Self-adaptive flexible adjustment method and device for distributed new energy access and medium

Abstract

The invention provides a self-adaptive flexible adjustment method and device for distributed new energy access and a medium, and relates to the technical field of new energy access, and the method comprises the steps: according to the power consumption of each power consumption main body in each historical time period and the power generation amount of each wind power station in each historical time period in a preset region, determining the power consumption of each power consumption main body in each historical time period; predicting an electricity consumption time curve of each electricity consumption main body in the prediction time period and a generating capacity time curve of each wind power station in the prediction time period; if the ratio of the number of the power consumption main bodies in the preset area to the number of the wind power stations is smaller than a preset weight, the similarity between each power consumption time curve and each power generation capacity time curve is obtained, and the wind power station connected to each power consumption main body in the preset area in the prediction time period is determined according to the similarity; according to the method, the power consumption main body with relatively high power consumption in the prediction time period is matched with the wind power station with relatively high power generation capacity, so that the electric energy utilization rate of the wind power station is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy access technology, and in particular to an adaptive flexible adjustment method, device and medium for distributed new energy access. Background Art

[0002] The access of new energy power generation to the grid can maximize the use of clean energy, such as wind energy and solar energy, which are widely distributed, but the energy density is unevenly distributed. Through the power grid, these scattered energy sources can be concentrated and transported to various places to achieve efficient use of resources. At the same time, new energy power generation has the characteristics of low emissions and zero emissions. Compared with traditional thermal power generation and fossil energy, the access of new energy power generation to the grid can effectively reduce air pollution, reduce greenhouse gas emissions, improve environmental quality, and help achieve the goal of "carbon peak and carbon neutrality"; wind energy, as a kind of new energy, is greatly affected by geographical and weather conditions. In the existing technology, the power generated by wind power stations is usually directly connected to the grid all the time for users to use; however, there will be certain differences in the time of electricity demand of different users, and at the same time, there will be certain differences in the time of power generation of wind power stations. How to match users with wind power stations according to the electricity demand of different users and the power generation law of wind power stations to improve the utilization rate of wind power generation has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a method for adaptive flexible adjustment of distributed new energy access is provided, the method comprising the following steps:

[0005] S100, obtaining the power consumption of each power user in a preset area in each historical time period, so as to obtain a list set of historical power consumption of each power user A=(A 1 , A 2 , …, A i , …, A n ), i=1, 2,...,n; where, A i is the historical electricity consumption list of the electricity user corresponding to the i-th electricity user in the preset area, and n is the number of electricity users in the preset area; A i =(A 1，1 , A i，2 , …, A i，j , …, A i，m ), j = 1, 2, ..., m; A i，j is the electricity consumption of the ith electricity user in the jth historical time period, and m is the number of historical time periods.

[0006] S200, obtaining the power generation of each wind power station in a preset area in each historical time period to obtain a list set of wind power station power generation amounts B = (B 1 , B 2 , …, B p , …, B q ), p = 1, 2, ..., q; where B p is a list of wind power station power generation corresponding to the p-th wind power station in the preset area, and q is the number of wind power stations in the preset area; B p =(B p，1 , B p，2 , …, B p，j , …, B p，m );B p，j is the power generation of the pth wind power station in the jth historical time period.

[0007] S300, input A and B into the preset prediction model respectively, to obtain the time curve of power consumption of each power user in the preset prediction time period and the time curve of power generation of each wind power station in the preset prediction time period in the preset area, and then obtain the predicted power consumption time curve list QA corresponding to the power user = (QA 1 , QA 2 ,…,QA i ,…,QA n ) and the predicted power generation time curve list QB corresponding to the wind power station = (QB 1 , Q.B. 2 , …, QB p , …, QB q );Among them, QA i is the time curve of electricity consumption of the ith electricity consumer in the preset area within the forecast time, QB i It is the time curve of power generation of the p-th wind power station in the preset area within the prediction time period; the start time of the prediction time period is the current time.

[0008] S400, if n / q<α 1 , then obtain the first similarity between each power consumption time curve in QA and each power generation time curve in QB to obtain the first similarity list set corresponding to QA = (η 1 , η 2 ,…,η i ,…,η n ), where η i For QA i The corresponding first similarity list; n i =(η i，1 , η i，2 ,…,η i，p ,…,ηi，q );η i，p For QA i With QB p The similarity between 1 The preset weights.

