Curve form matching-based direct-current near-area wind-light capacity configuration evaluation method and system

Abstract

The invention discloses a curve form matching-based direct-current near-area wind and light capacity configuration evaluation method and system, and belongs to the technical field of high-proportion new energy power system planning. The method comprises the following steps: acquiring wind and light data of a direct-current near region, and processing the wind and light data; based on the processed wind and light data, setting a wind and light ratio, and calculating a wind power and photovoltaic combined output curve; and based on the wind power and photovoltaic combined output curve, carrying out curve adaptation degree calculation, and based on a calculation result, outputting an evaluation result of the direct current near-area wind and light capacity. The method solves the problem that a traditional method depends on experience judgment and lacks quantitative basis, and provides guidance for follow-up energy storage configuration.

Description

TECHNICAL FIELD

[0001] The new energy power system planning relates to the technical field, and more particularly, to a DC near-zone wind and light capacity configuration evaluation method and system based on curve shape matching. BACKGROUND

[0002] With the increasing application of DC transmission technology in new energy delivery, whether the wind and light capacity resources in the DC near-zone are reasonable has become a key problem to ensure transmission efficiency. In the prior art, new energy capacity evaluation is mostly based on the total amount of resource endowment or the average power of a single time scale, lacking in-depth analysis of the matching relationship between the output curve and the transmission curve in the time sequence shape. SUMMARY

[0003] To solve the above problems, the present application provides a DC near-zone wind and light capacity evaluation method based on curve shape matching, comprising:

[0004] Obtaining wind and light data in the DC near-zone, and processing the wind and light data;

[0005] Based on the processed wind and light data, setting the wind and light ratio, and calculating the joint output curve of wind and light;

[0006] Based on the joint output curve of wind and light, calculating the curve fitting degree, and outputting the evaluation result of the DC near-zone wind and light capacity configuration based on the calculation result.

[0007] Optionally, obtaining the wind and light data in the DC near-zone comprises:

[0008] Collecting wind and light data of wind farms and photovoltaic power stations in the DC near-zone, wherein the wind and light data includes historical output data of wind farms and photovoltaic power stations and DC typical transmission curve data.

[0009] Optionally, the historical output data at least includes time sequence output data of one complete year, and the time resolution is not less than 1 hour.

[0010] Optionally, the DC typical transmission curve data of the whole year is obtained according to the design capacity and operation plan of the DC transmission system.

[0011] Optionally, processing the wind and light data comprises:

[0012] Normalizing the wind and light data, comprising:

[0013] Converting the historical output data and the DC typical transmission curve data in the wind and light data to the [0, 1] interval to eliminate the dimension, and the conversion formula is as follows:

[0014]

[0015] Wherein, P is the original power value, P min and P max are the maximum and minimum power values respectively, P norm is the normalized value.

[0016] Optionally, the calculation formula for calculating the output curve of wind power photovoltaic is as follows:

[0017]

[0018] Wherein, P W,i and P PV,i are the normalized output of wind power and photovoltaic at i time respectively, P Wnorm,i and P PVnorm,i are the original normalized output of wind power and photovoltaic at i time, and R is the set wind-light ratio.

[0019] Optionally, the cosine similarity calculation method is used to calculate the curve fitting degree, and the matching degree of the wind-light combined output and the direct current curve is obtained, and the calculation formula is as follows:

[0020]

[0021] Wherein, CS is the matching degree of the wind-light combined output and the direct current curve, P W,i and P PV,i are the normalized output of wind power and photovoltaic at i time respectively, P dc,i is the power transmission power of direct current at P dc,i time, and n is the number of data.

[0022] In still another aspect, the present application also proposes a direct current near-zone wind-light capacity configuration evaluation system based on curve shape matching, comprising:

[0023] A data acquisition unit is used to acquire the wind-light data of the direct current near-zone, and the wind-light data is processed;

[0024] A calculation unit is used to set the wind-light ratio based on the processed wind-light data, and the combined output curve of wind power photovoltaic is calculated;

[0025] An evaluation unit is used to calculate the curve fitting degree based on the combined output curve of wind power photovoltaic, and output the evaluation result of the direct current near-zone wind-light capacity configuration based on the calculation result.

