Charging strategy determination method, device, electronic device and non-volatile storage medium

Abstract

The present application discloses a charging strategy determination method, device, electronic device and non-volatile storage medium. The method includes: obtaining wind power output data and photovoltaic output data within multiple time periods; determining the total cross-correlation coefficient between every two different time periods based on the wind power output data and photovoltaic output data; clustering the time periods based on the total cross-correlation coefficient between the time periods to obtain multiple clusters; determining the risk of wind and solar power abandonment based on the wind power output and photovoltaic output corresponding to each sampling moment in each typical output scenario in the wind power output data and photovoltaic output data, and adjusting the charging time and power of the electric vehicle in the charging strategy to minimize the risk of wind and solar power abandonment and obtain the target charging strategy. The present application solves the technical problems of power waste and heavy load in the distribution network caused by the lack of consideration of the output characteristics of new energy power generation in the charging strategy of electric vehicles in the related art.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a charging strategy determination method, device, electronic device and non-volatile storage medium. Background Art

[0002] Electric vehicle charging security is an important guarantee for accelerating the promotion and application of electric vehicles, and it is also the premise and foundation for the development of electric vehicles. With the popularization of electric vehicles, the demand for charging facilities and electricity continues to grow, and more intelligent and efficient charging strategies are needed to meet the charging needs of electric vehicles.

[0003] On the one hand, the charging service time and charging power demand of electric vehicles are uncertain. If a large number of electric vehicles are charged in a concentrated manner during peak load periods, the increased load demand will increase the burden on the power system and increase network losses; on the other hand, wind power and photovoltaic power generation, as new energy sources, may lead to wind and solar power abandonment in power grid dispatching and power markets due to the fluctuation of new energy power generation output, resulting in power waste and economic losses.

[0004] How to optimize the electric vehicle charging strategy, and then coordinate the output characteristics of renewable energy power generation and the load characteristics of electric vehicles, reduce the impact of renewable energy power generation and electric vehicle access on the distribution network, improve the overall load characteristics of the distribution network, and achieve the greatest economic and social benefits, is an important issue that needs to be solved urgently.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a charging strategy determination method, device, electronic device and non-volatile storage medium to at least solve the technical problems of power waste and heavy load in the distribution network caused by the fact that the charging strategy of electric vehicles in the related technology lacks consideration of the output characteristics of new energy power generation.

[0007] According to one aspect of an embodiment of the present application, a charging strategy determination method is provided, including: obtaining wind power output data and photovoltaic output data within multiple time periods, wherein each time period includes multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length, and in the wind power output data and the photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output; based on the wind power output data and the photovoltaic output data, determining the total cross-correlation coefficient between each two different time periods, wherein the total cross-correlation coefficient is used to characterize the wind power output characteristics and photovoltaic output characteristics corresponding to the two time periods in the time series The similarity on the time periods is determined; according to the total cross-correlation coefficient between the time periods, the time periods are clustered to obtain multiple clusters, wherein each cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of renewable energy power generation in different time periods; according to the wind power output data and photovoltaic output data, the wind power output and photovoltaic output corresponding to each sampling moment under each typical output scenario are used to determine the risk of wind and photovoltaic abandonment, and the charging time and power of the electric vehicle in the charging strategy are adjusted to minimize the risk of wind and photovoltaic abandonment, and the target charging strategy is obtained, wherein the risk of wind and photovoltaic abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0008] Optionally, determining the total cross-correlation number between every two different time periods based on the wind power output data and the photovoltaic output data includes: performing zero-padding processing on the wind power output data and the photovoltaic output data, wherein the number of data sample points in each time period in the wind power output data and the photovoltaic output data after the zero-padding processing is the same; determining a first cross-correlation number between every two different time periods based on the wind power output data after the zero-padding processing, and determining a second cross-correlation number between every two different time periods based on the photovoltaic output data after the zero-padding processing, using Fourier transform; and determining the total cross-correlation number based on the first cross-correlation number and the second cross-correlation number.

[0009] Optionally, clustering the time periods based on the total cross-correlation numbers between the time periods includes: selecting a preset number of centroids from the data points of the initial cluster, wherein each data point corresponds to a time period; dividing the initial cluster into a preset number of cluster clusters based on the total cross-correlation numbers between the time period corresponding to each data point and the time period corresponding to the centroid; re-determining a preset number of new centroids in each cluster cluster, and determining the differentiation priority corresponding to each cluster cluster, wherein the differentiation priority is used to characterize the priority of a new round of cluster cluster division; dividing the cluster clusters in order of differentiation priority from high to low according to the new centroids, and repeating the above process of determining new centroids and dividing the cluster clusters according to the differentiation priorities until the number of iterations reaches a preset threshold.

[0010] Optionally, determining the differentiation priority corresponding to each clustering cluster includes: determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to the centroid before the clustering cluster differentiation, to obtain a first parameter; determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to each new centroid re-determined in the clustering cluster, to obtain a second parameter; determining the product of the preset number and the first parameter as a third parameter, and determining the differentiation priority corresponding to the clustering cluster based on the ratio of the third parameter to the second parameter.

