阅读说明：本技术 混合储能抑制风电波动方法、装置、电子设备和存储介质 (Method and device for inhibiting wind power fluctuation through hybrid energy storage, electronic equipment and storage medium ) 是由 李姚旺 张宁 杜尔顺 贺鸿杰 雍培 叶俊 李艳红 于 2021-08-20 设计创作，主要内容包括：本公开涉及一种混合储能抑制风电波动方法、装置、电子设备和存储介质,属于储能技术领域。其中方法包括：获取风电机组待预测时刻前连续多个时刻的风电波动功率及待预测时刻风速数据；将各时刻风电波动功率分解为多个模态分量；将各模态分量划分为对应的低频或高频波动分量；将各时刻同种波动分量和待预测时刻风速数据输入对应低频波动分量预测模型和高频波动分量预测模型,得到各低频波动分量和高频波动分量的预测结果；将预测结果作为储能装置的指令信号,实现对所述风电机组功率波动的平抑。本公开能够对风电波动分量进行准确预测,储能装置的指令信号为精细化功率预测的结果,可进一步提高风电功率平抑的准确性,保证风电并网的可靠性。(The disclosure relates to a method and a device for suppressing wind power fluctuation through hybrid energy storage, electronic equipment and a storage medium, and belongs to the technical field of energy storage. The method comprises the following steps: acquiring wind power fluctuation power and wind speed data of a wind turbine generator at a plurality of continuous moments before a moment to be predicted; wind power fluctuation power at each moment is decomposed into a plurality of modal components; dividing each modal component into corresponding low-frequency or high-frequency fluctuation components; inputting the same fluctuation component at each moment and wind speed data at the moment to be predicted into a corresponding low-frequency fluctuation component prediction model and a high-frequency fluctuation component prediction model to obtain prediction results of each low-frequency fluctuation component and each high-frequency fluctuation component; and taking the prediction result as an instruction signal of the energy storage device to realize the stabilization of the power fluctuation of the wind turbine generator. According to the method and the device, the wind power fluctuation component can be accurately predicted, the command signal of the energy storage device is a result of fine power prediction, the accuracy of wind power stabilization can be further improved, and the reliability of wind power grid connection is guaranteed.)

1. A method for suppressing wind power fluctuation through hybrid energy storage is characterized by comprising the following steps:

acquiring wind power fluctuation power of a wind turbine generator at a plurality of continuous moments before a moment to be predicted and wind speed data of the moment to be predicted;

decomposing the wind power fluctuation power at the multiple moments according to a preset variation modal decomposition number to obtain multiple modal components of the wind power fluctuation power at the multiple moments;

dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components respectively;

correspondingly inputting the same fluctuation component of the plurality of moments and the wind speed data of the moments to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator;

and taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of an energy storage device so as to realize the stabilization of the power fluctuation of the wind turbine generator.

2. The method according to claim 1, wherein the step of using the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as command signals of an energy storage device to achieve stabilization of the power fluctuation of the wind turbine generator comprises the following steps:

taking the sum of the prediction results of all the low-frequency fluctuation components as an instruction signal of a storage battery energy storage element in the energy storage device, and taking the sum of the prediction results of all the high-frequency fluctuation components as an instruction signal of a super-capacitor energy storage element in the energy storage device; when the command signal is positive, the energy storage device is charged, and the charging power is the magnitude of the command signal; and when the command signal is negative, the energy storage device discharges, and the discharge power is the magnitude of the command signal.

3. The method according to claim 1, characterized in that the wind power fluctuation power is obtained by the following method:

acquiring the original power of the wind turbine generator during actual operation;

filtering the original power, and taking a filtering result as wind power grid-connected power of the wind turbine generator;

and subtracting the wind power grid-connected power from the original power to obtain the wind power fluctuation power of the wind turbine generator.

4. The method of claim 1, wherein the number of variational modal decompositions is determined by:

1) setting the initial value K of the decomposition number of the variation mode as 2;

2) carrying out variation modal decomposition on the wind power fluctuation power to obtain K modal components;

3) respectively calculating the central frequencies of the K modal components;

4) judging whether repeated center frequencies exist in the K modal components: if not, making K equal to K +1, and then returning to the step 2); if present, the final value for the number of metamorphic mode decompositions is K-1.