[0026] Optionally, the wind-light data of the direct current near-zone is acquired, comprising:

[0027] The wind-light data of the direct current near-zone wind power plant and photovoltaic power station is acquired, and the wind-light data comprises: the historical output data of the wind power plant and photovoltaic power station and the direct current typical power transmission curve data.

[0028] Optionally, the historical output data at least includes time-series output data of one complete year, and the time resolution is not less than 1 hour.

[0029] Optionally, the DC typical transmission curve data of the whole year is obtained according to the design capacity and operation plan of the DC transmission system.

[0030] Optionally, the wind-solar data is processed, including:

[0031] The wind-solar data is normalized, including:

[0032] The historical output data and the DC typical transmission curve data in the wind-solar data are uniformly converted to the interval [0, 1] to eliminate the dimension, and the conversion formula is as follows:

[0033]

[0034] wherein P is an original power value, P min and P max are maximum and minimum power values respectively, and P norm is a normalized value.

[0035] Optionally, the calculation formula of the output curve of the wind power and the photovoltaic power is as follows:

[0036]

[0037] wherein P W,i and P PV,i are normalized output values of the wind power and the photovoltaic power respectively at i moment, P Wnorm,i and P PVnorm,i are original normalized output values of the wind power and the photovoltaic power respectively at i moment, and R is a set wind-solar ratio.

[0038] Optionally, the cosine similarity calculation method is used to calculate the curve fitting degree, to obtain the matching degree of the wind-solar combined output and the DC curve, and the calculation formula is as follows:

[0039]

[0040] wherein CS is the matching degree of the wind-solar combined output and the DC curve, P W,i and P PV,i are normalized output values of the wind power and the photovoltaic power respectively at i moment, and P dc,i is the transmission power of the DC at P dc,i moment, and n is the number of data.

[0041] In still another aspect, the application further provides a computing device, including one or more processors.

[0042] A processor is used to execute one or more programs;

[0043] When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0044] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] This invention provides a method for evaluating near-field wind and solar capacity configuration based on curve morphology matching, comprising: acquiring near-field wind and solar data, and processing the wind and solar data; setting a wind-solar ratio based on the processed wind and solar data, and calculating the combined output curve of wind and solar power; calculating the curve fit based on the combined output curve of wind and solar power, and outputting the evaluation result of near-field wind and solar capacity configuration based on the calculation result. This invention solves the problems of traditional methods relying on experience and lacking quantitative basis, and provides guidance for subsequent energy storage configuration. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is a flowchart of an embodiment of the method of the present invention;

[0049] Figure 3 This is a normalized wind power output curve diagram of an embodiment of the method of the present invention;

[0050] Figure 4 This is a normalized photovoltaic power output curve diagram of an embodiment of the method of the present invention;

[0051] Figure 5 This is a normalized curve of DC power transmission in an embodiment of the method of the present invention;

[0052] Figure 6 This is a structural diagram of the system of the present invention. Detailed Implementation

[0053] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0054] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0055] Example 1:

[0056] This invention proposes a DC near-field wind and solar capacity configuration evaluation method S100 based on curve morphology matching, such as... Figure 1 As shown, it includes:

[0057] S101, acquire wind and solar data of the DC near-field area, and process the wind and solar data;

[0058] S102, Based on the processed wind and solar data, set the wind and solar power ratio and calculate the combined output curve of wind and solar power;

[0059] S103, based on the combined output curve of wind and solar power, calculate the curve fit, and based on the calculation results, output the evaluation results of DC near-field wind and solar capacity configuration.

[0060] This includes acquiring wind and solar data in the DC near-field region, including:

[0061] Collect wind and solar data from wind farms and photovoltaic power plants in the DC near-field area. The wind and solar data includes historical power output data of wind farms and photovoltaic power plants and typical DC transmission curve data.

[0062] The historical output data includes at least one full year of time-series output data, with a time resolution of not less than 1 hour.

[0063] Among them, typical DC transmission curve data for the whole year are obtained based on the design capacity and operation plan of the DC transmission system.