[0011] Optionally, determining the risk of wind and solar power abandonment includes: determining, based on wind power output data and photovoltaic output data, the wind power output, photovoltaic output, and electricity demand corresponding to each sampling moment under a typical output scenario; determining, based on the wind power output, photovoltaic output, electricity demand, and the real-time electricity price corresponding to the sampling moment, the risk cost of wind and solar power abandonment corresponding to the typical output scenario; determining the risk of wind and solar power abandonment based on the scenario occurrence probability corresponding to each typical output scenario and the risk cost of wind and solar power abandonment corresponding to the typical output scenario.

[0012] Optionally, the method also includes: determining the difference in battery charge between every two adjacent sampling moments under a typical output scenario, and determining the battery loss risk cost based on the difference; determining the battery loss risk based on the scenario occurrence probability corresponding to each typical output scenario and the battery loss risk cost; determining the charging price risk cost corresponding to the typical output scenario based on the power demand corresponding to each sampling moment under the typical output scenario and the real-time electricity price, and determining the charging price risk based on the scenario occurrence probability corresponding to each typical output scenario and the charging price risk cost; using the risk of wind and solar power abandonment, battery loss risk, and charging price risk as objective functions, a multi-objective optimization algorithm is used to optimize the charging strategy to obtain a target charging strategy.

[0013] Optionally, the method also includes: determining the execution confidence of the target charging strategy based on the estimated number of charging users and the actual number of charging users corresponding to each sampling moment within a time period, wherein the execution confidence is used to characterize the proportion of customers complying with the target charging strategy for charging; when the execution confidence is lower than a preset confidence threshold, readjusting the target charging strategy.

[0014] According to another aspect of the embodiment of the present application, a charging strategy determination device is also provided, including: a data acquisition module, used to acquire wind power output data and photovoltaic output data within multiple time periods, wherein each time period contains multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output; a correlation number determination module is used to determine the total cross-correlation number between each two different time periods based on the wind power output data and the photovoltaic output data, wherein the total cross-correlation number is used to characterize the wind power output characteristics and photovoltaic output characteristics corresponding to the two time periods in time. The similarity degree of the sequence; a clustering analysis module, which is used to cluster the time periods according to the total cross-correlation coefficient between the time periods to obtain multiple cluster clusters, wherein each cluster cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of new energy power generation in different time periods; a strategy optimization module, which is used to determine the risk of wind and photovoltaic abandonment according to the wind power output and photovoltaic output corresponding to each sampling moment in each typical output scenario in the wind power output data and photovoltaic output data, and adjust the charging time and power of electric vehicles in the charging strategy to minimize the risk of wind and photovoltaic abandonment, and obtain the target charging strategy, wherein the risk of wind and photovoltaic abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0015] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the charging strategy determination method is executed when the program is run.

[0016] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, the non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the charging strategy determination method by running the computer program.

[0017] In an embodiment of the present application, wind power output data and photovoltaic output data within multiple time periods are obtained, wherein each time period contains multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output; based on the wind power output data and photovoltaic output data, the total cross-correlation coefficient between each two different time periods is determined, wherein the total cross-correlation coefficient is used to characterize the similarity of the wind power output characteristics and photovoltaic output characteristics corresponding to the two time periods in the time series; based on the total cross-correlation coefficient between the time periods, the time periods are clustered to obtain multiple cluster clusters, wherein each cluster cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of new energy power generation in different time periods; based on the total cross-correlation coefficient between the time periods, the time periods are clustered to obtain multiple cluster clusters, wherein each cluster cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of new energy power generation in different time periods; based on According to the wind power output data and photovoltaic output data, the wind power output and photovoltaic output corresponding to each sampling moment under each typical output scenario are used to determine the risk of wind and photovoltaic abandonment, and the charging time and power of electric vehicles in the charging strategy are adjusted to minimize the risk of wind and photovoltaic abandonment, and obtain the target charging strategy. The risk of wind and photovoltaic abandonment refers to the risk of wasting power resources due to the fact that the power generated by wind and photovoltaic power generation exceeds the power demand. By collecting and analyzing wind and photovoltaic power generation data within a certain time period, the clustering algorithm is used to identify the typical output scenarios of new energy power generation, and based on the typical output scenarios, the risk of wind and photovoltaic abandonment is calculated. The electric vehicle charging strategy is optimized with the risk of wind and photovoltaic abandonment as the objective function, achieving the purpose of maximizing the use of new energy and reducing the wind and photovoltaic abandonment while ensuring the charging needs of electric vehicles. This solves the technical problems of power waste and heavy load in the distribution network caused by the lack of consideration of the output characteristics of new energy power generation in the charging strategy of electric vehicles in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for determining a charging strategy according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a method flow for determining a charging strategy according to an embodiment of the present application;

[0021] Figure 3It is a schematic diagram of a method flow for charging an electric vehicle taking into account the risk of wind and solar power abandonment provided in an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a charging curve corresponding to an optimized charging strategy provided in an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of a charging curve corresponding to disordered charging provided in an embodiment of the present application;

[0024] Figure 6 It is a structural schematic diagram of a charging strategy determination device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Electric vehicle charging security is an important guarantee for accelerating the promotion and application of electric vehicles, and it is also the premise and foundation for the development of electric vehicles. With the popularization of electric vehicles, the demand for charging facilities and electricity continues to grow, and more intelligent and efficient charging strategies are needed to meet the charging needs of electric vehicles.

