Multivariate energy storage smoothing control method, device and equipment based on variable smoothing coefficient

Abstract

The application discloses a kind of based on variable smoothing coefficient Multivariate Energy Storage Smoothing Control Method, device and equipment, for the short service life of the hybrid energy storage currently using super capacitor and battery, the problem of polluting environment, by introducing hydrogen energy, real-time acquisition and record wind power;The target fluctuation of wind power allowed by power grid is calculated, to preliminarily determine the rated power and capacity of each energy storage in multivariate energy storage;The wind power fluctuation when wind power is connected to grid is calculated, the size of the fluctuation is compared with target fluctuation, to determine smoothing coefficient;Using smoothing index algorithm calculates the energy storage smoothing expected value of multivariate energy storage that smoothes wind power fluctuation;Combined with energy storage smoothing expected value, the power of multivariate energy storage grid is distributed;Introduce correction coefficient, calculate the distribution coefficient of multivariate energy storage power, to obtain the optimal allocation value of multivariate energy storage power. To extend the service life of multivariate energy storage system, reduce operation and maintenance cost.

Description

Technical Field

[0001] This invention belongs to the technical field of hybrid energy storage, and particularly relates to a method, device and equipment for multi-element energy storage smoothing control based on a variable smoothing coefficient. Background Technology

[0002] The inherent uncertainty, randomness, and volatility of wind power systems mean that large-scale grid connection of wind power will place a significant burden on grid frequency regulation, peak shaving, and system planning systems. Energy storage, with its excellent dynamic response, can effectively mitigate power fluctuations in wind power systems.

[0003] In recent years, with the rapid development of energy storage technology, most wind power mitigation studies have focused on the state of charge of energy storage systems, but have not given sufficient consideration to the factors affecting the charge-discharge cycle life of energy storage systems. Energy storage systems can be divided into power-type energy storage with fast response but relatively small capacity and energy-type energy storage with slow response but large capacity, based on their power and capacity.

[0004] Single-type energy storage devices are insufficient for effectively mitigating wind power fluctuations. Currently, hybrid energy storage systems are widely used, with supercapacitors and batteries being the most common. Supercapacitors offer high power density and excellent durability; however, battery storage suffers from short lifespan, high depreciation costs, heavy maintenance, and environmental pollution. Hydrogen energy storage, as an emerging energy storage method, boasts advantages such as being green and pollution-free, having high energy density, low operating and maintenance costs, and ease of storage and transmission, making it a highly promising new technology for large-scale energy storage. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-element energy storage smoothing control method, device, and equipment based on variable smoothing coefficients, which replaces the control of wind power fluctuations by traditional single energy storage systems. It uses a variable coefficient exponential smoothing method to smooth wind power fluctuations, and determines the optimal allocation of the expected power value corresponding to the optimal smoothing effect by using the expected power of multi-element energy storage, thereby extending the service life of the energy storage system.

[0006] To solve the above problems, the technical solution of the present invention is as follows:

[0007] A multi-element energy storage smoothing control method based on a variable smoothing coefficient includes:

[0008] Real-time acquisition and recording of wind power output;

[0009] Calculate the target fluctuation of wind power that the power grid can withstand in order to preliminarily determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage;

[0010] Calculate the wind power fluctuation when the wind power is connected to the grid, compare the magnitude of the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison result;

[0011] The expected wind power and expected energy storage level for smoothing wind power fluctuations were calculated using the exponential smoothing algorithm.

[0012] Based on the aforementioned energy storage mitigation expectation value, the power of the multi-energy storage grid is allocated;

[0013] A correction factor is introduced to calculate the allocation factor of the multi-element energy storage power in order to obtain the optimal allocation value of the multi-element energy storage power.

[0014] According to an embodiment of the present invention, the calculation of wind power fluctuation during grid connection further includes:

[0015] The wind power fluctuation is denoted as ΔP(t), and its calculation formula is as follows:

[0016] ΔP(t)=[P w (t)-(P c (t)+P e (t))]-[P w (t-1)-(P c (t-1)+P e (t-1))]

[0017] Among them, P w (t) represents the wind power at time t, P c (t) represents the supercapacitor damping power at time t, P e (t) represents the hydrogen storage damping power at time t, P w (t-1) represents the wind power at time t, P c (t-1) represents the supercapacitor damping power at time t-1, P e (t-1) represents the hydrogen storage power at time t-1.