[0064] The processing of the wind and light data includes:

[0065] Normalization processing of wind and solar data includes:

[0066] Historical power output data from wind and solar power and typical DC transmission curve data are uniformly converted to the [0,1] interval to eliminate dimensions. The conversion formula is as follows:

[0067]

[0068] Where P is the original power value, P min and P max These are the maximum and minimum power values, P. norm This is the normalized value.

[0069] The formula for calculating the output curve of wind and solar power is as follows:

[0070]

[0071] Among them, P W,i and P PV,i P represents the normalized power output of wind power and solar power at time i, proportionally allocated to each other. Wnorm,i and P PVnorm,i R represents the original normalized output of wind and solar power at time i, and R is the set wind-solar power ratio.

[0072] The cosine similarity method is used to calculate the fit of the curves, thus obtaining the matching degree between the combined wind and solar power output and the DC curve. The calculation formula is as follows:

[0073]

[0074] Where CS represents the matching degree between the combined wind and solar power output and the DC curve, and P... W,i and P PV,i P represents the normalized power output of wind power and solar power at time i, proportionally allocated to each other. dc,i For P dc,i The DC transmission power at any given time, where n is the number of data items.

[0075] The invention will be further explained below with reference to specific implementation examples:

[0076] The specific process is as follows: Figure 2 As shown, it includes:

[0077] Step 1: Data preparation and preprocessing.

[0078] Collect historical power output data and typical DC transmission curves for wind farms and photovoltaic power plants. Data collection requires obtaining time-series power output data for at least one full year, with a time resolution of no less than 1 hour, to generate power output characteristic curves for wind and solar power respectively. For typical DC curve construction, based on the design capacity and operation plan of the DC transmission system, obtain typical DC transmission power curves for the entire year.

[0079] Step 2: Data normalization.

[0080] The minimum-maximum normalization method is used to uniformly transform the wind and solar power output and DC curve data to the [0,1] interval to eliminate the influence of dimensions. The formula is as follows:

[0081]

[0082] Where P is the original power value, P min and P max These are the maximum and minimum power values, P.norm This is the normalized value.

[0083] Step 3: Set the landscape ratio.

[0084] Set the wind-solar power ratio R and calculate the output curves of wind and solar power:

[0085]

[0086] Among them, P W,i P PV,i P represents the normalized output of wind and solar power at time i, proportionally allocated. Wonorm,i P PVnorm,i It contributes to the original normalization of wind power and photovoltaic power at time i.

[0087] Step 4: Calculate the curve fit.

[0088] An improved cosine similarity method is used to evaluate the matching degree between the combined wind and solar power output and the DC power curve. The cosine similarity calculation method is as follows:

[0089]

[0090] Among them, P dc,i For P dc,i The DC transmission power at time n is the length of the time series.

[0091] Step 5: Output the evaluation results.

[0092] This embodiment takes a UHVDC transmission base in Northwest China as an example. This base transmits electricity to East China via a ±800kV UHVDC line, with a rated DC transmission capacity of 8000MW. The base plans a total installed capacity of 8400MW for new energy sources, and it is necessary to determine the optimal wind and solar capacity ratio to achieve the best match between the output characteristics of new energy sources and the DC transmission curve.

[0093] Typical annual wind power output data: maximum output 4200MW, average output 1680MW, normalized output curve over 8760 hours as shown below. Figure 3 As shown:

[0094] Typical annual photovoltaic power output data: maximum output 3500MW, average output 1050MW, normalized output curve over 8760 hours as shown below. Figure 4 As shown:

[0095] Typical DC transmission curve: Using typical load curves from East China, with a maximum transmission capacity of 8000MW, the 8760-hour output curve is as follows. Figure 5 As shown:

[0096] Five typical schemes were set up for calculation and comparison, as shown in Table 1:

[0097] Table 1

[0098] Scheme Ratio Cosine similarity 1 1:2 0.72 2 2:3 0.78 3 1:1 0.85 4 3:2 0.92 5 2:1 0.88

[0099] By comparison, it can be seen that the 3:2 landscape ratio scheme achieves the highest overall suitability and is the most suitable landscape ratio.