[0028] On the one hand, the charging service time and charging power demand of electric vehicles are uncertain. If a large number of electric vehicles are charged in a concentrated manner during peak load periods, the increased load demand will increase the burden on the power system and increase network losses; on the other hand, wind power and photovoltaic power generation, as new energy sources, may lead to wind and solar power abandonment in power grid dispatching and power markets due to the fluctuation of new energy power generation output, resulting in power waste and economic losses.

[0029] How to optimize the electric vehicle charging strategy, and then coordinate the output characteristics of renewable energy power generation and the load characteristics of electric vehicles, reduce the impact of renewable energy power generation and electric vehicle access on the distribution network, improve the overall load characteristics of the distribution network, and achieve the greatest economic and social benefits, is an important issue that needs to be solved urgently.

[0030] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0031] According to an embodiment of the present application, an embodiment of a method for determining a charging strategy is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal (or electronic device) for implementing a charging strategy determination method. Figure 1 As shown, the computer terminal 10 (or electronic device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As involved in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the charging strategy determination method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned charging strategy determination method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0037] Under the above operating environment, the embodiment of the present application provides a charging strategy determination method. Figure 2 is a schematic diagram of a method flow for determining a charging strategy according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0038] Step S202, obtaining wind power output data and photovoltaic output data in multiple time periods, wherein each time period includes multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output;

[0039] Step S204, determining the total cross-correlation coefficient between every two different time periods based on the wind power output data and the photovoltaic output data, wherein the total cross-correlation coefficient is used to characterize the similarity between the wind power output characteristics and the photovoltaic output characteristics corresponding to the two time periods in the time series;

[0040] Step S206, clustering the time periods according to the total cross-correlation coefficients between the time periods to obtain a plurality of clusters, wherein each cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of renewable energy power generation in different time periods;

[0041] Step S208, based on the wind power output data and photovoltaic output data corresponding to each sampling moment under each typical output scenario, determine the risk of wind and solar power abandonment, and adjust the charging time and power of the electric vehicle in the charging strategy to minimize the risk of wind and solar power abandonment, and obtain a target charging strategy, wherein the risk of wind and solar power abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0042] Through the above steps, by collecting and analyzing wind and photovoltaic power generation data within a certain time period, the clustering algorithm is used to identify the typical output scenarios of new energy power generation, and based on the typical output scenarios, the risk of wind and solar power abandonment is calculated. The electric vehicle charging strategy is optimized with the risk of wind and solar power abandonment as the objective function, thereby achieving the purpose of maximizing the use of new energy and reducing the wind and solar power abandonment while ensuring the charging needs of electric vehicles, thereby solving the technical problems of power waste and heavy load in the distribution network caused by the lack of consideration of the output characteristics of new energy power generation in the charging strategy of electric vehicles in related technologies.

[0043] The following further introduces the charging strategy determination method in steps S202 to S208 of the embodiment of the present application.

[0044] Figure 3 is a schematic diagram of a method flow for charging an electric vehicle taking into account the risk of wind and solar power abandonment provided in an embodiment of the present application, such as Figure 3 shown.

[0045] First, wind power output data and photovoltaic output data within a certain period of time are collected, and the two are aligned in time series to form a multidimensional matrix with both wind power output characteristics and photovoltaic output characteristics, that is, wind power output data and photovoltaic output data within multiple time periods are obtained.

[0046] The above multidimensional matrix contains a variety of information, including but not limited to: the number of samples of data sample points, wind power output characteristics, photovoltaic output characteristics, time series characteristics, etc., as shown in the following formula:

[0047]

[0048] Wherein, D is a multidimensional matrix, n represents the number of time periods contained in the sample (in this embodiment, one time period is described by taking one day as an example), feature1 corresponds to the wind power output feature, feature2 corresponds to the photovoltaic output feature, time series is the time series data with an interval of a preset time length (taking 15 minutes as an example), that is, the data corresponding to a data sample point, the wind power and photovoltaic output at a specific time point, there are 24 hours in a day, and there are 4 data points with an interval of 15 minutes per hour, then there will be 96 sample points (24 * 4 = 96) per day, which means that in this embodiment, for each day, there will be data at 96 time points (sampling moments), so as to be able to describe in detail the changes in wind power and photovoltaic output within a day.

[0049] The data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment. For example, the matrix D can contain relevant characteristics of wind power output, such as wind speed, wind direction, wind power generation, etc., which can be used to analyze the pattern and trend of wind power output; it can also contain relevant characteristics of photovoltaic power generation, such as sunshine intensity, photovoltaic panel temperature, photovoltaic power generation, etc., to facilitate the study of the performance of photovoltaic power generation; in addition, in addition to the output characteristics of wind power and photovoltaic, the matrix D can also contain time-related characteristics, such as whether it is a peak power consumption period, seasonal factors, etc. These characteristics are helpful to analyze the energy output pattern in different time periods.