[0018] According to an embodiment of the present invention, the smoothing coefficient θ(t) is determined by the following steps:

[0019] When ΔP(t)≥0, if 0≤ΔP(t)<χ, then θ(t)=1, where χ is the target fluctuation amount;

[0020] like Then θ(t) = χ / ΔP(t);

[0021] like but

[0022] When ΔP(t) < 0, if -χ ≤ ΔP(t) < 0, then θ(t) = 1;

[0023] like Then θ(t) = -χ / ΔP(t);

[0024] like but

[0025] in, Let t be the maximum continuous charging power and discharging power allowed by the multi-energy storage grid at time t.

[0026] According to an embodiment of the present invention, the calculation of the expected wind power and the expected energy storage smoothing value of the multi-element energy storage for smoothing wind power fluctuations using the exponential smoothing algorithm further includes:

[0027] Calculate the expected wind power using the following formula.

[0028]

[0029] Calculate the expected value of energy storage mitigation using the following formula.

[0030]

[0031] Among them, P w (t) represents the wind power at time t.

[0032] According to one embodiment of the present invention, the power allocation of the multi-energy storage grid, in conjunction with the energy storage mitigation expectation value, further includes:

[0033] Distribute power according to the following formula:

[0034]

[0035] in, Let be the expected power of the supercapacitor at time t. Let t be the expected power of hydrogen energy storage; k1 is the allocation coefficient of the expected power of the supercapacitor, and k2 is the allocation coefficient of the expected power of hydrogen energy storage. The values ​​of k1 and k2 range from 0 to 1.

[0036] According to one embodiment of the present invention, the calculation of the allocation coefficient of the multi-element energy storage power by introducing a correction coefficient to obtain the optimal allocation value of the multi-element energy storage power further includes:

[0037] Calculate the allocation coefficient k1 using the following formula:

[0038]

[0039] in, λ1 and λ2 are the maximum allowable charging power and maximum allowable discharging power of the supercapacitor at time t, respectively; λ1 and λ2 are correction coefficients used to ensure that the supercapacitor has sufficient charging and discharging margins at time (t+1).

[0040] k2 = 1 - k1, and the actual power distribution of hydrogen energy storage is calculated by combining the inherent limitations of hydrogen energy storage.

[0041] According to an embodiment of the present invention, the values ​​of the correction coefficients λ1 and λ2 are determined according to the following rules:

[0042] When the supercapacitor's state of charge (SOC) at time t-1 c (t) satisfies 0.45≤SOC c When (t)≤0.75, the values ​​of λ1 and λ2 are both 1;

[0043] When 0.35≤SOC c When (t) < 0.45, take λ1 = 1 and λ2 = 0.9;

[0044] When 0.25≤SOC c When (t) < 0.35, take λ1 = 1 and λ2 = 0.8;

[0045] When 0.75 <SOC c When (t)≤0.85, take λ1=0.9 and λ2=1;

[0046] When 0.85 <SOC c When (t)≤0.95, take λ1=0.8 and λ2=1.

[0047] A multi-element energy storage smoothing control device based on a variable smoothing coefficient includes:

[0048] The data acquisition module is used to collect and record wind power in real time;

[0049] An initialization module is used to calculate the target fluctuation of wind power that the power grid can withstand, so as to initially determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage.

[0050] The smoothing coefficient determination module is used to calculate the wind power fluctuation when the wind power is connected to the grid, compare the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison result.

[0051] The smoothing module is used to calculate the expected wind power and the expected energy storage smoothing value of the multi-element energy storage system to smooth wind power fluctuations using the exponential smoothing algorithm.

[0052] The power allocation module is used to allocate power to the multi-energy storage grid based on the energy storage smoothing expectation value.

[0053] The optimization module is used to introduce correction coefficients and calculate the allocation coefficients of multi-element energy storage power to obtain the optimal allocation value of multi-element energy storage power.

[0054] A multi-element energy storage smoothing control device based on a variable smoothing coefficient includes a memory and a processor. The memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform steps in the multi-element energy storage smoothing control method based on a variable smoothing coefficient according to an embodiment of the present invention.

[0055] A storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform steps in a multi-element energy storage smoothing control method based on a variable smoothing coefficient according to an embodiment of the present invention.