[0100] This invention constructs an evaluation index that simultaneously considers shape similarity, enabling the matching evaluation of the morphological structure of the combined wind and solar power output curve and the DC transmission curve. This addresses the problems of traditional methods relying on empirical judgment and lacking quantitative basis, providing guidance for subsequent energy storage configuration.

[0101] Example 2:

[0102] Furthermore, this invention also proposes a DC near-field wind and solar capacity configuration evaluation system 200 based on curve morphology matching, such as... Figure 6 As shown, it includes:

[0103] Data acquisition unit 201 is used to acquire wind and solar data in the DC near-field and process the wind and solar data;

[0104] The calculation unit 202 is used to set the wind-solar ratio based on the processed wind and solar data and calculate the combined output curve of wind and solar power.

[0105] Evaluation unit 203 is used to calculate the curve fit based on the combined output curve of wind power and photovoltaic power, and output the evaluation result of DC near-field wind and solar capacity configuration based on the calculation result.

[0106] This includes acquiring wind and solar data in the DC near-field region, including:

[0107] Collect wind and solar data from wind farms and photovoltaic power plants in the DC near-field area. The wind and solar data includes historical power output data of wind farms and photovoltaic power plants and typical DC transmission curve data.

[0108] The historical output data must include at least one full year of time-series output data, with a time resolution of at least 1 hour.

[0109] Among them, typical DC transmission curve data for the whole year are obtained based on the design capacity and operation plan of the DC transmission system.

[0110] The processing of the wind and light data includes:

[0111] Normalization processing of wind and solar data includes:

[0112] Historical power output data from wind and solar power and typical DC transmission curve data are uniformly converted to the [0,1] interval to eliminate dimensions. The conversion formula is as follows:

[0113]

[0114] Where P is the original power value, P min and P max These are the maximum and minimum power values, P. norm This is the normalized value.

[0115] The formula for calculating the output curve of wind and solar power is as follows:

[0116]

[0117] Among them, P W,i and P PV,i P represents the normalized power output of wind power and solar power at time i, proportionally allocated to each other. Wnorm,i and P PVnorm,i R represents the original normalized output of wind and solar power at time i, and R is the set wind-solar power ratio.

[0118] The cosine similarity method is used to calculate the fit of the curves, thus obtaining the matching degree between the combined wind and solar power output and the DC curve. The calculation formula is as follows:

[0119]

[0120] Where CS represents the matching degree between the combined wind and solar power output and the DC curve, and P... W,i and P PV,i P represents the normalized power output of wind power and solar power at time i, proportionally allocated to each other. dc,i For P dc,i The DC transmission power at any given time, where n is the number of data items.

[0121] This invention solves the problem of neglecting the matching with DC transmission curves in traditional wind and solar capacity configuration methods, and provides a scientific and practical method for estimating near-field wind and solar capacity in DC, effectively addressing the stability challenges brought about by high proportion of new energy access, and is of great significance for promoting the consumption of new energy and improving the efficiency of DC transmission.

[0122] Example 3:

[0123] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0124] Example 4:

[0125] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0126] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0127] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0131] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A DC near-zone wind and light capacity configuration evaluation method based on curve shape matching, characterized in that, The method comprises the following steps: Obtaining wind and light data of a direct current near area, processing the wind and light data; Based on the processed wind and light data, setting the wind and light ratio, and calculating the joint output curve of wind power and photovoltaic power; Based on the joint output curve of wind power and photovoltaic power, calculating the fitting degree of the curve, and outputting the evaluation result of the wind and light capacity configuration of the direct current near area based on the calculation result.

2. The DC near-zone wind and light capacity configuration evaluation method according to claim 1, characterized in that, Obtaining wind and light data of a direct current near area comprises: Collecting wind and light data of a direct current near area wind power plant and a photovoltaic power station, wherein the wind and light data comprises historical output data of the wind power plant and the photovoltaic power station and direct current typical transmission curve data.

3. The DC near-zone wind and light capacity configuration evaluation method according to claim 2, characterized in that, The historical output data at least comprises time series output data of one complete year, and the time resolution is not less than 1 hour.

