Therefore, the present invention provides a hybrid energy storage capacity optimization configuration method. Summary of the invention

[0004] The present invention provides a hybrid energy storage capacity optimization configuration method, which determines a preset unbalanced power allocation strategy of the hybrid energy storage system by constructing a hybrid energy storage system and obtaining the unbalanced power of the hybrid energy storage system; distributes the unbalanced power according to the preset unbalanced power allocation strategy, obtains the total reference power of the hybrid energy storage system, performs output calibration on the actual output power, and determines the rated power and capacity configuration of the hybrid energy storage system; verifies the effectiveness of the power allocation of the hybrid energy storage system according to the actual operation data of the wind farm; realizes the rational allocation of the total reference power of the hybrid energy storage system, improves the aliasing phenomenon of modal components, and at the same time, realizes the complementary advantages of multiple types of energy storage characteristics through the hybrid energy storage system, and meets the requirements of the power grid for multiple performances.

[0005] The present invention provides a hybrid energy storage capacity optimization configuration method, comprising: Step 1: Establish a hybrid energy storage system and obtain the unbalanced power of the hybrid energy storage system, and determine a preset unbalanced power allocation strategy for the hybrid energy storage system; Step 2: Allocate the unbalanced power according to the preset unbalanced power allocation strategy, obtain the total reference power of the hybrid energy storage system, calibrate the actual output power, and determine the rated power and capacity configuration of the hybrid energy storage system; Step 3: Verify the effectiveness of the power distribution of the hybrid energy storage system based on the actual operation data of the wind farm.

[0006] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, a hybrid energy storage system is established and the unbalanced power of the hybrid energy storage system is obtained, and a preset unbalanced power allocation strategy of the hybrid energy storage system is determined, including: Build a hybrid energy storage system based on supercapacitors and lithium batteries; Obtain the actual output power and planned output power of the wind farm and determine the unbalanced power of the hybrid energy storage system; According to the actual output power of the wind farm and the planned output power of the wind farm, a preset unbalanced power allocation strategy of the hybrid energy storage system is determined.

[0007] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, a preset unbalanced power allocation strategy of the hybrid energy storage system is determined according to the actual output power of the wind farm and the planned output power of the wind farm, including: Obtaining actual output characteristics of the actual output power of the wind farm and planned output characteristics of the planned output power of the wind farm, wherein the actual output characteristics include: volatility and uncertainty, and the planned output characteristics include: stability; According to the equipment characteristics of supercapacitors and lithium batteries, combined with the actual output characteristics and planned output characteristics, the preset unbalanced power distribution strategy of the hybrid energy storage system is determined.

[0008] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, after determining a preset unbalanced power allocation strategy for the hybrid energy storage system, the method further includes: Initialize parameter data of the characteristic mode decomposition algorithm and the frost and ice optimization algorithm in the preset unbalanced power allocation strategy of the hybrid energy storage system; According to the frost and ice optimization algorithm that has been initialized, the modal decomposition times and the secondary penalty factor in the eigenmode decomposition algorithm that has been initialized are optimized to obtain an unbalanced power decomposition strategy based on the frost and ice optimization algorithm to optimize the eigenmode decomposition algorithm.