[0056] Because the present invention adopts the above technical solution, it has the following advantages and positive effects compared with the prior art:

[0057] This invention discloses a multi-element energy storage system based on a variable smoothing coefficient. Addressing the issues of short lifespan and environmental pollution associated with current hybrid energy storage systems using supercapacitors and batteries, this method replaces batteries with hydrogen energy storage. It collects and records wind power in real time; calculates the target wind power fluctuation that the grid can tolerate to initially determine the rated power and capacity of each component in the multi-element energy storage system; calculates the wind power fluctuation during grid connection; compares the wind power fluctuation with the target fluctuation; and determines the smoothing coefficient based on the comparison results. A smoothing exponential algorithm is used to calculate the expected wind power and energy storage smoothing values ​​for the multi-element energy storage system to mitigate wind power fluctuations. The power of the multi-element energy storage grid is allocated based on the energy storage smoothing expectations. A correction coefficient is introduced to calculate the allocation coefficient of the multi-element energy storage power to obtain the optimal allocation value. This extends the lifespan of the multi-element energy storage system and reduces operation and maintenance costs. Attached Figure Description

[0058] Figure 1 This is a flowchart of a multi-element energy storage smoothing control method based on a variable smoothing coefficient in one embodiment of the present invention.

[0059] Figure 2 This is an architecture diagram of a multi-element energy storage smoothing control method based on a variable smoothing coefficient according to an embodiment of the present invention.

[0060] Figure 3 This is a typical daily power curve of wind power in one embodiment of the present invention;

[0061] Figure 4This is a block diagram of a multi-element energy storage smoothing control device based on a variable smoothing coefficient according to an embodiment of the present invention.

[0062] Figure 5 This is a schematic diagram of a multi-element energy storage smoothing control device based on a variable smoothing coefficient according to an embodiment of the present invention. Detailed Implementation

[0063] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a method, apparatus, and device for smoothing control of multi-element energy storage based on a variable smoothing coefficient, as proposed in this invention. The advantages and features of this invention will become clearer from the following description and claims.

[0064] Example 1

[0065] This embodiment provides a multi-element energy storage smoothing control method based on a variable smoothing coefficient, replacing the traditional single energy storage system for controlling wind power fluctuations. It includes the following steps:

[0066] Real-time acquisition and recording of wind power output;

[0067] Calculate the target fluctuation of wind power that the power grid can withstand in order to preliminarily determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage;

[0068] Calculate the wind power fluctuation when the wind power is connected to the grid, compare the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison results;

[0069] The expected wind power and expected energy storage level for smoothing wind power fluctuations were calculated using the exponential smoothing algorithm.

[0070] Based on the expected value of energy storage smoothing, the power of the multi-energy storage grid is allocated;

[0071] A correction factor is introduced to calculate the allocation factor of the multi-element energy storage power in order to obtain the optimal allocation value of the multi-element energy storage power.

[0072] For practical applications, please refer to Figure 1 The multi-element energy storage smoothing control method based on variable smoothing coefficient is implemented as follows:

[0073] 1) Initialize the hybrid energy storage state and input real-time wind power data P. w (t);

[0074] 2) Calculate the wind power fluctuation ΔP(t) and the smoothing coefficient θ;

[0075] 3) The expected value of wind power is calculated using the exponential smoothing algorithm. Calculate the expected value of energy storage smoothing

[0076] 4) Calculate the hybrid energy storage power allocation coefficients k1 and k2 and the expected power of each energy storage unit.

[0077] 5) Smoothing the expected values ​​of each energy storage system under its own internal constraints;

[0078] 6) Calculate the maximum charging power of the supercapacitor. and maximum discharge power

[0079] 7) Calculate the maximum charging power of hybrid energy storage and maximum discharge power

[0080] 8) Determine if t is greater than N, where N is the maximum value of the time window during the calculation process; if yes, the smoothing ends; otherwise, return to step 1) to continue executing the control method.

[0081] For details, please refer to Figure 2 The wind power fluctuation ΔP(t) represents the range of wind power fluctuations that the power grid to which the wind farm is connected can withstand. ΔP(t) = [P w (t)-(P c (t)+P e (t))]-[P w (t-1)-(P c (t-1)+P e (t-1))], where P w (t) represents the wind power at time t, P c (t) represents the supercapacitor damping power at time t, P e (t) represents the hydrogen storage damping power at time t, P w (t-1) represents the wind power at time t, P c (t-1) represents the supercapacitor damping power at time t-1, P e (t-1) represents the hydrogen storage power at time t-1.