5. The method according to claim 1, wherein the dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components, respectively; the specific method comprises the following steps:

and judging the center frequencies of the modal components according to a set threshold:

if the central frequency of the modal component is less than a set threshold value, dividing the modal component into corresponding low-frequency fluctuation components;

and if the central frequency of the modal component is greater than or equal to a set threshold value, dividing the modal component into corresponding high-frequency fluctuation components.

6. The method of claim 1, wherein the low frequency fluctuation component prediction model is a shallow neural network model and the high frequency fluctuation component prediction model is a deep neural network model.

7. The method according to claim 6, wherein before the wind speed data of the same kind of fluctuation component at each time and the time to be predicted are correspondingly input into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator, the method further comprises:

training the predictive model;

wherein the training the predictive model comprises:

establishing a training sample set of each low-frequency fluctuation component prediction model, training each low-frequency fluctuation component prediction model, and obtaining each trained low-frequency fluctuation component prediction model;

establishing a training sample set of each high-frequency fluctuation component prediction model, training each high-frequency fluctuation component prediction model, and obtaining each trained high-frequency fluctuation component prediction model;

each sample in a training sample set of the low-frequency fluctuation component prediction model comprises wind speed data of N +1 th historical time in continuous N +1 historical times corresponding to the low-frequency fluctuation component and the N +1 historical times; during training, the wind speed data of the previous N historical moments corresponding to the low-frequency fluctuation component and the N +1 th historical moment in the sample are used as the input of the low-frequency fluctuation component prediction model, and the low-frequency fluctuation component of the N +1 th historical moment in the sample is used as a label;

each sample in the training sample set of the high-frequency fluctuation component prediction model comprises wind speed data of N +1 th historical time in continuous N +1 historical times corresponding to the high-frequency fluctuation component and the N +1 historical times; during training, the high-frequency fluctuation components corresponding to the previous N historical moments and the wind speed data of the (N + 1) th historical moment in the sample are used as the input of the high-frequency fluctuation component prediction model, and the high-frequency fluctuation components corresponding to the (N + 1) th historical moments in the sample are used as labels.

8. The utility model provides a mixed energy storage suppresses wind-powered electricity generation undulant device which characterized in that includes:

the acquiring module is used for acquiring wind power fluctuation power of the wind turbine generator at a plurality of continuous moments before the moment to be predicted and wind speed data of the moment to be predicted;

the variation modal decomposition module is used for decomposing the wind power fluctuation power at multiple moments according to a preset variation modal decomposition number to obtain multiple modal components of the wind power fluctuation power at multiple moments;

the fluctuation component dividing module is used for dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components respectively;

the prediction module is used for correspondingly inputting the same fluctuation component of the moments and the wind speed data of the moments to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model so as to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator set;

and the stabilizing module is used for taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of the energy storage device so as to realize the stabilization of the power fluctuation of the wind turbine generator.

9. An electronic device, comprising:

at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.

Technical Field

The disclosure belongs to the technical field of energy storage, and particularly relates to a method and device for inhibiting wind power fluctuation through hybrid energy storage, electronic equipment and a storage medium.

Background

In order to deal with the threat brought by global warming, China proposes a strategy of 'carbon peak reaching and carbon neutralization', and strives to achieve the aim of carbon neutralization before 2060 without increasing the carbon emission before 2030. Combustion of fossil fuels to produce CO₂Therefore, the conventional power system is gradually transformed into a high-proportion renewable energy power system, and new energy represented by wind energy is started to be connected into a power grid in a large scale. Compared with the traditional fossil energy power generation mode, the wind energy has the advantages of cleanness, high efficiency, reproducibility and the like, but the output of the wind energy has strong uncertainty and severe power fluctuation, and the problem of oscillation can be caused when the wind energy is connected with a power grid. With the continuous improvement of the wind power permeability, the problem of wind power output fluctuation is paid more and more attention.

