Wind energy resource evaluation method and apparatus, computer device, and storage medium

Abstract

The present application relates to the technical field of new energy. Disclosed are a wind energy resource evaluation method and apparatus, a computer device, and a storage medium. The method comprises: acquiring wind energy resource data, and carrying out qualitative index evaluation of the wind energy resource data to generate a qualitative index confidence coefficient, qualitative indexes comprising a wind resource evaluation result confidence level index and a wind resource stability index; performing quantitative index evaluation of the wind energy resource data to generate quantitative index scores, quantitative indexes comprising a hub height average wind speed index, an average wind power density index, an effective wind speed hour index, a wind shear index, a turbulence intensity index and a wind power density grade index; and acquiring quantitative index weightings and, on the basis of the qualitative index confidence coefficient, the quantitative index scores and the quantitative index weightings, determining a total wind energy resource score. The present application performs overall quantitative evaluation of the rationality and feasibility of wind energy resources, so as to evaluate the wind energy resources in multiple dimensions, improving the evaluation efficiency and evaluation accuracy of the wind energy resources.

Description

Wind energy resource evaluation method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the field of new energy technology, and specifically to a wind energy resource evaluation method, device, computer equipment, and storage medium. Background Art

[0002] Some relevant work on new energy technology research and economic evaluation has been carried out at home and abroad, including research on resources, site selection, power generation costs, engineering geology, project economic benefits, etc., providing a certain theoretical basis and practical reference for the development of the new energy power generation industry and the development and evaluation of new energy grid-connected projects.

[0003] However, relevant assessment technologies lack comprehensive, quantitative analysis of all aspects of wind energy resources, and there is no research on the development of automated, digital, and precise decision-making support systems. Wind energy resource assessments lack a complete and scientific indicator evaluation system for horizontal comparisons across different projects, making it impossible to produce quantitative, comprehensive, and clear evaluation results.

[0004] Summary of the Invention

[0005] In view of this, the present application provides a wind energy resource evaluation method, apparatus, computer equipment and storage medium to solve the problem that wind energy resource evaluation lacks a complete and scientific indicator evaluation system.

[0006] In a first aspect, the present application provides a wind energy resource evaluation method, comprising:

[0007] Obtain wind energy resource data, conduct qualitative indicator evaluation on the wind energy resource data, and generate qualitative indicator confidence; wherein the qualitative indicators include wind resource evaluation result credibility index and wind resource stability index;

[0008] Conduct quantitative index evaluation on wind energy resource data and generate quantitative index scores; the quantitative indicators include average wind speed index at hub height, average wind power density index, effective wind hours index, wind shear index, turbulence intensity index and wind power density level index;

[0009] Obtain the quantitative indicator weights and determine the total wind energy resource score based on the qualitative indicator confidence, quantitative indicator score and quantitative indicator weights.

[0010] This application provides a wind energy resource evaluation method that conducts qualitative and quantitative indicator evaluations on wind energy resource data, conducts a comprehensive quantitative evaluation of the rationality and feasibility of wind energy resources, realizes a multi-dimensional evaluation of wind energy resources, improves the evaluation efficiency and accuracy of wind energy resources, and provides a theoretical basis for project investors' decision-making.

[0011] In an optional embodiment, performing qualitative indicator evaluation on wind energy resource data to generate qualitative indicator confidence includes:

[0012] The credibility of the wind resource assessment results in the wind energy resource data is judged by using resource measurement parameters to generate a credibility index value of the wind resource assessment results;

[0013] The stability of wind resource data in wind energy resource data is judged through long-sequence resource characteristic data, monthly resource data and quarterly resource data, and the wind resource stability index value is generated;

[0014] The confidence level of qualitative indicators is determined based on the values ​​of the wind resource assessment result credibility index and the wind resource stability index.

[0015] This application provides a wind energy resource evaluation method that implements a feasibility assessment of wind energy resources by performing qualitative indicator evaluation on wind energy resource data, thus providing a basis for project decision-making.