[0050] The multidimensional matrix D provides a rich data foundation for analyzing and predicting wind power and photovoltaic output, so that clustering algorithms, risk assessment methods and optimization algorithms can be applied to develop more efficient and economical electric vehicle charging strategies. Through in-depth analysis of these data, the output mode of new energy can be better understood and predicted, thereby optimizing the dispatch of power grids and the charging plan of electric vehicles, reducing the phenomenon of wind and light abandonment, and improving energy utilization efficiency. For example, in this embodiment, the MOSBD clustering algorithm can be combined with the binary Kmeans clustering algorithm considering priority, and the typical scenarios of new energy output can be clustered according to the frequency domain characteristics of the wind output curve and the photovoltaic output curve, which will be further introduced below.

[0051] After obtaining the wind power output data and the photovoltaic output data, the total cross-correlation coefficient between every two different time periods may be determined based on the wind power output data and the photovoltaic output data. The specific steps are as follows.

[0052] In some embodiments of the present application, determining the total cross-correlation number between every two different time periods based on the wind power output data and the photovoltaic output data includes the following steps: performing zero-padding processing on the wind power output data and the photovoltaic output data, wherein the number of data sample points in each time period in the wind power output data and the photovoltaic output data after the zero-padding processing is the same; determining a first cross-correlation number between every two different time periods based on the wind power output data after the zero-padding processing, and determining a second cross-correlation number between every two different time periods based on the photovoltaic output data after the zero-padding processing, using Fourier transform; and determining the total cross-correlation number based on the first cross-correlation number and the second cross-correlation number.

[0053] The MOSBD algorithm is a clustering method that pays more attention to the curve shape features. It eliminates the influence of the curve phase difference on the clustering results through time-frequency domain conversion. It includes the following steps: padding the time series data of all features with zeros to facilitate the subsequent Fourier transform (Fast Fourier Transform, FFT); determining the minimum clustering unit and calculating the cross-correlation coefficients of different units; compressing the feature dimension to form a matrix that can be divided into two K-means clusters. The process of calculating the cross-correlation coefficient is shown in the following formula:

[0054]

[0055]

[0056] Among them, i and j represent the i-th day and j-th day respectively, l represents the l-th feature, represents the cross-correlation between the lth feature on the i-th day and the j-th day, that is, the first cross-correlation and the second cross-correlation mentioned above, F( ) represents the fast Fourier transform operation, represents the inverse operation of Fourier transform, represents the time series data of the lth feature on the i-th day, represents the time series data of the lth feature on the jth day, the asterisk (*) represents the complex conjugate, m is the total number of features, which is 2 in this embodiment (i.e., wind power output features and photovoltaic output features), It represents the total cross-correlation between the features of the i-th day and the j-th day, that is, the total cross-correlation between two different time periods.

[0057] After obtaining the total cross-correlation coefficients between the various time periods, the typical output scenarios of new energy sources can be determined by clustering the time periods. The specific steps are as follows.

[0058] In some embodiments of the present application, clustering time periods based on the total cross-correlation number between time periods includes the following steps: selecting a preset number of centroids from the data points of the initial cluster, wherein each data point corresponds to a time period; dividing the initial cluster into a preset number of cluster clusters based on the total cross-correlation number between the time period corresponding to each data point and the time period corresponding to the centroid; re-determining a preset number of new centroids in each cluster cluster, and determining the differentiation priority corresponding to each cluster cluster, wherein the differentiation priority is used to characterize the priority of a new round of cluster cluster division; dividing the cluster clusters in order of differentiation priority from high to low according to the new centroids, and repeating the above process of determining new centroids and dividing the cluster clusters according to the differentiation priority until the number of iterations reaches a preset threshold.

[0059] Specifically, in this embodiment, a binary K-means algorithm can be used for clustering (i.e., the above-mentioned preset number is 2). The binary K-means algorithm is a decentralized clustering algorithm, which reduces the influence of the central cluster selection on the result. However, the traditional binary K-means algorithm does not worry about the order of cluster differentiation, while the binary K-means differentiation priority is considered in the embodiment of the present application, that is, the differentiation priority corresponding to each cluster is determined, and the order of cluster differentiation is determined in order from high to low priority, wherein the specific steps for determining the differentiation priority corresponding to the cluster are as follows.

[0060] In some embodiments of the present application, determining the differentiation priority corresponding to each clustering cluster includes the following steps: determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to the centroid before the clustering cluster differentiation, to obtain a first parameter; determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to each new centroid re-determined in the clustering cluster, to obtain a second parameter; determining the product of the preset number and the first parameter as a third parameter, and determining the differentiation priority corresponding to the clustering cluster based on the ratio of the third parameter to the second parameter.

[0061] Specifically, differentiation priorities The calculation formula is as follows:

[0062]

[0063] Among them, b is the cluster number, B is the total number of clusters; bi is the individual number of the b-th cluster, BI is the number of individuals in the b-th cluster; curcenter is the cluster center before differentiation, curcenter1 and curcenter2 are the cluster centers after differentiation, and bruch is the matrix that records cluster information.