[0009] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, the unbalanced power is distributed according to a preset unbalanced power distribution strategy of the hybrid energy storage system, the total reference power of the hybrid energy storage system is obtained and the actual output power is calibrated, and the rated power and capacity configuration of the hybrid energy storage system are determined, including: Allocate the unbalanced power according to the preset unbalanced power allocation strategy of the hybrid energy storage system, and record the unbalanced power of the hybrid energy storage system as the total reference power of the hybrid energy storage system; According to the unbalanced power decomposition strategy based on the frost optimization algorithm to optimize the characteristic mode decomposition algorithm, the total reference power of the hybrid energy storage system is decomposed to obtain a series of modal sub-components with different frequencies; Perform Hilbert transform on the modal subcomponents to obtain the distribution characteristics of each modal subcomponent in the frequency domain marginal spectrum, determine the boundary between high-frequency and low-frequency fluctuation subcomponents based on the principle of minimum aliasing of modal components, and obtain the power low-frequency fluctuation subcomponent and the power high-frequency fluctuation subcomponent; The power low-frequency fluctuation sub-component is recorded as the lithium battery sub-reference power of the lithium battery in the hybrid energy storage system; The power high frequency fluctuation subcomponent is recorded as the supercapacitor subreference power of the supercapacitor in the hybrid energy storage system; The actual output power is calibrated according to the supercapacitor sub-reference power and the lithium battery sub-reference power; Obtain the discharge power and charging power of the lithium battery. When the power low-frequency fluctuation sub-component is higher than 0, the lithium battery is determined to be in a discharge state. The lithium battery sub-reference power is output calibrated according to the charging power of the lithium battery to determine the actual output power of the lithium battery. When the power low-frequency fluctuation sub-component is lower than 0, it is determined that the lithium battery is in a charging state, and the lithium battery sub-reference power is output calibrated according to the discharge power of the lithium battery to determine the actual output power of the lithium battery; Obtain the discharge power and charging power of the supercapacitor. When the high-frequency fluctuation subcomponent of the power is lower than 0, the supercapacitor is determined to be in a discharge state. The supercapacitor sub-reference power is output calibrated according to the discharge efficiency of the supercapacitor to determine the actual output power of the supercapacitor. When the power high-frequency fluctuation sub-component is higher than 0, it is determined that the supercapacitor is in a charging state, and the supercapacitor sub-reference power is output calibrated according to the charging efficiency of the supercapacitor to determine the actual output power of the supercapacitor; The rated power and capacity configuration of the hybrid energy storage system are determined according to the actual output power of the lithium battery and the actual output power of the supercapacitor.

[0010] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, the rated power and capacity configuration of the hybrid energy storage system are determined according to the actual output power of the lithium battery and the actual output power of the supercapacitor, including: Obtain a preset sampling period, and determine the rated power of the hybrid energy storage system according to the actual charging and discharging power demand of the hybrid energy storage system, wherein the rated power of the hybrid energy storage system includes: the rated power of the lithium battery and the rated power of the supercapacitor; ;in, Indicates the rated power of the lithium battery; Represents the low-frequency fluctuation subcomponent of power; Indicates the discharge efficiency of lithium batteries; Indicates the charging power of lithium battery; Indicates the rated power of the supercapacitor; Represents the high-frequency fluctuation subcomponent of power; Indicates the supercapacitor discharge efficiency; Indicates the supercapacitor charging efficiency; Determine the energy change of the lithium battery and the energy change of the supercapacitor according to the rated power of the hybrid energy storage system; The maximum discharge depth of the hybrid energy storage system is set, and the capacity configuration of the hybrid energy storage system is determined in combination with the energy change of the lithium battery and the energy change of the supercapacitor, wherein the capacity configuration of the hybrid energy storage system includes: supercapacitor capacity configuration and lithium battery capacity configuration; ;in, Indicates the supercapacitor capacity configuration; Indicates the maximum value of supercapacitor energy change; Indicates the minimum value of supercapacitor energy change; Indicates the supercapacitor discharge depth; Indicates the lithium battery capacity configuration; Indicates the maximum value of lithium battery energy change; Indicates the minimum value of lithium battery energy change; Indicates the discharge depth of lithium battery.

[0011] A hybrid energy storage capacity optimization configuration method provided by the present invention also includes: Set capacity configuration constraints for the hybrid energy storage system.

[0012] According to a hybrid energy storage capacity optimization configuration method provided by the present invention, the effectiveness of the power distribution of the hybrid energy storage system is verified according to the actual operation data of the wind farm, including: Obtain the actual operation data of the wind farm in the preset sampling period, determine the predicted power curve, measured power curve and unbalanced power curve of the wind farm, and verify the effectiveness of the power distribution of the hybrid energy storage system; According to the unbalanced power decomposition strategy of optimizing the characteristic mode decomposition algorithm based on the frost optimization algorithm, the unbalanced power in the unbalanced power curve is decomposed to obtain the modal decomposition result. When the distribution of each component curve in the modal decomposition result is clear, it is effective for the power allocation of the hybrid energy storage system.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By building a hybrid energy storage system and obtaining the unbalanced power of the hybrid energy storage system, the preset unbalanced power allocation strategy of the hybrid energy storage system is determined; the unbalanced power is allocated according to the preset unbalanced power allocation strategy, the total reference power of the hybrid energy storage system is obtained, and the actual output power is calibrated to determine the rated power and capacity configuration of the hybrid energy storage system; the power allocation of the hybrid energy storage system is verified for validity according to the actual operation data of the wind farm; the rational allocation of the total reference power of the hybrid energy storage system is achieved, the aliasing phenomenon of the modal components is improved, and at the same time, the hybrid energy storage system realizes the complementary advantages of multiple types of energy storage characteristics and meets the requirements of the power grid for multiple performances.