[0009] S500: Determine, based on η, the wind power station to which each electricity user in the preset area is connected within the predicted time period.

[0010] Further, step S500 includes the following steps:

[0011] S510, obtaining the first target similarity corresponding to each electricity user in the preset area to obtain a first target similarity list η'=(η' 1 , η' 2 ,…,η' i ,…,η' n );wherein, η ' i is the first target similarity corresponding to the i-th electricity user in the preset area; η' i =MAX(η i ); MAX() is the preset maximum value function;

[0012] S520, η' i The corresponding wind power station is determined to be the wind power station connected to the i-th electricity user in the preset area within the predicted time period.

[0013] Furthermore, after step S400 and before step S500, the method further includes the following steps:

[0014] S410, if n / q ≥ α 1 , then the predicted power consumption corresponding to each preset time point on each predicted power consumption time curve in QA is obtained to obtain the predicted power consumption feature vector XA corresponding to QA = (XA 1 , XA 2 , …, XA i , …, XA n ), where XA i For QA i The corresponding predicted power consumption feature vector; XA i =(XA i，1 , XA i，2 , …, XA i，e , …, XA i，g ), e = 1, 2, ..., g; XA i，e For QA i The predicted power consumption corresponding to the e-th preset time point on , g is the number of preset time points corresponding to each predicted power consumption time curve in QA;

[0015] S420, clustering the predicted power consumption feature vector in XA using a preset clustering algorithm to obtain a cluster list λ=(λ 1 ,λ 2 , …, λ x , …, λ y ), x = 1, 2, ..., y; where λ x is the xth cluster obtained by clustering the predicted power consumption feature vector in XA, and y is the number of clusters obtained by clustering the predicted power consumption feature vector in XA;

[0016] S430, obtaining the average predicted power consumption time curve corresponding to each cluster in λ, so as to obtain the average predicted power consumption time curve list θ=(θ 1 ,θ 2 ,…,θ x ,…,θ y ), where θ x is x The corresponding average predicted electricity consumption time curve;

[0017] S440, obtaining a second similarity between each average predicted power consumption time curve in θ and each predicted power generation time curve in QB, so as to obtain a second similarity list set ε=(ε 1 , ε 2 , …, ε x , …, ε y ), where ε x is θ x The corresponding second similarity list; ε x =(ε x，1 , ε x，2 , …, ε x，p , …, ε x，q ); x，p is θ x With QB p The similarity between

[0018] S450, according to ε, determine the target second similarity corresponding to each average predicted power consumption time curve to obtain a target second similarity list ε'=(ε' 1 , ε' 2 , …, ε' x , …, ε' y ), where ε' x is θ x The corresponding target second similarity; ε' x =MAX(ε x ); MAX() is the preset maximum value function;

[0019] S460, ε' xThe corresponding wind power station is determined as λ x The corresponding wind power stations connected to each electricity user during the forecast period.

[0020] Furthermore, the preset clustering algorithm includes a K-means clustering algorithm.

[0021] Furthermore, the preset prediction model includes an LSTM model.

[0022] Furthermore, α 1 The value range is 2 to 5.

[0023] Furthermore, the time intervals between any two adjacent preset time points are the same.

[0024] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned adaptive flexible adjustment method for distributed new energy access.

[0025] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0026] The present invention has at least the following beneficial effects:

[0027] The adaptive flexible adjustment method for distributed renewable energy access of the present invention predicts the time curve of electricity consumption of each electricity user in the predicted time period and the time curve of power generation of each wind power station in the predicted time period according to the electricity consumption of each electricity user in the preset area in each historical time period and the power generation of each wind power station in each historical time period; if the ratio of the number of electricity users in the preset area to the number of wind power stations is less than the preset weight, the similarity between the time of each electricity consumption time curve and the time of each power generation time curve is obtained, and the wind power station to which each electricity user in the preset area is connected in the predicted time period is determined according to the similarity; thereby, the electricity users with higher electricity consumption in the predicted time period are matched with the wind power stations with higher power generation, thereby improving the power utilization rate of the wind power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1A flow chart of an adaptive flexible adjustment method for distributed new energy access provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] It should be noted that, based on the present disclosure, those skilled in the art should understand that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0032] The following will refer to Figure 1 The flowchart of the adaptive flexible adjustment method for distributed renewable energy access is shown, which introduces an adaptive flexible adjustment method for distributed renewable energy access.