[0064] After summarizing the typical output scenarios of new energy through clustering algorithms, the risk assessment method can be used to calculate the risks of wind and solar power abandonment, battery loss risk, and charging electricity price risk. The specific steps are as follows.

[0065] In some embodiments of the present application, determining the risk of wind and solar power abandonment includes the following steps: determining the wind power output, photovoltaic output, and power demand corresponding to each sampling moment under a typical output scenario based on wind power output data and photovoltaic output data; determining the risk cost of wind and solar power abandonment corresponding to the typical output scenario based on the wind power output, photovoltaic output, power demand, and the real-time electricity price corresponding to the sampling moment; determining the risk of wind and solar power abandonment based on the scenario occurrence probability corresponding to each typical output scenario and the risk cost of wind and solar power abandonment corresponding to the typical output scenario.

[0066] Specifically, the CvaR (Conditional Value at Risk) assessment method can be used to determine the risk of wind and solar power abandonment, as shown in the following formula:

[0067]

[0068]

[0069] in, To reduce the risk of wind and solar power abandonment, is the boundary value of the risk loss of wind and solar power abandonment, is the confidence level of the loss boundary value, which is set to 0.9 by default in this embodiment, k is the scene number, K is the total number of scenes, The risk cost of curtailing wind and solar power. is the scene probability, is the wind power output corresponding to time t (the tth sampling time), is the photovoltaic output corresponding to time t, is the power demand corresponding to time t, is the real-time electricity price corresponding to time t.

[0070] In some embodiments of the present application, the method also includes the following steps: determining the difference in battery charge between every two adjacent sampling moments under a typical output scenario, and determining the battery loss risk cost based on the difference; determining the battery loss risk based on the scenario occurrence probability corresponding to each typical output scenario and the battery loss risk cost; determining the charging price risk cost corresponding to the typical output scenario based on the power demand corresponding to each sampling moment under the typical output scenario and the real-time electricity price, and determining the charging price risk based on the scenario occurrence probability corresponding to each typical output scenario and the charging price risk cost; using the wind and solar power abandonment risk, battery loss risk, and charging price risk as objective functions, a multi-objective optimization algorithm is used to optimize the charging strategy to obtain a target charging strategy.

[0071] Specifically, the formula for determining battery loss risk is as follows:

[0072]

[0073]

[0074] in, , is the boundary value of battery loss risk, is the confidence level of the loss boundary value, which is set to 0.9 by default in this embodiment, k is the scene number, K is the total number of scenes, is the battery loss risk cost, is the scene probability, is the battery charge capacity corresponding to the t-1th sampling moment, is the battery charge amount corresponding to the t-th sampling moment, and bprice is the battery loss cost.

[0075] The formula for determining charging price risk is as follows:

[0076]

[0077]

[0078] in, To charge price risk, is the boundary value of charging price risk loss, is the confidence level of the loss boundary value, which is set to 0.9 by default in this embodiment, k is the scene number, K is the total number of scenes, For charging price risk cost, is the scene probability, is the real-time electricity price corresponding to time t, is the electricity demand corresponding to time t.

[0079] Afterwards, the three calculated risks can be used as the objective function, and the TRPSO (Trust Region Policy Optimization) algorithm can be used to optimize the electric vehicle charging strategy. Finally, an electric vehicle charging strategy that takes into account wind and solar power abandonment, battery loss, and charging prices can be summarized.

[0080] The above TRPSO algorithm optimizes the electric vehicle charging strategy with the risk of wind and solar power abandonment, battery loss risk, and charging price risk as the objective function, and obtains the electric vehicle charging Pareto surface (Pareto). The calculation formula of the TRPSO algorithm is as follows:

[0081] Among them, fitness(1) is the wind and solar power abandonment risk of the existing optimal solution, fitness(2) is the battery loss risk of the existing optimal solution, and fitness(3) is the charging price risk of the existing optimal solution.

[0082] In addition, the electric vehicle charging strategy can be further adjusted according to the charging compliance of electric vehicles in each time period, and a strategy that can make the charging strategy execution confidence higher than the set confidence threshold (for example, 90%) is selected from the Pareto surface. The specific steps are as follows.

[0083] In some embodiments of the present application, the method also includes the following steps: determining the execution confidence of the target charging strategy based on the estimated number of charging users and the actual number of charging users corresponding to each sampling moment within a time period, wherein the execution confidence is used to characterize the proportion of customers who comply with the target charging strategy for charging; when the execution confidence is lower than a preset confidence threshold, readjusting the target charging strategy.

[0084] Specifically, the calculation formula for execution confidence is as follows:

[0085]

[0086] in, Implement confidence for charging decisions, The number of charging users estimated for charging decisions, The actual number of charging users.