[0014] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a hybrid energy storage capacity optimization configuration method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, such as Figure 1 As shown, including: Step 1: Establish a hybrid energy storage system and obtain the unbalanced power of the hybrid energy storage system, and determine a preset unbalanced power allocation strategy for the hybrid energy storage system; Step 2: Allocate the unbalanced power according to the preset unbalanced power allocation strategy, obtain the total reference power of the hybrid energy storage system, calibrate the actual output power, and determine the rated power and capacity configuration of the hybrid energy storage system; Step 3: Verify the effectiveness of the power distribution of the hybrid energy storage system based on the actual operation data of the wind farm.

[0019] In this embodiment, the hybrid energy storage system refers to an energy storage system composed of supercapacitors and lithium batteries, which can achieve fast, high capacity, high endurance and fast, long-lasting and stable energy storage.

[0020] In this embodiment, the unbalanced power refers to the difference power between the actual output power of the wind farm and the planned output power of the wind farm.

[0021] In this embodiment, the preset unbalanced power allocation strategy refers to dividing the unbalanced power into high-frequency and low-frequency parts, compensating and smoothing the high-frequency fluctuation component of the total reference power of the hybrid energy storage by supercapacitors, and smoothing the low-frequency fluctuation component by lithium batteries.

[0022] In this embodiment, power distribution refers to an operation of decomposing unbalanced power into high-frequency power and low-frequency power.

[0023] In this embodiment, output calibration refers to calibrating the actual output power according to the charge and discharge efficiency and the reference power.

[0024] In this embodiment, the rated power refers to the maximum output power that the hybrid energy storage system can achieve when operating stably.

[0025] In this embodiment, the capacity configuration refers to the configuration of the rated capacity of the hybrid energy storage system, and the rated capacity refers to the maximum storage capacity that can be achieved by the hybrid energy storage system in stable operation.

[0026] In this embodiment, the actual operation data of the wind farm refers to the output power data of the wind turbines during the actual operation.

[0027] In this embodiment, the validity check refers to the check of the power distribution of the hybrid energy storage system according to the actual operation data of the wind farm, which is used to detect whether the preset unbalanced power distribution strategy is effective.

[0028] The working principle and beneficial effects of the above technical solution are: by forming a hybrid energy storage system and obtaining the unbalanced power of the hybrid energy storage system, the preset unbalanced power allocation strategy of the hybrid energy storage system is determined; the unbalanced power is allocated according to the preset unbalanced power allocation strategy, the total reference power of the hybrid energy storage system is obtained and the actual output power is calibrated to determine the rated power and capacity configuration of the hybrid energy storage system; the power allocation of the hybrid energy storage system is verified for validity according to the actual operation data of the wind farm; the rational allocation of the total reference power of the hybrid energy storage system is achieved, the aliasing phenomenon of the modal components is improved, and at the same time, the complementary advantages of multiple types of energy storage characteristics are achieved through the hybrid energy storage system, meeting the requirements of the power grid for multiple performances.

[0029] Embodiment 2: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which constructs a hybrid energy storage system and obtains the unbalanced power of the hybrid energy storage system, and determines a preset unbalanced power allocation strategy of the hybrid energy storage system, including: Build a hybrid energy storage system based on supercapacitors and lithium batteries; Obtain the actual output power and planned output power of the wind farm and determine the unbalanced power of the hybrid energy storage system; According to the actual output power of the wind farm and the planned output power of the wind farm, a preset unbalanced power allocation strategy of the hybrid energy storage system is determined.

[0030] In this embodiment, both supercapacitors and lithium batteries are energy storage devices. Supercapacitors have the characteristics of fast charging and discharging speed and long service life, and can be used to smooth out high-frequency fluctuating power on a shorter time scale. Lithium batteries have the characteristics of large capacity and high energy density, and are suitable for smoothing out low-frequency fluctuating power on a longer time scale.

[0031] In this embodiment, the hybrid energy storage system refers to an energy storage system composed of supercapacitors and lithium batteries, which can achieve fast, high capacity, high endurance and fast, long-lasting and stable energy storage.

[0032] In this embodiment, the actual output power of the wind farm refers to the output power of the wind turbines; the planned output power of the wind farm refers to the output power of the wind turbines that is planned to be connected to the power grid.