[0016] In an optional embodiment, performing a quantitative indicator evaluation on wind energy resource data to generate a quantitative indicator score includes:

[0017] Determine the average wind speed index value at hub height based on the wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in the target period;

[0018] Determine the average wind power density index value based on the monthly average air density, wind speed sequence and the number of wind speed sequences in the target period in the wind energy resource data;

[0019] Determine the effective wind hours index value based on the effective hours of wind speed of the wind turbine within the target period in the wind energy resource data;

[0020] Determine a wind shear index value based on the current wind speed and wind speed in the wind energy resource data;

[0021] Determine the turbulence intensity index value based on the fluctuating wind speed value and the average wind speed value in the wind energy resource data;

[0022] The annual effective wind power density, annual cumulative hours of wind speed and annual average wind speed in the wind energy resource data are compared with the preset thresholds to determine the wind power density level index value;

[0023] The average wind speed index value at hub height, the average wind power density index value, the effective wind hours index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value are matched with the scoring rule database respectively to generate a quantitative index score; the quantitative index score includes the average wind speed index score at hub height, the average wind power density index score, the effective wind hours index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

[0024] This application provides a wind energy resource evaluation method that achieves a rational evaluation of wind energy resources by performing quantitative indicator evaluation on wind energy resource data, providing a basis for project decision-making.

[0025] In an optional embodiment, the total wind energy resource score is determined based on the qualitative indicator confidence, the quantitative indicator score, and the quantitative indicator weight. The calculation formula for the total wind energy resource score is as follows: S = C*Σ(x1*w1+x2*w2+x3*w3+x4*w4+x5*w5+x6*w6)

[0026] Among them, S represents the total score of wind energy resources, C represents the confidence of qualitative indicators, x1 represents the average wind speed index score at hub height, w1 represents the weight of the average wind speed index at hub height, x2 represents the average wind power density index score, w2 represents the weight of the average wind power density index, x3 represents the effective wind hours index score, w3 represents the weight of the effective wind hours index, x4 represents the wind shear index index score, w4 represents the wind shear index index weight, x5 represents the turbulence intensity index score, w5 represents the turbulence intensity index weight, x6 represents the wind power density level index score, and w6 represents the wind power density level index weight.

[0027] The present application provides a wind energy resource evaluation method, which determines the total wind energy resource score based on the confidence of qualitative indicators, the score of quantitative indicators and the weight of quantitative indicators, thereby making the wind energy resource evaluation process more flexible and improving the accuracy of the wind energy resource evaluation results.

[0028] In a second aspect, the present application provides a wind energy resource evaluation device, comprising:

[0029] The first generation module is used to obtain wind energy resource data, perform qualitative indicator evaluation on the wind energy resource data, and generate qualitative indicator confidence; wherein the qualitative indicators include wind resource evaluation result credibility index and wind resource stability index;

[0030] The second generation module is used to perform quantitative index evaluation on wind energy resource data and generate quantitative index scores; the quantitative indicators include the average wind speed index at hub height, the average wind power density index, the effective wind hours index, the wind shear index, the turbulence intensity index and the wind power density level index;

[0031] The evaluation module is used to obtain the weights of quantitative indicators and determine the total score of wind energy resources based on the confidence of qualitative indicators, the scores of quantitative indicators and the weights of quantitative indicators.

[0032] In an optional implementation, the first generation module includes:

[0033] The first generating unit is configured to judge the credibility of the wind resource assessment result in the wind energy resource data by using the resource measurement parameters, and generate a credibility index value of the wind resource assessment result;

[0034] The second generating unit is used to judge the stability of wind resource data in the wind energy resource data by using the long sequence resource characteristic data, the monthly resource data and the quarterly resource data, and generate a wind resource stability index value;

[0035] The third generating unit is used to determine the qualitative indicator confidence based on the wind resource assessment result credibility indicator value and the wind resource stability indicator value.

[0036] In an optional implementation, the second generation module includes:

[0037] A first determining unit is configured to determine a hub height average wind speed index value based on a wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in a target period;

[0038] The second determining unit is used to determine the average wind power density index value based on the monthly average air density, wind speed sequence and the number of wind speed sequences in the target period in the wind energy resource data;

[0039] A third determining unit is configured to determine an effective wind hours index value based on the effective hours of wind speed of the wind turbine within the target period in the wind energy resource data;

[0040] a fourth determining unit, configured to determine a wind shear index indicator value based on the current wind speed and the wind speed in the wind energy resource data;

[0041] a fifth determining unit, configured to determine a turbulence intensity index value based on the fluctuating wind speed value and the average wind speed value in the wind energy resource data;

[0042] a sixth determining unit, configured to compare the annual effective wind power density, the annual cumulative hours of wind speed, and the annual average wind speed in the wind energy resource data with preset thresholds, respectively, to determine a wind power density level index value;

[0043] A matching unit is used to match the hub height average wind speed index value, the average wind power density index value, the effective wind hour index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value with the scoring rule database to generate a quantitative index score; the quantitative index score includes the hub height average wind speed index score, the average wind power density index score, the effective wind hour index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