[0033] The adaptive flexible adjustment method for distributed new energy access may include the following steps:

[0034] S100, obtaining the power consumption of each power user in a preset area in each historical time period, so as to obtain a list set of historical power consumption of the power user A=(A 1 , A 2 , …, A i , …, A n ), i=1, 2,...,n; among them, A i is the historical electricity consumption list of the electricity user corresponding to the i-th electricity user in the preset area, and n is the number of electricity users in the preset area; A i =(A 1，1 , A i，2 , …, A i，j , …, A i，m ), j = 1, 2, ..., m; A i，j is the electricity consumption of the ith electricity user in the jth historical time period, and m is the number of historical time periods.

[0035] In this embodiment, the preset area can be considered as a setting, for example: the preset area is a province; there are several electricity users and wind power stations in the preset area, and the electricity user can be a sub-area, for example: the electricity user is a factory area or a large office area, etc.; the wind power station and each electricity user can be scheduled; the historical time period can be in days or hours, which is not limited here; in each historical time period, the electricity consumption of the electricity user is recorded, and A can be obtained through the historical records.

[0036] S200, obtaining the power generation of each wind power station in a preset area in each historical time period to obtain a list set of wind power station power generation amounts B = (B 1 , B 2 , …, B p , …, B q ), p = 1, 2, ..., q; where B p is a list of wind power station power generation corresponding to the p-th wind power station in the preset area, and q is the number of wind power stations in the preset area; B p =(B p，1 , B p，2 , …, B p，j , …, B p，m );B p，j is the power generation of the pth wind power station in the jth historical time period.

[0037] In this embodiment, the power generation of each wind power station in the preset area in each historical time period is also recorded, and B can be obtained through the corresponding historical records; it can be understood that the power generation of the wind power station will fluctuate in a short period of time due to the influence of geographical location and seasonal weather, and after the time line is extended, there will be certain rules; similarly, the power consumption of electricity users will also have certain rules.

[0038] S300, input A and B into the preset prediction model respectively, to obtain the time curve of power consumption of each power user in the preset prediction time period and the time curve of power generation of each wind power station in the preset prediction time period in the preset area, and then obtain the predicted power consumption time curve list QA corresponding to the power user = (QA 1 , QA 2 ,…,QA i ,…,QA n ) and the predicted power generation time curve list QB corresponding to the wind power station = (QB 1 , Q.B. 2 , …, QB p , …, QB q );Among them, QA iis the time curve of electricity consumption of the ith electricity consumer in the preset area within the forecast time, QB i It is the time curve of power generation of the p-th wind power station in the preset area within the prediction time period; the start time of the prediction time period is the current time.

[0039] In this embodiment, the preset prediction model can be an LSTM model. The preset prediction model is a trained model that can predict the electricity consumption corresponding to the electricity users in the future time period and the power generation corresponding to the wind power station; the prediction time period can be set as needed, for example: the length of the prediction time period is 10 days or 30 days, etc.

[0040] S400, if n / q<α 1 , then obtain the first similarity between each power consumption time curve in QA and each power generation time curve in QB to obtain the first similarity list set corresponding to QA = (η 1 , η 2 ,…,η i ,…,η n ), where η i For QA i The corresponding first similarity list; n i =(η i，1 , η i，2 ,…,η i，p ,…,η i，q );η i，p For QA i With QB p The similarity between 1 The preset weights.

[0041] In this embodiment, α 1 The value range of can be 2 to 5. If n / q<α 1 , indicating that the number of electricity users in the preset area is not much more than the number of wind power stations. Therefore, considering the accuracy and processing efficiency, the first similarity between each electricity consumption time curve in QA and each power generation time curve in QB can be obtained to obtain the first similarity list set η corresponding to QA, and then the wind power station connected to each electricity user in the preset area within the predicted time period is further determined according to η.