[0033] In this embodiment, the actual output power of the wind farm is volatile and uncertain; the planned output power of the wind farm needs to be stable.

[0034] In this embodiment, the unbalanced power refers to the difference power between the actual output power of the wind farm and the planned output power of the wind farm.

[0035] In this embodiment, the preset unbalanced power allocation strategy refers to a strategy for dividing the unbalanced power into high-frequency and low-frequency parts, compensating and smoothing the high-frequency fluctuation component of the total reference power of the hybrid energy storage through supercapacitors, and smoothing the low-frequency fluctuation component through lithium batteries, and then incorporating the smoothed electric energy into the power grid, thereby achieving the purpose of "peak shaving and valley filling" and "promoting balanced consumption of wind power".

[0036] The working principle and beneficial effects of the above technical solution are: by forming a hybrid energy storage system, it is beneficial to realize the complementary advantages of multiple types of energy storage characteristics and meet the power grid's requirements for multiple performances; determine the preset unbalanced power distribution strategy of the hybrid energy storage system, which lays a strategic foundation for subsequent power distribution.

[0037] Embodiment 3: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which determines a preset unbalanced power allocation strategy of a hybrid energy storage system according to the actual output power of a wind farm and the planned output power of a wind farm, including: Obtaining actual output characteristics of the actual output power of the wind farm and planned output characteristics of the planned output power of the wind farm, wherein the actual output characteristics include: volatility and uncertainty, and the planned output characteristics include: stability; According to the equipment characteristics of supercapacitors and lithium batteries, combined with the actual output characteristics and planned output characteristics, the preset unbalanced power distribution strategy of the hybrid energy storage system is determined.

[0038] In this embodiment, the device characteristics of the supercapacitor are fast charging and discharging speed and long service life; the device characteristics of the lithium battery are large capacity and high energy density.

[0039] In this embodiment, the preset unbalanced power distribution strategy of the hybrid energy storage system is determined based on the equipment characteristics of the supercapacitor and the lithium battery, combined with the actual output characteristics and the planned output characteristics, that is, the high-frequency fluctuation component of the total reference power of the hybrid energy storage is compensated and smoothed by the supercapacitor and the low-frequency fluctuation component is smoothed by the lithium battery.

[0040] The working principle and beneficial effect of the above technical solution are: according to the actual output power of the wind farm and the planned output power of the wind farm, the preset unbalanced power allocation strategy of the hybrid energy storage system is determined, which lays a strategic foundation for subsequent power allocation.

[0041] Embodiment 4: An embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which, after determining a preset unbalanced power allocation strategy for a hybrid energy storage system, further includes: Initialize parameter data of the characteristic mode decomposition algorithm and the frost and ice optimization algorithm in the preset unbalanced power allocation strategy of the hybrid energy storage system; According to the frost and ice optimization algorithm that has been initialized, the modal decomposition times and the secondary penalty factor in the eigenmode decomposition algorithm that has been initialized are optimized to obtain an unbalanced power decomposition strategy based on the frost and ice optimization algorithm to optimize the eigenmode decomposition algorithm.

[0042] In this embodiment, the characteristic mode decomposition algorithm is a non-recursive decomposition algorithm, which updates the filter coefficients by initializing the finite impulse response filter combination to achieve adaptive selection of different modes.

[0043] In this embodiment, the frost optimization algorithm is an optimization algorithm for optimizing the modal decomposition times and the quadratic penalty factor in the eigenmode decomposition algorithm, thereby improving the modal decomposition effect of the eigenmode decomposition algorithm.

[0044] In this embodiment, the parameter data, for example, the modal decomposition times and the quadratic penalty factor of the characteristic mode decomposition algorithm.

[0045] The working principle and beneficial effect of the above technical solution are: by initializing parameter data and obtaining an unbalanced power decomposition strategy based on the frost optimization algorithm to optimize the characteristic mode decomposition algorithm, it is beneficial to subsequently realize the reasonable distribution of the total reference power of the hybrid energy storage system, thereby improving the aliasing phenomenon of modal components.