In recent years, energy storage technology is continuously developed and perfected. The energy storage system has bidirectional power regulation capacity, and the energy storage element is used for flexibly storing and releasing electric energy, so that the wind power output fluctuation can be reduced, and the wind power receiving capacity is improved. However, in the conventional method, a power regulation instruction signal of the future energy storage is generally set based on the current operating state of the wind turbine, and although the wind power fluctuation can be reduced, the difference between the current operating state of the wind turbine and the future is not considered.

Disclosure of Invention

The disclosure aims to overcome the defects of the prior art and provide a method and a device for suppressing wind power fluctuation by hybrid energy storage, an electronic device and a storage medium. According to the method and the device, the wind power fluctuation component can be accurately predicted, the command signal of the energy storage device is a result of fine power prediction, the accuracy of wind power stabilization can be further improved, and the reliability of wind power grid connection is guaranteed.

An embodiment of a first aspect of the present disclosure provides a method for suppressing wind power fluctuation by hybrid energy storage, including:

acquiring wind power fluctuation power of a wind turbine generator at a plurality of continuous moments before a moment to be predicted and wind speed data of the moment to be predicted;

decomposing the wind power fluctuation power at the multiple moments according to a preset variation modal decomposition number to obtain multiple modal components of the wind power fluctuation power at the multiple moments;

dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components respectively;

correspondingly inputting the same fluctuation component of the plurality of moments and the wind speed data of the moments to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator;

and taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of an energy storage device so as to realize the stabilization of the power fluctuation of the wind turbine generator.

In an embodiment of the present disclosure, the taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of an energy storage device to achieve stabilization of the power fluctuation of the wind turbine generator, includes:

taking the sum of the prediction results of the low-frequency fluctuation components as an instruction signal of a storage battery energy storage element in the energy storage device, and taking the sum of the prediction results of the high-frequency fluctuation components as an instruction signal of a super-capacitor energy storage element in the energy storage device; when the command signal is positive, the energy storage device is charged, and the charging power is the magnitude of the command signal; and when the command signal is negative, the energy storage device discharges, and the discharge power is the magnitude of the command signal.

In an embodiment of the disclosure, the wind power fluctuation power obtaining method includes:

acquiring the original power of the wind turbine generator during actual operation;

filtering the original power, and taking a filtering result as wind power grid-connected power of the wind turbine generator;

and subtracting the wind power grid-connected power from the original power to obtain the wind power fluctuation power of the wind turbine generator.

In one embodiment of the present disclosure, the method for determining the number of variational modal decompositions is as follows:

1) setting the initial value K of the decomposition number of the variation mode as 2;

2) carrying out variation modal decomposition on the wind power fluctuation power to obtain K modal components;

3) respectively calculating the central frequencies of the K modal components;

4) judging whether repeated center frequencies exist in the K modal components: if not, making K equal to K +1, and then returning to the step 2); if present, the final value for the number of metamorphic mode decompositions is K-1.

In an embodiment of the present disclosure, the dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components according to center frequencies of the plurality of modal components; the specific method comprises the following steps:

and judging the center frequencies of the modal components according to a set threshold:

if the central frequency of the modal component is less than a set threshold value, dividing the modal component into corresponding low-frequency fluctuation components;

and if the central frequency of the modal component is greater than or equal to a set threshold value, dividing the modal component into corresponding high-frequency fluctuation components.

In one embodiment of the present disclosure, the low-frequency fluctuation component prediction model is a shallow neural network model, and the high-frequency fluctuation component prediction model is a deep neural network model.