[0042] It should be noted that those skilled in the art can use the existing curve similarity acquisition method according to actual needs to obtain the first similarity between each power consumption time curve in QA and each power generation time curve in QB to obtain the first similarity list set corresponding to QA, which is not elaborated here.

[0043] S500: Determine, based on η, the wind power station to which each electricity user in the preset area is connected within the predicted time period.

[0044] Further, step S500 may include the following steps:

[0045] S510, obtaining the first target similarity corresponding to each electricity user in the preset area to obtain a first target similarity list η'=(η' 1 , η' 2 ,…,η' i ,…,η' n );wherein, η ' i is the first target similarity corresponding to the i-th electricity user in the preset area; η' i =MAX(η i ); MAX() is the preset maximum value function.

[0046] In this embodiment, the maximum similarity between the power consumption time curve of each power user and the power generation time curve of each wind power station can be obtained, and then the target first similarity corresponding to each power user in the preset area can be obtained.

[0047] S520, η' i The corresponding wind power station is determined to be the wind power station connected to the i-th electricity user in the preset area within the predicted time period.

[0048] In this embodiment, the similarity between the electricity consumption time curve of the electricity user and the power generation time curve of the wind power station is the greatest, indicating that the time period when the electricity consumption of the electricity user is relatively large coincides with the time period when the wind power station generates relatively large power, thereby making maximum use of the electricity generated by the wind power station and improving the utilization rate of the electricity generated by the wind power station.

[0049] Further, after step S400 and before step S500, the method may further include the following steps:

[0050] S410, if n / q ≥ α 1 , then the predicted power consumption corresponding to each preset time point on each predicted power consumption time curve in QA is obtained to obtain the predicted power consumption feature vector XA corresponding to QA = (XA 1 , XA 2 , …, XA i , …, XA n ), where XA i For QA i The corresponding predicted power consumption feature vector; XA i =(XA i，1 , XA i，2 , …, XA i，e, …, XA i，g ), e = 1, 2, ..., g; XA i，e For QA i The predicted power consumption corresponding to the e-th preset time point on QA, g is the number of preset time points corresponding to each predicted power consumption time curve in QA.

[0051] In this embodiment, if n / q≥α 1 , indicating that the number of electricity users in the preset area is much larger than the number of wind power stations, and the electricity consumption patterns of most electricity users are similar, and they can be considered as the same type of electricity users; therefore, the predicted electricity consumption corresponding to each preset time point on each predicted electricity consumption time curve in QA can be obtained to obtain the predicted electricity consumption feature vector XA corresponding to QA.

[0052] S420, clustering the predicted power consumption feature vector in XA using a preset clustering algorithm to obtain a cluster list λ=(λ 1 ,λ 2 , …, λ x , …, λ y ), x = 1, 2, ..., y; where λ x is the xth cluster obtained by clustering the predicted power consumption feature vector in XA, and y is the number of clusters obtained by clustering the predicted power consumption feature vector in XA.

[0053] In this embodiment, the preset clustering algorithm may be a K-means clustering algorithm. It should be noted that those skilled in the art can use existing clustering algorithms to cluster the predicted power consumption feature vectors in XA according to actual needs to obtain the cluster list λ, which will not be elaborated here.

[0054] S430, obtaining the average predicted power consumption time curve corresponding to each cluster in λ, so as to obtain the average predicted power consumption time curve list θ=(θ 1 ,θ 2 ,…,θ x ,…,θ y ), where θ x is x The corresponding average predicted electricity consumption time curve.

[0055] In this embodiment, λ can be obtained x The average power consumption corresponding to each preset time point on each power consumption time curve in the figure is then obtained by curve fitting. x It should be noted that those skilled in the art can use existing curve fitting methods to obtain θ according to actual needs. x , which will not be elaborated here.

[0056] S440, obtaining a second similarity between each average predicted power consumption time curve in θ and each predicted power generation time curve in QB, so as to obtain a second similarity list set ε=(ε 1 , ε 2 , …, ε x , …, ε y ), where ε x is θ x The corresponding second similarity list; ε x =(ε x，1 , ε x，2 , …, ε x，p , …, ε x，q ); x，p is θ x With QB p The similarity between .