[0046] Embodiment 5: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which distributes unbalanced power according to a preset unbalanced power allocation strategy of the hybrid energy storage system, obtains the total reference power of the hybrid energy storage system and performs output calibration on the actual output power, and determines the rated power and capacity configuration of the hybrid energy storage system, including: Allocate the unbalanced power according to the preset unbalanced power allocation strategy of the hybrid energy storage system, and record the unbalanced power of the hybrid energy storage system as the total reference power of the hybrid energy storage system; According to the unbalanced power decomposition strategy based on the frost optimization algorithm to optimize the characteristic mode decomposition algorithm, the total reference power of the hybrid energy storage system is decomposed to obtain a series of modal sub-components with different frequencies; Perform Hilbert transform on the modal subcomponents to obtain the distribution characteristics of each modal subcomponent in the frequency domain marginal spectrum, determine the boundary between high-frequency and low-frequency fluctuation subcomponents based on the principle of minimum aliasing of modal components, and obtain the power low-frequency fluctuation subcomponent and the power high-frequency fluctuation subcomponent; The power low-frequency fluctuation sub-component is recorded as the lithium battery sub-reference power of the lithium battery in the hybrid energy storage system; The power high frequency fluctuation subcomponent is recorded as the supercapacitor subreference power of the supercapacitor in the hybrid energy storage system; The actual output power is calibrated according to the supercapacitor sub-reference power and the lithium battery sub-reference power; Obtain the discharge power and charging power of the lithium battery. When the power low-frequency fluctuation sub-component is higher than 0, the lithium battery is determined to be in a discharge state. The lithium battery sub-reference power is output calibrated according to the charging power of the lithium battery to determine the actual output power of the lithium battery. When the power low-frequency fluctuation sub-component is lower than 0, it is determined that the lithium battery is in a charging state, and the lithium battery sub-reference power is output calibrated according to the discharge power of the lithium battery to determine the actual output power of the lithium battery; Obtain the discharge power and charging power of the supercapacitor. When the high-frequency fluctuation subcomponent of the power is lower than 0, the supercapacitor is determined to be in a discharge state. The supercapacitor sub-reference power is output calibrated according to the discharge efficiency of the supercapacitor to determine the actual output power of the supercapacitor. When the power high-frequency fluctuation sub-component is higher than 0, it is determined that the supercapacitor is in a charging state, and the supercapacitor sub-reference power is output calibrated according to the charging efficiency of the supercapacitor to determine the actual output power of the supercapacitor; The rated power and capacity configuration of the hybrid energy storage system are determined according to the actual output power of the lithium battery and the actual output power of the supercapacitor.

[0047] In this embodiment, the total reference power of the hybrid energy storage system refers to the reference power used for power distribution later.

[0048] In this embodiment, power distribution refers to an operation of decomposing unbalanced power into high-frequency power and low-frequency power.

[0049] In this embodiment, the modal sub-component refers to a component obtained by decomposing the total reference power of the hybrid energy storage system according to an unbalanced power decomposition strategy based on the frost and ice optimization algorithm to optimize the characteristic modal decomposition algorithm.

[0050] In this embodiment, the frequency domain marginal spectrum refers to the energy distribution used to reflect the power at different frequencies.

[0051] In this embodiment, the high-frequency and low-frequency fluctuation sub-component dividing line refers to the dividing line that divides the high-frequency fluctuation sub-component and the low-frequency fluctuation sub-component.

[0052] In this embodiment, the power low-frequency fluctuation subcomponent refers to a modal subcomponent whose frequency is lower than the boundary between the high-frequency and low-frequency fluctuation subcomponents; the power high-frequency fluctuation subcomponent refers to a modal subcomponent whose frequency is not lower than the boundary between the high-frequency and low-frequency fluctuation subcomponents.

[0053] In this embodiment, the lithium battery sub-reference power refers to a reference power determined according to the power low-frequency fluctuation sub-component and used for subsequently determining the rated power and rated capacity of the lithium battery.

[0054] In this embodiment, the supercapacitor sub-reference power refers to a reference power determined according to the power high-frequency fluctuation sub-component and used for subsequently determining the rated power and rated capacity of the supercapacitor.

[0055] In this embodiment, output calibration refers to calibrating the actual output power according to the charging and discharging efficiency and the reference power, so as to take into account the problem of power loss in the energy storage process and thereby ensure the accuracy of the output power.

[0056] The working principle and beneficial effects of the above technical solution are: to distribute the unbalanced power according to the preset unbalanced power distribution strategy of the hybrid energy storage system, to obtain the total reference power of the hybrid energy storage system and to calibrate the actual output power, to determine the rated power and capacity configuration of the hybrid energy storage system, and to achieve the reasonable distribution of the total reference power of the hybrid energy storage system.