[0044] In an optional embodiment, the evaluation module is specifically configured to determine a total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score, and the quantitative indicator weight. The total wind energy resource score is calculated as follows: S = C*Σ(x1*w1+x2*w2+x3*w3+x4*w4+x5*w5+x6*w6)

[0045] Among them, S represents the total score of wind energy resources, C represents the confidence of qualitative indicators, x1 represents the average wind speed index score at hub height, w1 represents the weight of the average wind speed index at hub height, x2 represents the average wind power density index score, w2 represents the weight of the average wind power density index, x3 represents the effective wind hours index score, w3 represents the weight of the effective wind hours index, x4 represents the wind shear index index score, w4 represents the wind shear index index weight, x5 represents the turbulence intensity index score, w5 represents the turbulence intensity index weight, x6 represents the wind power density level index score, and w6 represents the wind power density level index weight.

[0046] In a third aspect, the present application provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute a wind energy resource evaluation method according to the first aspect or any corresponding embodiment thereof.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute a wind energy resource evaluation method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] FIG1 is a flow chart of a method for evaluating wind energy resources according to an embodiment of the present application;

[0050] FIG2 is a schematic diagram of a wind energy resource evaluation system according to an embodiment of the present application;

[0051] FIG3 is a flow chart of another automated decision-making support management method for new energy technology assessment according to an embodiment of the present application;

[0052] FIG4 is a flow chart of another automated decision-making support management method for new energy technology assessment according to an embodiment of the present application;

[0053] FIG5 is a structural block diagram of a wind energy resource evaluation device according to an embodiment of the present application;

[0054] FIG6 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions 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 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 those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0056] The embodiment of the present application provides a wind energy resource evaluation method, which achieves a multi-dimensional evaluation of wind energy resources by evaluating qualitative indicators and quantitative indicators of wind energy resources.

[0057] According to an embodiment of the present application, an embodiment of a wind energy resource evaluation method 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.

[0058] In this embodiment, a wind energy resource evaluation method is provided, which can be used in the above-mentioned mobile terminal, such as a mobile phone, tablet computer, etc. FIG1 is a flow chart of a wind energy resource evaluation method according to an embodiment of the present application. As shown in FIG1-2 , the process includes the following steps:

[0059] Step S101 , obtaining wind energy resource data, performing qualitative index evaluation on the wind energy resource data, and generating qualitative index confidence; wherein the qualitative index includes a wind resource evaluation result credibility index and a wind resource stability index.

[0060] Step S102, performing a quantitative index evaluation on the wind energy resource data to generate a quantitative index score; wherein the quantitative index includes an average wind speed index at hub height, an average wind power density index, an effective wind hours index, a wind shear index, a turbulence intensity index, and a wind power density level index.

[0061] Step S103: obtaining the quantitative indicator weights, and determining the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score, and the quantitative indicator weights.

[0062] Specifically, the weight ratio is set according to the impact of each quantitative indicator on the investment return of the entire wind energy resource decision, and the total weight of each quantitative indicator is 100%.

[0063] Optionally, the total wind energy resource score is calculated as follows: S = C*Σ(x1*w1+x2*w2+x3*w3+x4*w4+x5*w5+x6*w6) (1)

[0064] Among them, S represents the total score of wind energy resources, C represents the confidence of qualitative indicators, x1 represents the average wind speed index score at hub height, w1 represents the weight of the average wind speed index at hub height, x2 represents the average wind power density index score, w2 represents the weight of the average wind power density index, x3 represents the effective wind hours index score, w3 represents the weight of the effective wind hours index, x4 represents the wind shear index index score, w4 represents the wind shear index index weight, x5 represents the turbulence intensity index score, w5 represents the turbulence intensity index weight, x6 represents the wind power density level index score, and w6 represents the wind power density level index weight.

[0065] Optionally, the total wind energy resource score is based on a 0-10 point system; a total wind energy resource score of 60 points (inclusive) or less is considered "poor", indicating that this part of the review is not recommended for passing; a total wind energy resource score of 60 points to 85 points (inclusive) is considered "good", indicating that this part of the review can be conditionally passed after rectification and optimization; a total wind energy resource score of 85 points or more is considered "excellent", indicating that this part has passed the assessment.

[0066] This embodiment provides a wind energy resource evaluation method, which performs qualitative and quantitative indicator evaluations on wind energy resource data, and conducts a comprehensive quantitative evaluation of the rationality and feasibility of wind energy resources. This achieves a multi-dimensional evaluation of wind energy resources, improves the evaluation efficiency and accuracy of wind energy resources, and provides a theoretical basis for project investors' decision-making. In addition, by determining the total wind energy resource score based on the confidence level of qualitative indicators, the score of quantitative indicators, and the weight of quantitative indicators, the wind energy resource evaluation process is made more flexible and the accuracy of the wind energy resource evaluation results is improved.