[0082] When the wind power fluctuation ΔP(t) exceeds the grid's allowable fluctuation range χ, it is first smoothed out by supercapacitors. To prevent the supercapacitors from overcharging and over-discharging, hydrogen energy storage is used to assist in smoothing. When the supercapacitors are saturated or undercharged, the hydrogen energy storage system is activated or the smoothing intensity is increased to bring the supercapacitors back to normal.

[0083] The smoothing coefficient θ refers to the variable coefficient used in the exponential smoothing operation based on the weighted average of all historical sequence data of the smoothing object.

[0084] The smoothing coefficient θ is determined as follows:

[0085] When ΔP(t)≥0, if 0≤ΔP(t)<χ, then θ(t)=1, meaning that the wind power fluctuation meets the fluctuation requirements and no hybrid energy storage is needed to smooth it out. Here, χ is the allowable fluctuation range of wind power, i.e., the quasi-target fluctuation amount; if Then θ(t) = χ / ΔP(t); if Considering the discharge capacity of the energy storage system during this time period, we take...

[0086] When ΔP(t) < 0, if -χ ≤ ΔP(t) < 0, then θ(t) = 1, and there is no need for mixed energy storage to smooth it out; if Then θ(t) = -χ / ΔP(t); if Considering the charging capacity of the energy storage system during this time period, we take...

[0087] In the above formula, Let t be the maximum allowable continuous charging power and discharging power of the hybrid energy storage power station.

[0088] The method for calculating the expected value of wind power is as follows:

[0089]

[0090] Energy storage smooths out expectations The calculation method is as follows:

[0091]

[0092] This energy storage system, combined with supercapacitors and hydrogen energy storage, distributes system power according to the following formula:

[0093]

[0094] in, denoted by k1 and k2, respectively, represent the expected charge and discharge power of the supercapacitor and the hydrogen storage at time t; k1 and k2 are the power allocation coefficients of the supercapacitor and the hydrogen storage, respectively, with values ​​ranging from 0 to 1.

[0095] The formula for calculating the power distribution factor k1 of a supercapacitor is:

[0096]

[0097] These represent the maximum allowable charging power and the maximum allowable discharging power of the supercapacitor at time t, respectively.

[0098] k2 = 1 - k1, and the actual power distribution of hydrogen energy storage is calculated by combining the inherent limitations of hydrogen energy storage.

[0099] λ1 and λ2 are correction coefficients used in the calculation of the power distribution coefficient to ensure that the supercapacitor has sufficient charge and discharge margin at time (t+1). The values ​​of λ1 and λ2 are determined according to the following rules:

[0100] 1) The state of charge (SOC) of the supercapacitor at time t-1 c (t) satisfies 0.45≤SOC c When (t)≤0.75, λ1 and λ2 both take the value of 1;

[0101] 2) When 0.35 ≤ SOC c When (t) < 0.45, the SOC of the supercapacitor is too low. If the stored energy needs to be discharged at this time, the discharge amount of the supercapacitor should be slightly reduced. Therefore, λ1 = 1 and λ2 = 0.9 are taken.

[0102] 3) When 0.25 ≤ SOC c When (t) < 0.35, as described in 2), λ1 = 1 and λ2 = 0.8.

[0103] 4) When 0.75 <SOC c When (t)≤0.85, the SOC of the supercapacitor is too high. If the energy storage needs to be charged at this time, the charging amount of the supercapacitor should be slightly reduced. Therefore, λ1=0.9 and λ2=1 are taken.

[0104] 5) When 0.85 <SOC c When (t)≤0.95, as described in 4), we take λ1=0.8 and λ2=1.

[0105] The multi-element energy storage stabilization control method based on variable smoothing coefficient in this embodiment uses a variable coefficient exponential smoothing method to stabilize wind power fluctuations, and determines the optimal allocation of the expected power value corresponding to the optimal stabilization effect by using the expected power of multi-element energy storage.