[0057] In this embodiment, it should be noted that those skilled in the art can use the existing curve similarity acquisition method according to actual needs to obtain the second similarity between each average predicted power consumption time curve in θ and each predicted power generation time curve in QB to obtain the second similarity list set ε, which will not be elaborated here.

[0058] S450, according to ε, determine the target second similarity corresponding to each average predicted power consumption time curve to obtain a target second similarity list ε'=(ε' 1 , ε' 2 , …, ε' x , …, ε' y ), where ε' x is θ x The corresponding target second similarity; ε' x =MAX(ε x ); MAX() is the preset maximum value function.

[0059] In this embodiment, it can be understood that the electricity consumption patterns of the electricity users corresponding to the same cluster are relatively similar, and therefore, the average predicted electricity consumption time curve can represent the electricity consumption patterns of the electricity users in the corresponding cluster.

[0060] S460, ε' x The corresponding wind power station is determined as λ x The corresponding wind power stations connected to each electricity user during the forecast period.

[0061] In this embodiment, ε' x The corresponding wind power station power generation law in the forecast period is x The corresponding power consumption pattern of each power user is matched, so ε' x The corresponding wind power station is determined as λx The corresponding wind power stations connected to each electricity user within the predicted time period; through the above steps, the electricity users are first clustered, and then the wind power stations are matched in batches, which can improve the matching efficiency.

[0062] The adaptive flexible adjustment method for distributed renewable energy access in this embodiment predicts the time curve of electricity consumption of each electricity user in the predicted time period and the time curve of power generation of each wind power station in the predicted time period according to the electricity consumption of each electricity user in the preset area in each historical time period and the power generation of each wind power station in each historical time period; if the ratio of the number of electricity users in the preset area to the number of wind power stations is less than the preset weight, the similarity between the time of each electricity consumption time curve and the time of each power generation time curve is obtained, and the wind power station to which each electricity user in the preset area is connected in the predicted time period is determined according to the similarity; thereby, the electricity users with higher electricity consumption in the predicted time period are matched with the wind power stations with higher power generation, thereby improving the power utilization rate of the wind power stations.

[0063] Furthermore, when there are many electricity users, the matching efficiency can be improved by clustering.

[0064] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0065] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0066] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0067] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0068] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0069] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0070] An embodiment of the present invention further provides an electronic device, comprising a processor and the aforementioned non-transitory computer-readable storage medium.

[0071] The electronic device is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0072] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one memory mentioned above, and a bus connecting different system components (including the memory and the processor).

[0073] The memory stores program codes, which can be executed by the processor, so that the processor executes the steps in various embodiments described in this specification.

[0074] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0075] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0076] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0077] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0078] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0079] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0080] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are only for illustration, not for limiting the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. An adaptive flexible adjustment method for distributed new energy access, characterized in that: The method comprises the following steps: S100, obtaining the power consumption of each power user in a preset area in each historical time period to obtain a list set of historical power consumption of the power users A = (A1, A2, ..., A i , …, A n ), i=1, 2,...,n; where, A i is the historical electricity consumption list of the electricity user corresponding to the i-th electricity user in the preset area, and n is the number of electricity users in the preset area; A i =(A 1，1 , A i，2 , …, A i，j , …, A i，m ), j = 1, 2, ..., m; A i，j is the electricity consumption of the ith electricity user in the jth historical time period, and m is the number of historical time periods; S200, obtaining the power generation of each wind power station in a preset area in each historical time period to obtain a list set of wind power station power generation amounts B = (B1, B2, ..., B p , …, B q ), p = 1, 2, ..., q; where B p is a list of wind power station power generation corresponding to the p-th wind power station in the preset area, and q is the number of wind power stations in the preset area; B p =(B p，1 , B p，2 , …, B p，j , …, B p，m );B p，j is the power generation of the p-th wind power station in the j-th historical time period; S300, input A and B into the preset prediction model respectively, to obtain the time curve of power consumption of each power user in the preset prediction time period and the time curve of power generation of each wind power station in the preset prediction time period in the preset area, and then obtain the predicted power consumption time curve list QA corresponding to the power user = (QA1, QA2, ..., QA i ,…,QA n ) and the predicted power generation time curve list QB corresponding to the wind power station = (QB1, QB2, ..., QB p , …, QB q );Among them, QA i is the time curve of electricity consumption of the ith electricity consumer in the preset area within the forecast time, QB i is a time curve of power generation of the p-th wind power station in the preset area within the prediction time period; the start time of the prediction time period is the current time; S400, if n / q<α1, then obtain the first similarity between each power consumption time curve in QA and each power generation time curve in QB to obtain the first similarity list set corresponding to QA η=(η1,η2,…,η i ,…,η n ), where η i For QA i The corresponding first similarity list; n i =(η i，1 , η i，2 ,…,η i，p ,…,η i，q );η i，p For QA i With QB p The similarity between them; α1 is the preset weight; S500: Determine, based on η, the wind power station to which each electricity user in the preset area is connected within the predicted time period.