[0067] In this embodiment, a wind energy resource evaluation method is provided, which can be used in the above-mentioned mobile terminal, such as a mobile phone, a tablet computer, etc. FIG3 is a flow chart of a wind energy resource evaluation method according to an embodiment of the present application. As shown in FIG3 , the flow chart includes the following steps:

[0068] Step S301: Acquire wind energy resource data, perform qualitative index evaluation on the wind energy resource data, and generate qualitative index confidence; wherein the qualitative index includes a wind resource evaluation result credibility index and a wind resource stability index.

[0069] Specifically, the above step S301 includes:

[0070] Step S3011 , performing credibility judgment on the wind resource assessment results in the wind energy resource data using resource measurement parameters, and generating a credibility index value of the wind resource assessment results.

[0071] Specifically, the resource measurement parameters include: observation location (coordinates), on-site photos, elevation, wind measurement period, surrounding topography, wind measuring instrument configuration and wind measurement height, maintenance records; mesoscale data, operation data of surrounding built projects; the completeness rate of observation data, processing methods for unreasonable data and missing data, wind speed and wind power before and after data interpolation, wind direction frequency; wind shear index, wind speed calculation method and results at hub height; comparison chart of measurement data from different sensors at the same height (wind speed, wind direction, turbulence, etc.).

[0072] Step S3012 , performing stability judgment on wind resource data in the wind energy resource data using the long sequence resource characteristic data, monthly resource data, and quarterly resource data, and generating a wind resource stability index value.

[0073] Specifically, the long-sequence resource characteristic data and monthly resource data include multi-year meteorological element characteristics and resource characteristics; interannual and intra-year variations in wind speed and characteristic values ​​of meteorological elements; statistics on the number of freezing days in low-temperature and high-humidity areas in winter and the actual operation and shutdown conditions of surrounding wind farms; the correlation between the hourly wind speed of the wind measurement tower and the meteorological station in the same period, the consistency between the annual variations of the long-sequence wind speed of the wind measurement tower and the same period, and the frequency distribution pattern of wind direction; and long-sequence wind speed observation data.

[0074] Step S3013: determining the qualitative indicator confidence based on the wind resource assessment result credibility indicator value and the wind resource stability indicator value.

[0075] Specifically, confidence is given based on qualitative indicators, among which resource stability is mainly judged by long-sequence resource characteristic data, monthly and quarterly resource data; the credibility of the assessment results is mainly judged by the data quality, algorithms, key parameters, etc. used in the resource measurement process, and the confidence parameters are given by comprehensively considering resource stability and the credibility of the assessment conclusions.

[0076] This application provides a wind energy resource evaluation method that implements a feasibility assessment of wind energy resources by performing qualitative indicator evaluation on wind energy resource data, thus providing a basis for project decision-making.

[0077] Step S302: Quantitatively evaluate the wind energy resource data to generate a quantitative index score. The quantitative indicators include the average wind speed at hub height, the average wind power density, the effective wind hours, the wind shear index, the turbulence intensity, and the wind power density level. For details, please refer to step S102 in the embodiment shown in FIG1 , and will not be repeated here.

[0078] Step S303: Obtain the quantitative indicator weight, and determine the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score, and the quantitative indicator weight. For details, please refer to step S103 of the embodiment shown in FIG1 , which will not be described in detail here.

[0079] In this embodiment, a wind energy resource evaluation method is provided, which can be used in the above-mentioned mobile terminal, such as a mobile phone, a tablet computer, etc. FIG4 is a flow chart of a wind energy resource evaluation method according to an embodiment of the present application. As shown in FIG4 , the flow chart includes the following steps:

[0080] Step S401: Obtain wind energy resource data, perform qualitative indicator assessment on the wind energy resource data, and generate qualitative indicator confidence levels. Qualitative indicators include a wind resource assessment result credibility indicator and a wind resource stability indicator. For details, see step S301 in the embodiment shown in FIG3 , and will not be repeated here.

[0081] Step S402: Perform quantitative index evaluation on wind energy resource data to generate a quantitative index score.

[0082] Specifically, the above step S402 includes:

[0083] Step S4021: determining the hub height average wind speed index value based on the wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in the target period.

[0084] Specifically, the calculation formula for the average wind speed index value at hub height is as follows:

[0085] Among them, V E Indicates the average wind speed index value at hub height, V i represents the wind speed observation sequence, and n represents the number of wind speed sequences within the average wind speed calculation period (year, month) (i.e., the number of wind speed sequences within the target period).