[0106] In another embodiment, during the initialization of the hybrid energy storage state, the multi-element energy storage is regulated by fluctuation probability. As the target fluctuation, it is combined with the typical historical daily power curve of wind power (see below). Figure 3Capacity allocation is performed. In the expression Ps for the fluctuation probability, P(t) is the input active power value of the wind farm (wind power + energy storage) connected to the grid at time t; P(t-1) is the input active power value of the wind farm (wind power + energy storage) connected to the grid at time (t-1); Δt is the sampling period time corresponding to the sampling window, usually the wind power system samples once every 15 minutes; T is the total sampling time; χ is the target fluctuation range allowed by the frequency regulation capability of wind power connected to the grid. Generally, wind farms larger than 100MW should have the capability of one frequency regulation, and the regulation capacity should be at least 2% of the rated power of the wind farm.

[0107] Because the rated power and capacity of supercapacitors and hydrogen energy storage are limited by the smoothing effect and economic cost, their smoothing power is selected based on a relatively small rated power and capacity of energy storage at a low level of fluctuation probability.

[0108] Specifically, the remaining energy stored in the supercapacitor is represented by a gradually accumulating form of energy. The remaining energy of the supercapacitor at time t is expressed as:

[0109] E c (t)=(1-μ c Δt)E c (t-1)+P c (t)Δtη c

[0110] In the formula, μ c η is the self-discharge rate of the supercapacitor. c P represents the charge / discharge efficiency of the supercapacitor, Δt is the duration of the calculation window, and P is the value of P. c (t) represents the charging and discharging power of the supercapacitor at time t.

[0111] The remaining charge E of the supercapacitor c (t) and state of charge (SOC) c The formula for calculating (t) is:

[0112]

[0113] In the formula, E ct This represents the total capacitance of the supercapacitor.

[0114] The constraints of this supercapacitor are:

[0115]

[0116] In the formula, SOC Cmin SOC Cmax These are the lower and upper limits of the state of charge of the supercapacitor, respectively, and are set to 25% and 95%. P represents the maximum allowable charging power and the maximum allowable discharging power of the supercapacitor at time t, respectively;Cc_max P Cd_max These represent the maximum continuous charging power and discharging power of the supercapacitor, respectively; P Csc (t), P Csd (t) represents the charging power and discharging power of the supercapacitor at time t, constrained by the remaining charge; E Cmax E Cmin These represent the maximum stored capacity and minimum remaining capacity of the supercapacitor, respectively; min{a,b} is the minimum value function, taking the minimum value between a and b.

[0117] Hydrogen energy storage involves two processes: electrolyzers storing electrical energy to produce hydrogen and fuel cells releasing electrical energy to consume hydrogen. The relationship between the power of the electrolyzer and fuel cells and the rates of hydrogen production and consumption is as follows:

[0118]

[0119] In the formula, Let t represent the hydrogen production power of the electrolyzer and the hydrogen consumption power of the fuel cell. For the hydrogen production efficiency of electrolyzers and the hydrogen consumption efficiency of fuel cells, Let be the hydrogen production rate of the electrolyzer and the hydrogen consumption rate of the fuel cell at time t. Let t be the rated voltage of the electrolyzer and the rated voltage of the fuel cell at time t, and F be the Faraday constant.

[0120] The remaining electricity and its state of charge relationship in a hydrogen energy storage system constructed using the gas pressure of a hydrogen storage tank are as follows:

[0121]

[0122] In the formula, The maximum pressure that the hydrogen storage tank can withstand is given by the following: At time t, the pressure inside the hydrogen storage tank is:

[0123]

[0124] In the formula, V is the volume of the hydrogen storage tank, R is the universal gas constant, and T is the gas temperature.

[0125] The constraints for hydrogen energy storage are:

[0126]

[0127] In the formula, These are the upper and lower limits of the gas storage state of the hydrogen storage tank, respectively, with values ​​of 80% and 20%. P represents the maximum allowable charging power of the electrolyzer and the maximum allowable discharging power of the fuel cell at time t, respectively; esc (t), P esd(t) represents the charging power and discharging power of the hydrogen energy storage system at time t, constrained by the remaining hydrogen in the hydrogen storage tank. These are the rated power of a single electrolyzer and the rated power of a single fuel cell unit, respectively. These represent the number of electrolyzers connected in series and the number of fuel cell units connected in series, respectively. These are the upper and lower limits of the gas pressure in the hydrogen storage tank, respectively.