4. The method of claim 2, wherein, According to the design capacity and operation plan of the direct current transmission system, the direct current typical transmission curve data of the whole year is obtained.

5. The method of claim 1, wherein, Processing the wind and light data comprises: Normalizing the wind and light data comprises: Converting the historical output data and the direct current typical transmission curve data in the wind and light data to the interval [0, 1] to eliminate the dimension, and the conversion formula is as follows: Where P is the original power value, P min and P max These are the maximum and minimum power values, P. norm This is the normalized value. 6.The direct current near-zone wind light capacity evaluation method according to claim 1, characterized in that, The calculation formula of the output curve of wind power and photovoltaic power is as follows: where P W,i and P PV,i are the scaled normalized outputs of wind and PV at time i, respectively, P Wnorm,i and P PVnorm,i are the raw normalized outputs of wind and PV at time i, and R is the set wind-PV ratio.

7. The method of claim 1, wherein, The cosine similarity calculation method is used to calculate the fitting degree of the curve, and the matching degree of the joint output of wind and light and the direct current curve is obtained, and the calculation formula is as follows: Where CS represents the matching degree between the combined wind and solar power output and the DC curve, and P... W,i and P PV,i P represents the normalized power output of wind power and solar power at time i, proportionally allocated to each other. dc,i For P dc,i The DC transmission power at any given time, where n is the number of data items.

8. A DC near-zone wind and light capacity configuration evaluation system based on curve shape matching, characterized in that, The method comprises the following steps: A data collection unit is configured to obtain wind and light data of a direct current near area, and process the wind and light data; A calculation unit is configured to set the wind and light ratio based on the processed wind and light data, and calculate the joint output curve of wind power and photovoltaic power; An evaluation unit is configured to calculate the fitting degree of the curve based on the joint output curve of wind power and photovoltaic power, and output the evaluation result of the wind and light capacity configuration of the direct current near area based on the calculation result.

9. The direct near zone wind light capacity configuration evaluation system of claim 8, wherein, Obtaining wind and light data of a direct current near area comprises: Collecting wind and light data of a direct current near area wind power plant and a photovoltaic power station, wherein the wind and light data comprises historical output data of the wind power plant and the photovoltaic power station and direct current typical transmission curve data.

10. The DC near-zone wind and solar capacity configuration evaluation system according to claim 9, characterized in that, The historical output data at least comprises time series output data of one complete year, and the time resolution is not less than 1 hour.

11. The DC near-zone wind and solar capacity configuration evaluation system according to claim 9, characterized in that, According to the design capacity and operation plan of the direct current transmission system, the direct current typical transmission curve data of the whole year is obtained.

12. The DC near-zone wind and solar capacity configuration evaluation system according to claim 8, characterized in that, Processing the wind and light data comprises: Normalizing the wind and light data comprises: Converting the historical output data and the direct current typical transmission curve data in the wind and light data to the interval [0, 1] to eliminate the dimension, and the conversion formula is as follows: where P is the original power value, P min and P max are the maximum and minimum power values, respectively, and P norm is the normalized value.

13. The DC near-zone wind and solar capacity configuration evaluation system of claim 8, wherein, The calculation formula of the output curve of wind power and photovoltaic power is as follows: where P W,i and P PV,i are the scaled normalized outputs of wind and PV at time i, respectively, P Wnorm,i and P PVnorm,i are the raw normalized outputs of wind and PV at time i, respectively, and R is the set wind-PV ratio.

14. The DC near-zone wind and solar capacity configuration evaluation system according to claim 8, characterized in that, The cosine similarity calculation method is used to calculate the fitting degree of the curve, and the matching degree of the joint output of wind and light and the direct current curve is obtained, and the calculation formula is as follows: Where CS is the matching degree of the wind-solar combined output and the DC curve, P W,i and P PV,i are the normalized outputs of the wind power and the photovoltaic power respectively at time i, P dc,i is the power transmitted by the DC at time P dc,i , and n is the number of data.

15. A computer device, comprising: The method comprises the following steps: One or more processors; A processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the method as claimed in any one of claims 1-7 is implemented.

16. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable medium, and when the computer program is executed, the method as claimed in any one of claims 1-7 is implemented.

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