[0057] Embodiment 6: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which determines the rated power and capacity configuration of the hybrid energy storage system according to the actual output power of the lithium battery and the actual output power of the supercapacitor, including: Obtain a preset sampling period, and determine the rated power of the hybrid energy storage system according to the actual charging and discharging power demand of the hybrid energy storage system, wherein the rated power of the hybrid energy storage system includes: the rated power of the lithium battery and the rated power of the supercapacitor; ;in, Indicates the rated power of the lithium battery; Represents the low-frequency fluctuation subcomponent of power; Indicates the discharge efficiency of lithium batteries; Indicates the charging power of lithium battery; Indicates the rated power of the supercapacitor; Represents the high-frequency fluctuation subcomponent of power; Indicates the supercapacitor discharge efficiency; Indicates the supercapacitor charging efficiency; Determine the energy change of the lithium battery and the energy change of the supercapacitor according to the rated power of the hybrid energy storage system; The maximum discharge depth of the hybrid energy storage system is set, and the capacity configuration of the hybrid energy storage system is determined in combination with the energy change of the lithium battery and the energy change of the supercapacitor, wherein the capacity configuration of the hybrid energy storage system includes: supercapacitor capacity configuration and lithium battery capacity configuration; ;in, Indicates the supercapacitor capacity configuration; Indicates the maximum value of supercapacitor energy change; Indicates the minimum value of supercapacitor energy change; Indicates the supercapacitor discharge depth; Indicates the lithium battery capacity configuration; Indicates the maximum value of lithium battery energy change; Indicates the minimum value of lithium battery energy change; Indicates the discharge depth of lithium battery.

[0058] In this embodiment, the preset sampling period refers to the period for sampling the output power of the hybrid energy storage system, for example, the output power is obtained once every 15 minutes.

[0059] In this embodiment, the actual charging and discharging power demand refers to the power demand for charging and discharging of the hybrid energy storage system.

[0060] In this embodiment, the rated power refers to the maximum output power that the hybrid energy storage system can achieve when operating stably.

[0061] In this embodiment, the energy change of the lithium battery and the energy change of the supercapacitor are determined according to the rated power of the hybrid energy storage system, that is, the energy change is determined according to the sum of the rated power changes within a time period.

[0062] In this embodiment, the capacity configuration refers to the configuration of the rated capacity of the hybrid energy storage system, and the rated capacity refers to the maximum storage capacity that can be achieved by the hybrid energy storage system in stable operation.

[0063] In this embodiment, the discharge depth refers to the percentage of the discharge amount of the hybrid energy storage system to the rated capacity of the hybrid energy storage system.

[0064] The working principle and beneficial effect of the above technical solution are: determining the rated power and capacity configuration of the hybrid energy storage system according to the actual output power of the lithium battery and the actual output power of the supercapacitor, thereby achieving a reasonable allocation of the total reference power of the hybrid energy storage system.

[0065] Embodiment 7: An embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which also includes: Set capacity configuration constraints for the hybrid energy storage system.

[0066] In this embodiment, the capacity configuration constraint refers to the constraint condition on the capacity configuration of the hybrid energy storage system. For example, to ensure the stable operation of the hybrid energy storage system, the maximum energy value and the minimum energy value of the supercapacitor are set to 0.9 and 0.1 of the rated capacity of the supercapacitor; the maximum energy value and the minimum energy value of the lithium battery are set to 0.85 and 0.15 of the rated capacity of the lithium battery.

[0067] The working principle and beneficial effect of the above technical solution are: by setting the capacity configuration constraints of the hybrid energy storage system, it is helpful to ensure the stable operation of the hybrid energy storage system.

[0068] Embodiment 8: The embodiment of the present invention provides a hybrid energy storage capacity optimization configuration method, which verifies the effectiveness of power distribution of a hybrid energy storage system according to actual operation data of a wind farm, including: Obtain the actual operation data of the wind farm in the preset sampling period, determine the predicted power curve, measured power curve and unbalanced power curve of the wind farm, and verify the effectiveness of the power distribution of the hybrid energy storage system; According to the unbalanced power decomposition strategy of optimizing the characteristic mode decomposition algorithm based on the frost optimization algorithm, the unbalanced power in the unbalanced power curve is decomposed to obtain the modal decomposition result. When the distribution of each component curve in the modal decomposition result is clear, it is effective for the power allocation of the hybrid energy storage system.