In an embodiment of the present disclosure, before the correspondingly inputting the same kind of fluctuation component at each time and the wind speed data at the time to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model to obtain prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator, the method further includes:

training the predictive model;

wherein the training the predictive model comprises:

establishing a training sample set of each low-frequency fluctuation component prediction model, training each low-frequency fluctuation component prediction model, and obtaining each trained low-frequency fluctuation component prediction model;

establishing a training sample set of each high-frequency fluctuation component prediction model, training each high-frequency fluctuation component prediction model, and obtaining each trained high-frequency fluctuation component prediction model;

each sample in a training sample set of the low-frequency fluctuation component prediction model comprises wind speed data of N +1 th historical time in continuous N +1 historical times corresponding to the low-frequency fluctuation component and the N +1 historical times; during training, the wind speed data of the previous N historical moments corresponding to the low-frequency fluctuation component and the N +1 th historical moment in the sample are used as the input of the low-frequency fluctuation component prediction model, and the low-frequency fluctuation component of the N +1 th historical moment in the sample is used as a label;

each sample in the training sample set of the high-frequency fluctuation component prediction model comprises wind speed data of N +1 th historical time in continuous N +1 historical times corresponding to the high-frequency fluctuation component and the N +1 historical times; during training, the high-frequency fluctuation components corresponding to the previous N historical moments and the wind speed data of the (N + 1) th historical moment in the sample are used as the input of the high-frequency fluctuation component prediction model, and the high-frequency fluctuation components corresponding to the (N + 1) th historical moments in the sample are used as labels.

An embodiment of a second aspect of the present disclosure provides a hybrid energy storage device for suppressing wind power fluctuation, including:

the acquiring module is used for acquiring wind power fluctuation power of the wind turbine generator at a plurality of continuous moments before the moment to be predicted and wind speed data of the moment to be predicted;

the variation modal decomposition module is used for decomposing the wind power fluctuation power at multiple moments according to a preset variation modal decomposition number to obtain multiple modal components of the wind power fluctuation power at multiple moments;

the fluctuation component dividing module is used for dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components respectively;

the prediction module is used for correspondingly inputting the same fluctuation component of the moments and the wind speed data of the moments to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model so as to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator set;

and the stabilizing module is used for taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of the energy storage device so as to realize the stabilization of the power fluctuation of the wind turbine generator.

An embodiment of a third aspect of the present disclosure provides an electronic device, including:

at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a hybrid energy storage wind power fluctuation suppression method as described above.

A fourth aspect of the present disclosure is directed to a computer-readable storage medium, which stores computer instructions for causing a computer to execute the method for suppressing wind power fluctuation by hybrid energy storage.

The characteristics and the beneficial effects of the disclosure are as follows:

the method decomposes the fluctuation power of the wind turbine into fluctuation components with different frequencies by using variational modal decomposition, constructs shallow and deep neural network models to perform refined prediction on the low-frequency and high-frequency fluctuation components respectively, and performs wind power fluctuation stabilization by using the prediction result as an instruction signal of a hybrid energy storage element. According to the fine power prediction model, wind power fluctuation components can be accurately predicted, the instruction signal of the energy storage device is the result of fine power prediction, the accuracy of wind power stabilization can be further improved, and the reliability of wind power grid connection is guaranteed.

Drawings

Fig. 1 is an overall flowchart of a method for suppressing wind power fluctuation by hybrid energy storage in the embodiment of the present disclosure.

Detailed Description

The present disclosure provides a method, an apparatus, an electronic device, and a storage medium for suppressing wind power fluctuation by hybrid energy storage, and the following describes technical solutions of the present disclosure in detail with reference to the accompanying drawings and embodiments.

An embodiment of the first aspect of the disclosure provides a method for suppressing wind power fluctuation by hybrid energy storage, an overall process is shown in fig. 1, and the method includes the following steps:

s1, collecting original power and wind speed data of any wind turbine during actual operation, wherein in the embodiment, the time range of data collection is at least 1 year, and the data sampling time interval is at least 30 min;

and S2, filtering the original power of the wind turbine generator by adopting first-order low-pass filtering, taking the filtering result as the wind power grid-connected power of the wind turbine generator, and subtracting the corresponding grid-connected power from the original power of the wind turbine generator to obtain the wind power fluctuation power of the wind turbine generator. Wherein, the time constant of the first-order low-pass filter in the embodiment of the disclosure is 10 min.

S3, determining the number of variational modal decomposition by adopting a center frequency method, and specifically executing the steps of:

initializing, and enabling the number K of the variational modal decomposition to be 2;

performing variation modal decomposition on the wind power fluctuation power to obtain K modal components;

thirdly, calculating the center frequency of each modal component;

and fourthly, judging whether the K modal components have repeated center frequencies. If not, making K equal to K +1, and repeating the step two to four; if so, outputting the optimal number of variational modal decomposition as K-1.