2. The adaptive flexible adjustment method for distributed new energy access according to claim 1 is characterized in that: Step S500 includes the following steps: S510, obtaining the first target similarity corresponding to each electricity user in the preset area to obtain a first target similarity list η'=(η'1, η'2, ..., η' i ,…,η' n );wherein, η ' i is the first target similarity corresponding to the i-th electricity user in the preset area; η' i =MAX(η i ); MAX() is the preset maximum value function; S520, η' i The corresponding wind power station is determined to be the wind power station connected to the i-th electricity user in the preset area within the predicted time period.

3. The adaptive flexible adjustment method for distributed new energy access according to claim 1 is characterized in that: After step S400 and before step S500, the method further includes the following steps: S410, if n / q≥α1, obtain the predicted power consumption corresponding to each preset time point on each predicted power consumption time curve in QA to obtain the predicted power consumption feature vector XA corresponding to QA=(XA1, XA2, ..., XA i , …, XA n ), where XA i For QA i The corresponding predicted power consumption feature vector; XA i =(XA i，1 , XA i，2 , …, XA i，e , …, XA i，g ), e = 1, 2, ..., g; XA i，e For QA i The predicted power consumption corresponding to the e-th preset time point on , g is the number of preset time points corresponding to each predicted power consumption time curve in QA; S420, clustering the predicted power consumption feature vector in XA using a preset clustering algorithm to obtain a cluster list λ=(λ1, λ2, ..., λ x , …, λ y ), x = 1, 2, ..., y; where λ x is the xth cluster obtained by clustering the predicted power consumption feature vector in XA, and y is the number of clusters obtained by clustering the predicted power consumption feature vector in XA; S430, obtaining the average predicted power consumption time curve corresponding to each cluster in λ, so as to obtain the average predicted power consumption time curve list θ=(θ1, θ2, ..., θ x ,…,θ y ), where θ x is x The corresponding average predicted electricity consumption time curve; S440, obtaining a second similarity between each average predicted power consumption time curve in θ and each predicted power generation time curve in QB, so as to obtain a second similarity list set ε=(ε1, ε2, ..., ε x , …, ε y ), where ε x is θ x The corresponding second similarity list; ε x =(ε x，1 , ε x，2 , …, ε x，p , …, ε x，q ); x，p is θ x With QB p The similarity between S450, according to ε, determine the target second similarity corresponding to each average predicted power consumption time curve to obtain a target second similarity list ε'=(ε'1, ε'2, ..., ε' x , …, ε' y ), where ε' x is θ x The corresponding target second similarity; ε' x =MAX(ε x ); MAX() is the preset maximum value function; S460, ε' x The corresponding wind power station is determined as λ x The corresponding wind power stations connected to each electricity user during the forecast period.

4. The adaptive flexible adjustment method for distributed new energy access according to claim 3 is characterized in that: The preset clustering algorithm includes a K-means clustering algorithm.

5. The adaptive flexible adjustment method for distributed new energy access according to claim 1 is characterized in that: The preset prediction model includes an LSTM model.

6. The adaptive flexible adjustment method for distributed new energy access according to claim 1 is characterized in that: The value range of α1 is 2 to 5.

7. The adaptive flexible adjustment method for distributed new energy access according to claim 3 is characterized in that: The time interval between any two adjacent preset time points is the same.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the adaptive flexible adjustment method for distributed new energy access as described in any one of claims 1-7.

9. An electronic device, characterized in that: Includes a processor and the non-transitory computer-readable storage medium of claim 8.

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