[0087] This application scheme makes the charging strategy more comprehensive and robust by comprehensively considering multiple risks such as wind and solar power abandonment, battery loss and charging electricity prices. Using clustering algorithms, time periods with similar output characteristics can be identified, providing a basis for subsequent risk assessment and strategy optimization. Using three risks as objective functions, an optimization algorithm is used to ensure that the optimal charging strategy is found under complex risk constraints. The Pareto surface is used to select high-confidence strategies, which improves the execution rate and reliability of the strategy. Figure 4 and Figure 5They are respectively the charging curves of optimized charging strategy and disordered charging, such as Figure 4 and Figure 5 As shown, the present application scheme can maximize the use of new energy while ensuring the charging needs of electric vehicles, reduce wind and solar power abandonment, reduce charging costs, and extend battery life.

[0088] According to an embodiment of the present application, an embodiment of a charging strategy determination device is also provided. Figure 6 is a schematic diagram of the structure of a charging strategy determination device provided according to an embodiment of the present application. Figure 6 As shown, the device comprises:

[0089] The data acquisition module 60 is used to acquire wind power output data and photovoltaic output data in multiple time periods, wherein each time period includes multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time. In the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output;

[0090] A correlation number determination module 62 is used to determine the total cross-correlation number between every two different time periods based on the wind power output data and the photovoltaic output data, wherein the total cross-correlation number is used to characterize the similarity of the wind power output characteristics and the photovoltaic output characteristics corresponding to the two time periods in the time series;

[0091] A cluster analysis module 64 is used to cluster the time periods according to the total cross-correlation coefficients between the time periods to obtain a plurality of clusters, wherein each cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, and is used to characterize the characteristics of renewable energy power generation in different time periods;

[0092] The strategy optimization module 66 is used to determine the risk of wind and solar power abandonment based on the wind power output and photovoltaic output corresponding to each sampling moment in each typical output scenario in the wind power output data and photovoltaic output data, and adjust the charging time and power of electric vehicles in the charging strategy to minimize the risk of wind and solar power abandonment and obtain the target charging strategy, wherein the risk of wind and solar power abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0093] Optionally, determining the total cross-correlation number between every two different time periods based on the wind power output data and the photovoltaic output data includes: performing zero-padding processing on the wind power output data and the photovoltaic output data, wherein the number of data sample points in each time period in the wind power output data and the photovoltaic output data after the zero-padding processing is the same; determining a first cross-correlation number between every two different time periods based on the wind power output data after the zero-padding processing, and determining a second cross-correlation number between every two different time periods based on the photovoltaic output data after the zero-padding processing, using Fourier transform; and determining the total cross-correlation number based on the first cross-correlation number and the second cross-correlation number.

[0094] Optionally, clustering the time periods based on the total cross-correlation numbers between the time periods includes: selecting a preset number of centroids from the data points of the initial cluster, wherein each data point corresponds to a time period; dividing the initial cluster into a preset number of cluster clusters based on the total cross-correlation numbers between the time period corresponding to each data point and the time period corresponding to the centroid; re-determining a preset number of new centroids in each cluster cluster, and determining the differentiation priority corresponding to each cluster cluster, wherein the differentiation priority is used to characterize the priority of a new round of cluster cluster division; dividing the cluster clusters in order of differentiation priority from high to low according to the new centroids, and repeating the above process of determining new centroids and dividing the cluster clusters according to the differentiation priorities until the number of iterations reaches a preset threshold.

[0095] Optionally, determining the differentiation priority corresponding to each clustering cluster includes: determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to the centroid before the clustering cluster differentiation, to obtain a first parameter; determining the sum of the total cross-correlation numbers between the time period corresponding to each data point in the clustering cluster and the time period corresponding to each new centroid re-determined in the clustering cluster, to obtain a second parameter; determining the product of the preset number and the first parameter as a third parameter, and determining the differentiation priority corresponding to the clustering cluster based on the ratio of the third parameter to the second parameter.

[0096] Optionally, determining the risk of wind and solar power abandonment includes: determining, based on wind power output data and photovoltaic output data, the wind power output, photovoltaic output, and electricity demand corresponding to each sampling moment under a typical output scenario; determining, based on the wind power output, photovoltaic output, electricity demand, and the real-time electricity price corresponding to the sampling moment, the risk cost of wind and solar power abandonment corresponding to the typical output scenario; determining the risk of wind and solar power abandonment based on the scenario occurrence probability corresponding to each typical output scenario and the risk cost of wind and solar power abandonment corresponding to the typical output scenario.

[0097] Optionally, the strategy optimization module 66 is also used to: determine the difference in battery charge between every two adjacent sampling moments under a typical output scenario, and determine the battery loss risk cost based on the difference; determine the battery loss risk based on the scenario occurrence probability corresponding to each typical output scenario and the battery loss risk cost; determine the charging price risk cost corresponding to the typical output scenario based on the power demand corresponding to each sampling moment under the typical output scenario and the real-time electricity price, and determine the charging price risk based on the scenario occurrence probability corresponding to each typical output scenario and the charging price risk cost; use the wind and solar power abandonment risk, battery loss risk, and charging price risk as objective functions, adopt a multi-objective optimization algorithm, optimize the charging strategy, and obtain a target charging strategy.