[0069] In this embodiment, actual operation data of the wind farm in a preset sampling period is obtained, for example, actual operation data of the wind farm in the next week is obtained, and the preset sampling period is 15 minutes.

[0070] In this embodiment, the actual operation data of the wind farm refers to the output power data of the wind turbines during the actual operation.

[0071] In this embodiment, the predicted power curve, the measured power curve and the unbalanced power curve refer to curves drawn according to actual operation data of the wind farm, and are used to analyze the effectiveness of power allocation of the hybrid energy storage system.

[0072] In this embodiment, the modal decomposition result refers to the frequency domain marginal spectrum obtained by performing Hilbert transform on the decomposed modal sub-components. When the modal component curves in the frequency domain marginal spectrum are clearly distributed, it is determined that the power allocation of the hybrid energy storage system is effective.

[0073] The working principle and beneficial effect of the above technical solution are: the effectiveness of the power distribution of the hybrid energy storage system is verified according to the actual operation data of the wind farm, which further ensures the rational distribution of the total reference power of the hybrid energy storage system.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid energy storage capacity optimization configuration method, characterized in that: include: Step 1: Establish a hybrid energy storage system and obtain the unbalanced power of the hybrid energy storage system, and determine a preset unbalanced power allocation strategy for the hybrid energy storage system; Step 2: Allocate the unbalanced power according to the preset unbalanced power allocation strategy, obtain the total reference power of the hybrid energy storage system, calibrate the actual output power, and determine the rated power and capacity configuration of the hybrid energy storage system; Step 3: Verify the effectiveness of the power distribution of the hybrid energy storage system based on the actual operation data of the wind farm.

2. A hybrid energy storage capacity optimization configuration method according to claim 1, characterized in that: Establish a hybrid energy storage system and obtain the unbalanced power of the hybrid energy storage system, and determine the preset unbalanced power allocation strategy of the hybrid energy storage system, including: Build a hybrid energy storage system based on supercapacitors and lithium batteries; Obtain the actual output power and planned output power of the wind farm and determine the unbalanced power of the hybrid energy storage system; According to the actual output power of the wind farm and the planned output power of the wind farm, a preset unbalanced power allocation strategy of the hybrid energy storage system is determined.

3. A hybrid energy storage capacity optimization configuration method according to claim 2, characterized in that: According to the actual output power of the wind farm and the planned output power of the wind farm, the preset unbalanced power allocation strategy of the hybrid energy storage system is determined, including: Obtaining actual output characteristics of the actual output power of the wind farm and planned output characteristics of the planned output power of the wind farm, wherein the actual output characteristics include: volatility and uncertainty, and the planned output characteristics include: stability; According to the equipment characteristics of supercapacitors and lithium batteries, combined with the actual output characteristics and planned output characteristics, the preset unbalanced power distribution strategy of the hybrid energy storage system is determined.

4. A hybrid energy storage capacity optimization configuration method according to claim 1, characterized in that: After determining the preset unbalanced power distribution strategy of the hybrid energy storage system, it also includes: Initialize parameter data of the characteristic mode decomposition algorithm and the frost and ice optimization algorithm in the preset unbalanced power allocation strategy of the hybrid energy storage system; According to the frost and ice optimization algorithm that has been initialized, the modal decomposition times and the secondary penalty factor in the eigenmode decomposition algorithm that has been initialized are optimized to obtain an unbalanced power decomposition strategy based on the frost and ice optimization algorithm to optimize the eigenmode decomposition algorithm.