S4, decomposing the wind power fluctuation power by using a variational modal decomposition algorithm according to the optimal variational modal decomposition number determined in the step S3 to obtain a plurality of modal components of the wind power fluctuation power;

and S5, according to the size of the central frequency and a set threshold value, dividing each modal component of the wind power fluctuation power into a low-frequency fluctuation component and a high-frequency fluctuation component. In a specific embodiment of the present disclosure, the classification criteria of the modal components are: if the center frequency of the modal component is less than 1.66 × 10^-3If the modal component is a low-frequency fluctuation component; if the center frequency of the modal component is 1.66 × 10 or more^-3Then the modal component is a high frequency fluctuation component.

S6, establishing a corresponding low-frequency fluctuation component prediction model for each low-frequency fluctuation component, and establishing a corresponding high-frequency fluctuation component prediction model for each high-frequency fluctuation component;

the low-frequency fluctuation component prediction model is a shallow neural network model. The low-frequency fluctuation component prediction model in one embodiment of the disclosure comprises a convolution layer, a pooling layer, two LSTM layers, a Dropout layer, an attention mechanism layer and a full connection layer which are connected in sequence. The parameters of each layer of the low-frequency fluctuation component prediction model are shown in table 1:

TABLE 1 Low frequency fluctuating component prediction model parameter Table in one embodiment of the present disclosure

Name (R)	Parameter(s)
		Convolutional layer	Number of convolution kernels: 32, convolution kernel size: 2
Pooling layer	The size of the pooling window: 2
		LSTM layer	Number of neurons: 128
LSTM layer	Number of neurons: 64
		Dropout layer	Random inactivation ratio: 0.3
Attention mechanism layer	Using additive or multiplicative attention
		Full connecting layer (output layer)	Number of neurons: 1

The high-frequency fluctuation component prediction model is a deep neural network model. The high-frequency fluctuation component prediction model in one embodiment of the disclosure comprises a convolution layer, a pooling layer, three LSTM layers, a Dropout layer, an attention mechanism layer and a full connection layer which are connected in sequence. The parameters of each layer of the high-frequency fluctuation component prediction model are shown in table 2:

TABLE 2 high frequency fluctuation component prediction model parameter Table in one embodiment of the present disclosure

Name (R)	Parameter(s)
		Convolutional layer	Number of convolution kernels: 32, convolution kernel size: 2
Pooling layer	The size of the pooling window: 2
		LSTM layer	Number of neurons: 256
LSTM layer	Number of neurons: 128
		LSTM layer	Number of neurons: 64
Dropout layer	Random inactivation ratio: 0.3
		Attention mechanism layer	Using additive or multiplicative attention
Full connecting layer (output layer)	Number of neurons: 1

The parameters of the two prediction models are: the activation function is Relu, the loss function is an average absolute error function, the optimizer is Adam, and the initial learning rate of the network is 0.001.

S7, establishing a corresponding training sample set for each low-frequency fluctuation component prediction model, and training the low-frequency fluctuation component prediction model by using the training sample set until the loss function of the model is converged to obtain each trained low-frequency fluctuation component prediction model.

Each sample in the training sample set of each low-frequency fluctuation component prediction model comprises a corresponding low-frequency fluctuation component of the wind turbine generator at continuous N +1 sampling moments and wind speed data of the N +1 th sampling moment in the N +1 sampling moments; during training, the low-frequency fluctuation components corresponding to the first N sampling moments and the wind speed data at the (N + 1) th sampling moment in each sample are used as the model input, and the low-frequency fluctuation components corresponding to the (N + 1) th sampling moment (namely the predicted moment) in the sample are used as labels. In some embodiments of the present disclosure, N is generally 10.

S8, establishing a corresponding training sample set for each high-frequency fluctuation component prediction model, and training the high-frequency fluctuation component prediction model by using the training sample set until the loss function of the model is converged to obtain each trained high-frequency fluctuation component prediction model.