[0098] Optionally, the strategy optimization module 66 is also used to: determine the execution confidence of the target charging strategy based on the estimated number of charging users and the actual number of charging users corresponding to each sampling moment within the time period, wherein the execution confidence is used to characterize the proportion of customers complying with the target charging strategy for charging; and readjust the target charging strategy when the execution confidence is lower than a preset confidence threshold.

[0099] It should be noted that the various modules in the above-mentioned charging strategy determination device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0100] It should be noted that the charging strategy determination device provided in this embodiment can be used to execute Figure 2 The charging strategy determination method shown, therefore, the relevant explanations and instructions on the above charging strategy determination method are also applicable to the embodiments of the present application and will not be repeated here.

[0101] The embodiment of the present application also provides a non-volatile storage medium, the non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following charging strategy determination method by running the computer program: obtaining wind power output data and photovoltaic output data within multiple time periods, wherein each time period includes multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length, in the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output, photovoltaic output; based on the wind power output data and photovoltaic output data, determining the total cross-correlation coefficient between each two different time periods, wherein the total cross-correlation coefficient is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment. The similarity of the wind power output characteristics and photovoltaic output characteristics corresponding to the period in the time series; clustering the time periods according to the total cross-correlation coefficient between the time periods to obtain multiple clustering clusters, wherein each clustering cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, which is used to characterize the characteristics of renewable energy power generation in different time periods; determining the risk of wind and photovoltaic abandonment based on the wind power output and photovoltaic output corresponding to each sampling moment under each typical output scenario in the wind power output data and photovoltaic output data, and adjusting the charging time and power of electric vehicles in the charging strategy to minimize the risk of wind and photovoltaic abandonment, and obtaining the target charging strategy, wherein the risk of wind and photovoltaic abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0102] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the charging strategy determination method described in each embodiment of the present application: obtaining wind power output data and photovoltaic output data within multiple time periods, wherein each time period contains multiple data sample points, and the sampling moments corresponding to each data sample point are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each data sample point is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample point includes: wind power output and photovoltaic output; based on the wind power output data and photovoltaic output data, determining the total cross-correlation coefficient between each two different time periods, wherein the total cross-correlation coefficient is used to characterize the wind power output characteristics and photovoltaic output characteristics corresponding to the two time periods. The similarity between wind power output characteristics and photovoltaic output characteristics in time series; clustering the time periods according to the total cross-correlation coefficient between the time periods to obtain multiple clusters, where each cluster corresponds to a typical output scenario, where the typical output scenario is a specific output mode of wind and photovoltaic power generation, which is used to characterize the characteristics of renewable energy power generation in different time periods; determining the risk of wind and photovoltaic abandonment based on the wind power output and photovoltaic output corresponding to each sampling moment under each typical output scenario in the wind power output data and photovoltaic output data, and adjusting the charging time and power of electric vehicles in the charging strategy to minimize the risk of wind and photovoltaic abandonment, and obtaining the target charging strategy, where the risk of wind and photovoltaic abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

[0103] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0104] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated unit is implemented in the form of a software functional unit 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc., which can store program code.

[0109] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A charging strategy determination method, characterized in that: include: Acquire wind power output data and photovoltaic output data in multiple time periods, wherein each of the time periods includes multiple data sample points, and the sampling moments corresponding to each of the data sample points are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each of the data sample points is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample points include: wind power output and photovoltaic output; Determining the total cross-correlation number between every two different time periods according to the wind power output data and the photovoltaic output data, including: performing zero-padding processing on the wind power output data and the photovoltaic output data, wherein the number of data sample points in each time period in the wind power output data and the photovoltaic output data after the zero-padding processing is the same; determining a first cross-correlation number between every two different time periods according to the wind power output data after the zero-padding processing, and determining a second cross-correlation number between every two different time periods according to the photovoltaic output data after the zero-padding processing by means of Fourier transform; determining the total cross-correlation number according to the first cross-correlation number and the second cross-correlation number, wherein the total cross-correlation number is used to characterize the similarity degree of the wind power output characteristics and the photovoltaic output characteristics corresponding to the two time periods in time series; Clustering the time periods according to the total cross-correlation coefficients between the time periods to obtain a plurality of clusters, wherein each cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, and is used to characterize the characteristics of renewable energy power generation in different time periods; Based on the wind power output data and the photovoltaic output data, the wind power output and the photovoltaic output corresponding to each sampling moment under each typical output scenario, the risk of wind and solar power abandonment is determined, and the charging time and power of the electric vehicle in the charging strategy are adjusted to minimize the risk of wind and solar power abandonment, and a target charging strategy is obtained, wherein the risk of wind and solar power abandonment refers to the risk of wasting power resources due to the fact that the power generated by wind and photovoltaic power generation exceeds the power demand.