[0128] In this embodiment, the power fluctuation probability is used as the target fluctuation amount of the system. Based on the construction of supercapacitor and hydrogen energy storage models, the state of charge and charging and discharging constraints of supercapacitor and hydrogen energy storage are considered to determine the rated power and capacity of each energy storage module of multi-element energy storage.

[0129] Example 2

[0130] This embodiment provides a multi-element energy storage smoothing control device based on a variable smoothing coefficient. Please refer to [link / reference]. Figure 4 The device includes:

[0131] Data acquisition module 1 is used to collect and record wind power in real time;

[0132] Initialization module 2 is used to calculate the target fluctuation of wind power that the power grid can withstand, so as to initially determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage;

[0133] The smoothing coefficient determination module 3 is used to calculate the wind power fluctuation when the wind power is connected to the grid, compare the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison result.

[0134] The smoothing module 4 is used to calculate the expected wind power and the expected energy storage smoothing value of the multi-element energy storage for smoothing wind power fluctuations using the smoothing exponential algorithm.

[0135] The power allocation module 5 is used to allocate power to the multi-energy storage grid by combining the energy storage smoothing expectation value;

[0136] Optimization module 6 is used to introduce correction coefficients and calculate the allocation coefficients of multi-element energy storage power in order to obtain the optimal allocation value of multi-element energy storage power.

[0137] The functions and implementation methods of the above-mentioned data acquisition module 1, initialization module 2, smoothing coefficient determination module 3, smoothing module 4, power allocation module 5 and optimization module 6 are as described in Embodiment 1 above, and will not be repeated here.

[0138] Example 3

[0139] This embodiment provides a multi-element energy storage smoothing control device based on a variable smoothing coefficient. Please refer to [link / reference].Figure 5 The variable smoothing coefficient-based multi-element energy storage smoothing control device 500 can vary considerably depending on its configuration or performance. It may include one or more central processing units (CPUs) 510 (e.g., x86, ARM architecture processors, or FPGAs) and memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing application programs 533 or data 532. The memory 520 and storage media 530 can be temporary or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the figure), each module including a series of instruction operations on the variable smoothing coefficient-based multi-element energy storage smoothing control device 500.

[0140] Furthermore, the processor 510 can be configured to communicate with the storage medium 530 and execute a series of instruction operations in the storage medium 530 on the multi-element energy storage stabilization control device 500 based on the variable smoothing coefficient.

[0141] The multi-element energy storage smoothing control device 500 based on a variable smoothing coefficient may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Vista, etc.

[0142] Those skilled in the art will understand that Figure 5 The structure of the multi-element energy storage smoothing control device based on the variable smoothing coefficient shown does not constitute a limitation on the multi-element energy storage smoothing control device based on the variable smoothing coefficient. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0143] Another embodiment of the present invention also provides a computer-readable storage medium.

[0144] The computer-readable storage medium can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the method for a multi-element energy storage smoothing control device based on a variable smoothing coefficient in Embodiment 1.

[0145] If the multi-element energy storage smoothing control method based on variable smoothing coefficients is implemented in the form of program instructions and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, essentially, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in software. This computer software is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific identification content executed by the system and device described above can be referred to the corresponding process in the foregoing method embodiments.

[0147] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they shall still fall within the protection scope of the present invention.

Claims

1. A multi-element energy storage smoothing control method based on a variable smoothing coefficient, characterized in that, include: Real-time acquisition and recording of wind power output; Calculate the target fluctuation of wind power that the power grid can withstand in order to preliminarily determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage; Calculate the wind power fluctuation when the wind power is connected to the grid, compare the magnitude of the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison result; The expected wind power value of multi-element energy storage to mitigate wind power fluctuations is calculated using a first-order exponential smoothing algorithm. The expected value of energy storage mitigation is determined based on the difference between the expected wind power value and the real-time wind power. Based on the aforementioned energy storage mitigation expectation value, the power of the multi-energy storage grid is allocated; A correction factor is introduced, and the allocation factor of the multi-element energy storage power is calculated based on the segmented values ​​of the current state of charge of the supercapacitor, so as to obtain the optimal allocation value of the multi-element energy storage power; the correction factor is used to ensure that the supercapacitor has sufficient charge and discharge margin for smoothing in the next moment.

2. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 1, characterized in that, The calculation of wind power fluctuation during grid connection further includes: The wind power fluctuation is denoted as The calculation formula is as follows: in, Let be the wind power at time t. The supercapacitor power smoothing at time t. Let be the hydrogen storage power level at time t. Let be the wind power at time t-1. The supercapacitor power smoothing at time t-1 The hydrogen storage power level at time t-1 is the power level of the hydrogen storage system.

3. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 2, characterized in that, Determine the smoothing coefficient using the following steps. : When ΔP(t)≥0, if ,but ,in, The target volatility; like ,but ; like ,but ); When ΔP(t) < 0, if ,but ; like- ,but ; If -∞ < ΔP < - ,but ); in, , Let t be the maximum continuous charging power and discharging power allowed by the multi-energy storage grid at time t.

4. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 3, characterized in that, The expected wind power output and expected energy storage output for smoothing wind power fluctuations, calculated using the exponential smoothing algorithm, further include: Calculate the expected wind power using the following formula. : Calculate the expected value of energy storage mitigation using the following formula. : in, Let t be the wind power output at time t.

5. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 4, characterized in that, Based on the aforementioned energy storage mitigation expectation, the power allocation of the multi-energy storage grid further includes: Distribute power according to the following formula: in, Let be the expected power of the supercapacitor at time t. Let t be the expected power of hydrogen energy storage; k1 is the allocation coefficient of the expected power of the supercapacitor, and k2 is the allocation coefficient of the expected power of hydrogen energy storage. The values ​​of k1 and k2 are in the range of 0 to 1.

6. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 5, characterized in that, Introducing a correction factor, the allocation coefficient for multi-element energy storage power is calculated to obtain the optimal allocation value for multi-element energy storage power. This further includes: Calculate the allocation coefficient k1 using the following formula: in, These represent the maximum allowable charging power and the maximum allowable discharging power of the supercapacitor at time t, respectively. and This is a correction factor used to ensure that the supercapacitor has sufficient charge and discharge margin at time (t+1). k2 = 1 - k1, and the actual power distribution of hydrogen energy storage is calculated by combining the inherent limitations of hydrogen energy storage.

7. The multi-element energy storage smoothing control method based on variable smoothing coefficient as described in claim 6, characterized in that, The correction coefficient and The rules for determining the value are as follows: The state of charge of the supercapacitor at time t Satisfying 0.45≤ When ≤0.75, take , All values ​​are 1; When 0.35≤ When <0.45, take , ; When 0.25≤ When <0.35, take , ; When 0.75 < When ≤0.85, take , ; When 0.85 < When ≤0.95, take , .

8. A multi-element energy storage smoothing control device based on a variable smoothing coefficient, characterized in that, include: The data acquisition module is used to collect and record wind power in real time; An initialization module is used to calculate the target fluctuation of wind power that the power grid can withstand, so as to initially determine the rated power and capacity of each part of the multi-element energy storage; the multi-element energy storage includes at least supercapacitor energy storage and hydrogen energy storage. The smoothing coefficient determination module is used to calculate the wind power fluctuation when the wind power is connected to the grid, compare the wind power fluctuation with the target fluctuation, and determine the smoothing coefficient based on the comparison result. The smoothing module is used to calculate the expected wind power value of the multi-element energy storage to smooth wind power fluctuations using a first-order exponential smoothing algorithm, and to determine the energy storage smoothing expected value based on the difference between the expected wind power value and the real-time wind power. The power allocation module is used to allocate power to the multi-energy storage grid based on the energy storage smoothing expectation value. The optimization module is used to introduce correction coefficients and calculate the allocation coefficients of the multi-element energy storage power based on the segmented values ​​of the current state of charge of the supercapacitor, so as to obtain the optimal allocation value of the multi-element energy storage power; the correction coefficients are used to ensure that the supercapacitor has sufficient charge and discharge margins for smoothing in the next moment.

9. A multi-element energy storage smoothing control device based on a variable smoothing coefficient, characterized in that, include: A memory and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform the steps in the multi-element energy storage smoothing control method based on a variable smoothing coefficient as described in any one of claims 1 to 7.

10. A storage medium storing computer-readable instructions, characterized in that, When the computer-readable instructions are executed by one or more processors, the one or more processors perform the steps in the multi-element energy storage smoothing control method based on a variable smoothing coefficient as described in any one of claims 1 to 7.

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