5. A hybrid energy storage capacity optimization configuration method according to claim 4, characterized in that: The unbalanced power is distributed according to the preset unbalanced power distribution strategy of the hybrid energy storage system, the total reference power of the hybrid energy storage system is obtained, and the actual output power is calibrated to determine the rated power and capacity configuration of the hybrid energy storage system, including: Allocate the unbalanced power according to the preset unbalanced power allocation strategy of the hybrid energy storage system, and record the unbalanced power of the hybrid energy storage system as the total reference power of the hybrid energy storage system; According to the unbalanced power decomposition strategy based on the frost optimization algorithm to optimize the characteristic mode decomposition algorithm, the total reference power of the hybrid energy storage system is decomposed to obtain a series of modal sub-components with different frequencies; Perform Hilbert transform on the modal subcomponents to obtain the distribution characteristics of each modal subcomponent in the frequency domain marginal spectrum, determine the boundary between high-frequency and low-frequency fluctuation subcomponents based on the principle of minimum aliasing of modal components, and obtain the power low-frequency fluctuation subcomponent and the power high-frequency fluctuation subcomponent; The power low-frequency fluctuation sub-component is recorded as the lithium battery sub-reference power of the lithium battery in the hybrid energy storage system; The power high frequency fluctuation subcomponent is recorded as the supercapacitor subreference power of the supercapacitor in the hybrid energy storage system; The actual output power is calibrated according to the supercapacitor sub-reference power and the lithium battery sub-reference power; Obtain the discharge power and charging power of the lithium battery. When the power low-frequency fluctuation sub-component is higher than 0, the lithium battery is determined to be in a discharge state. The lithium battery sub-reference power is output calibrated according to the charging power of the lithium battery to determine the actual output power of the lithium battery. When the power low-frequency fluctuation sub-component is lower than 0, it is determined that the lithium battery is in a charging state, and the lithium battery sub-reference power is output calibrated according to the discharge power of the lithium battery to determine the actual output power of the lithium battery; Obtain the discharge power and charging power of the supercapacitor. When the high-frequency fluctuation sub-component of the power is lower than 0, the supercapacitor is determined to be in a discharge state. The supercapacitor sub-reference power is output calibrated according to the discharge efficiency of the supercapacitor to determine the actual output power of the supercapacitor. When the power high-frequency fluctuation sub-component is higher than 0, it is determined that the supercapacitor is in a charging state, and the supercapacitor sub-reference power is output calibrated according to the charging efficiency of the supercapacitor to determine the actual output power of the supercapacitor; The rated power and capacity configuration of the hybrid energy storage system are determined according to the actual output power of the lithium battery and the actual output power of the supercapacitor.

6. A hybrid energy storage capacity optimization configuration method according to claim 5, characterized in that: The rated power and capacity configuration of the hybrid energy storage system are determined based on the actual output power of the lithium battery and the actual output power of the supercapacitor, including: Obtain a preset sampling period, and determine the rated power of the hybrid energy storage system according to the actual charging and discharging power demand of the hybrid energy storage system, wherein the rated power of the hybrid energy storage system includes: the rated power of the lithium battery and the rated power of the supercapacitor; ;in, Indicates the rated power of the lithium battery; Represents the low-frequency fluctuation subcomponent of power; Indicates the discharge efficiency of lithium batteries; Indicates the charging power of lithium battery; Indicates the rated power of the supercapacitor; Represents the high-frequency fluctuation subcomponent of power; Indicates the supercapacitor discharge efficiency; Indicates the supercapacitor charging efficiency; Determine the energy change of the lithium battery and the energy change of the supercapacitor according to the rated power of the hybrid energy storage system; The maximum discharge depth of the hybrid energy storage system is set, and the capacity configuration of the hybrid energy storage system is determined in combination with the energy change of the lithium battery and the energy change of the supercapacitor, wherein the capacity configuration of the hybrid energy storage system includes: supercapacitor capacity configuration and lithium battery capacity configuration; ;in, Indicates the supercapacitor capacity configuration; Indicates the maximum value of supercapacitor energy change; Indicates the minimum value of supercapacitor energy change; Indicates the supercapacitor discharge depth; Indicates the lithium battery capacity configuration; Indicates the maximum value of lithium battery energy change; Indicates the minimum value of lithium battery energy change; Indicates the discharge depth of lithium battery.

7. A hybrid energy storage capacity optimization configuration method according to claim 6, characterized in that: Also includes: Set capacity configuration constraints for the hybrid energy storage system.

8. The hybrid energy storage capacity optimization configuration method according to claim 1, characterized in that: The effectiveness of the power distribution of the hybrid energy storage system is verified based on the actual operation data of the wind farm, including: Obtain the actual operation data of the wind farm in the preset sampling period, determine the predicted power curve, measured power curve and unbalanced power curve of the wind farm, and verify the effectiveness of the power distribution of the hybrid energy storage system; According to the unbalanced power decomposition strategy of optimizing the characteristic mode decomposition algorithm based on the frost optimization algorithm, the unbalanced power in the unbalanced power curve is decomposed to obtain the modal decomposition result. When the distribution of each component curve in the modal decomposition result is clear, it is effective for the power allocation of the hybrid energy storage system.

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