2. The charging strategy determination method according to claim 1, characterized in that: Clustering the time periods according to the total cross-correlation number between the time periods comprises: Selecting a preset number of centroids from the data points of the initial cluster, wherein each data point corresponds to one of the time periods; Dividing the initial cluster into the preset number of clusters according to the total cross-correlation number between the time period corresponding to each data point and the time period corresponding to the centroid; Re-determining the preset number of new centroids in each of the clusters, and determining a differentiation priority corresponding to each of the clusters, wherein the differentiation priority is used to represent the priority of a new round of division of the clusters; In the order of the differentiation priority from high to low, the clusters are divided in turn according to the new centroid, and the above process of determining the new centroid and dividing the clusters according to the differentiation priority is repeated until the number of iterations reaches a preset threshold.

3. The charging strategy determination method according to claim 2, characterized in that: Determining the differentiation priority corresponding to each of the clusters includes: Determine the sum of the total cross-correlation coefficients between the time period corresponding to each data point in the cluster and the time period corresponding to the centroid before differentiation of the cluster to obtain a first parameter; Determine the sum of the total cross-correlation numbers between the time period corresponding to each data point in the cluster and the time period corresponding to each new centroid re-determined in the cluster to obtain a second parameter; The product of the preset number and the first parameter is determined as a third parameter, and the differentiation priority corresponding to the cluster is determined according to a ratio of the third parameter to the second parameter.

4. The charging strategy determination method according to claim 1, characterized in that: Determining the risks of wind and solar power curtailment includes: Determine, according to the wind power output data and the photovoltaic output data, the wind power output, the photovoltaic output, and the power demand corresponding to each sampling moment in the typical output scenario; Determine the risk cost of wind and solar power abandonment corresponding to the typical output scenario according to the wind power output, the photovoltaic output, the power demand, and the real-time electricity price corresponding to the sampling time; The wind and solar power abandonment risks are determined according to the scenario occurrence probabilities corresponding to the typical output scenarios and the wind and solar power abandonment risk costs corresponding to the typical output scenarios.

5. The charging strategy determination method according to claim 4, characterized in that: The method further comprises: Determine the difference in battery charge between every two adjacent sampling moments in the typical output scenario, and determine the battery loss risk cost based on the difference; Determine the battery loss risk according to the scenario occurrence probability corresponding to each of the typical output scenarios and the battery loss risk cost; Determine the charging price risk cost corresponding to the typical output scenario according to the power demand corresponding to each sampling moment under the typical output scenario and the real-time electricity price, and determine the charging price risk according to the scenario occurrence probability corresponding to each typical output scenario and the charging price risk cost; The risk of wind and solar power abandonment, the battery loss risk, and the charging price risk are used as objective functions, and a multi-objective optimization algorithm is adopted to optimize the charging strategy to obtain the target charging strategy.

6. The charging strategy determination method according to claim 5, characterized in that: The method further comprises: Determine the execution confidence of the target charging strategy according to the estimated number of charging users and the actual number of charging users corresponding to each sampling time within the time period, wherein the execution confidence is used to characterize the proportion of customers complying with the target charging strategy for charging; When the execution confidence is lower than a preset confidence threshold, the target charging strategy is readjusted.

7. A charging strategy determination device, characterized in that: include: A data acquisition module, used to acquire wind power output data and photovoltaic output data within a plurality of time periods, wherein each of the time periods includes a plurality of data sample points, and the sampling moments corresponding to each of the data sample points are separated by a preset time length. In the wind power output data and photovoltaic output data, the data corresponding to each of the data sample points is used to characterize the wind power output characteristics and photovoltaic output characteristics at the sampling moment, and the data corresponding to the data sample points include: wind power output and photovoltaic output; A correlation number determination module, used for determining the total cross-correlation number between every two different time periods according to the wind power output data and the photovoltaic output data, comprising: performing zero-padding processing on the wind power output data and the photovoltaic output data, wherein the number of data sample points in each time period in the wind power output data and the photovoltaic output data after the zero-padding processing is the same; using Fourier transform to determine the first cross-correlation number between every two different time periods according to the wind power output data after the zero-padding processing, and to determine the second cross-correlation number between every two different time periods according to the photovoltaic output data after the zero-padding processing; determining the total cross-correlation number according to the first cross-correlation number and the second cross-correlation number, wherein the total cross-correlation number is used to characterize the similarity degree of the wind power output characteristics and the photovoltaic output characteristics corresponding to the two time periods in time series; A cluster analysis module, configured to cluster the time periods according to the total cross-correlation coefficients between the time periods to obtain a plurality of clusters, wherein each cluster corresponds to a typical output scenario, wherein the typical output scenario is a specific output mode of wind power and photovoltaic power generation, and is used to characterize the characteristics of renewable energy power generation in different time periods; A strategy optimization module is used to determine the risk of wind and solar power abandonment based on the wind power output and photovoltaic output corresponding to each sampling moment in each typical output scenario in the wind power output data and the photovoltaic output data, and adjust the charging time and power of the electric vehicle in the charging strategy to minimize the risk of wind and solar power abandonment, and obtain a target charging strategy, wherein the risk of wind and solar power abandonment refers to the risk of wasting power resources due to the power generated by wind and photovoltaic power generation exceeding the power demand.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the charging strategy determination method according to any one of claims 1 to 6 is executed when the program is run.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the charging strategy determination method according to any one of claims 1 to 6 by running the computer program.

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