Each sample in the training sample set of each high-frequency fluctuation component prediction model comprises a corresponding high-frequency fluctuation component of the wind turbine at continuous N +1 sampling moments and wind speed data of the N +1 th sampling moment in the N +1 sampling moments; during training, the corresponding high-frequency fluctuation components of the first N sampling moments and the wind speed data of the (N + 1) th sampling moment in each sample are used as model input, and the corresponding high-frequency fluctuation components of the (N + 1) th sampling moment (namely the predicted moment) in the sample are used as labels. In some embodiments of the present disclosure, N is generally 10.

(in the embodiment of the disclosure, the construction and training of the low-frequency fluctuation component prediction model and the high-frequency fluctuation component prediction model are implemented by using a keras deep learning toolkit in python programming language.

S9, obtaining the prediction results of each low-frequency fluctuation component and each high-frequency fluctuation component of the wind turbine generator; the specific method comprises the following steps:

acquiring the original power of the wind turbine generator in actual operation at N continuous moments before the moment to be predicted;

repeating the step S2 to obtain the wind power fluctuation power of the wind turbine generator at the N continuous moments;

repeating the steps S4-S5 to obtain each modal component of the wind power fluctuation power at the N continuous moments and dividing the modal component into a low-frequency fluctuation component and a high-frequency fluctuation component;

acquiring wind speed data at the (N + 1) th moment;

inputting any low-frequency fluctuation component of N continuous moments and wind speed data of the (N + 1) th moment into a corresponding low-frequency fluctuation component prediction model to obtain a prediction result of the low-frequency fluctuation component of the wind turbine generator at the (N + 1) th moment (namely, the moment to be predicted).

Inputting any high-frequency fluctuation component of N continuous moments and wind speed data of the (N + 1) th moment into a corresponding high-frequency fluctuation component prediction model to obtain a prediction result of the wind turbine generator at the (N + 1) th moment (namely the moment to be predicted).

S10, the sum of the prediction results of the low-frequency fluctuation components is used as a command signal of the storage battery energy storage element, the sum of the prediction results of the high-frequency fluctuation components is used as a command signal of the super capacitor energy storage element, and the two types of fluctuation power components are stabilized. Wherein, above-mentioned two components are the extra energy memory of supplementary wind turbine generator system.

The specific method comprises the following steps: when the command signal is positive, the energy storage device (comprising a storage battery energy storage element and a stage capacitor energy storage element) is charged, and the charging power is the magnitude of the corresponding command signal; when the command signal is negative, the energy storage device discharges, and the discharge power is the magnitude of the corresponding command signal.

In order to achieve the above embodiments, an embodiment of a second aspect of the present disclosure provides a hybrid energy storage wind power fluctuation suppression device, including:

the acquiring module is used for acquiring wind power fluctuation power of the wind turbine generator at a plurality of continuous moments before the moment to be predicted and wind speed data of the moment to be predicted;

the variation modal decomposition module is used for decomposing the wind power fluctuation power at multiple moments according to a preset variation modal decomposition number to obtain multiple modal components of the wind power fluctuation power at multiple moments;

the fluctuation component dividing module is used for dividing the plurality of modal components into corresponding low-frequency fluctuation components or high-frequency fluctuation components respectively;

the prediction module is used for correspondingly inputting the same fluctuation component of the moments and the wind speed data of the moments to be predicted into a preset low-frequency fluctuation component prediction model and a preset high-frequency fluctuation component prediction model so as to obtain the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component of the wind turbine generator set;

and the stabilizing module is used for taking the prediction results of the low-frequency fluctuation component and the high-frequency fluctuation component as instruction signals of the energy storage device so as to realize the stabilization of the power fluctuation of the wind turbine generator.

In order to achieve the above embodiments, an embodiment of a third aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a hybrid energy storage wind power fluctuation suppression method as described above.

In order to achieve the above embodiments, a fourth aspect of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to execute a hybrid energy storage wind power fluctuation suppression method according to the above embodiments.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which when executed by the electronic device, cause the electronic device to execute a hybrid energy storage wind power fluctuation suppression method of the above embodiments.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.