[0086] Step S3022: determining an average wind power density index value based on the monthly average air density, wind speed sequence, and the number of wind speed sequences in the target period in the wind energy resource data.

[0087] Specifically, the calculation formula for the average wind power density index value is as follows:

[0088] Among them, D WP Indicates the average wind power density index value, n k,i represents the number of observation hours in the kth month, where k represents the month, k=1,2,……,12,ρ k represents the average air density in the kth month, v k,i represents the wind speed sequence of the kth month.

[0089] Step S4023: determining an effective wind hours index value based on the effective hours of wind speed of the wind turbine in the target period in the wind energy resource data.

[0090] Specifically, the number of hours during the target period when the wind speed of the wind turbine is higher than the cut-in wind speed and lower than the cut-out wind speed is counted, thereby determining the effective hours.

[0091] Step S3024: determining a wind shear index value based on the current wind speed and the wind speed in the wind energy resource data.

[0092] Specifically, the wind shear index represents the degree of change of wind speed with height. A large value indicates that wind energy increases rapidly with height and the wind speed gradient is large; a small value indicates that wind energy increases slowly with height and the wind speed gradient is small.

[0093] Optionally, the calculation formula for the wind shear index value is as follows:

[0094] Among them, α represents the wind shear index value, Z1 and Z2 represent wind speed heights, V2 represents the wind speed corresponding to Z2, and V1 represents the wind speed corresponding to Z1.

[0095] Step S4025: determining a turbulence intensity index value based on the fluctuating wind speed value and the average wind speed value in the wind energy resource data.

[0096] Specifically, the calculation formula for the turbulence intensity index value is as follows:

[0097] Where I represents the turbulence intensity index value, u′ represents the fluctuating wind speed value (sampling interval ≤ 3s), Indicates the average wind speed value.

[0098] Step S4026: compare the annual effective wind power density, the annual cumulative hours of wind speed, and the annual average wind speed in the wind energy resource data with preset thresholds to determine the wind power density level index value.

[0099] Specifically, the effective wind power density in that year was greater than 200W / m 3 (Watts per cubic meter), the annual cumulative hours of wind speed of 3 to 20 m / s (meters per second) are greater than 5000 h (hours), and the annual average wind speed is greater than 6 m / s. The current wind power area in the wind energy resources is a wind energy resource-rich area, and the wind power density level index value is 4.

[0100] Optionally, the effective wind power density in that year is 200-150W / m 3 , the annual cumulative hours of wind speed of 3 to 20 m / s is 5000h to 4000h, and when the annual average wind speed is around 5.5 m / s, the current wind power area in the wind energy resources is a secondary wind energy resource rich area, and the wind power density level index value is 3.

[0101] Optionally, the effective wind power density in that year is 150-100W / m 3 , the annual cumulative hours of wind speed of 3 to 20 m / s is 4000h to 2000h, and when the annual average wind speed is around 5 m / s, the current wind power area in the wind energy resources is a wind energy resource utilization area, and the wind power density level index value is 2.

[0102] Optionally, the effective wind power density in that year is less than 100W / m 3 , the annual cumulative hours of wind speed of 3 to 20 m / s are less than 2000 h, and the annual average wind speed is 4.5 m / s. The current wind power area in the wind energy resources is a wind energy resource-poor area, and the wind power density level index value is 1.

[0103] Step S4027, respectively match the hub height average wind speed index value, the average wind power density index value, the effective wind hours index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value with the scoring rule database to generate a quantitative index score; the quantitative index score includes the hub height average wind speed index score, the average wind power density index score, the effective wind hours index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

[0104] Specifically, based on the numerical values ​​of the above quantitative indicators, a quantitative scoring system of 0-10 is used to obtain a quantitative indicator score.

[0105] This application provides a wind energy resource evaluation method that achieves a rational evaluation of wind energy resources by performing quantitative indicator evaluation on wind energy resource data, providing a basis for project decision-making.

[0106] Step S403: Obtain the quantitative indicator weight, and determine the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score, and the quantitative indicator weight. For details, please refer to step S303 of the embodiment shown in FIG3 , which will not be described in detail here.

[0107] The following describes a method for evaluating wind energy resources using a specific embodiment.

[0108] Example 1:

[0109] The full life cycle assessment process of a wind power project mainly includes the assessment of the project's preliminary approval documents, feasibility study report assessment, preliminary design report assessment, EPC procurement assessment, equipment quality assessment, and post-production project operation evaluation.

[0110] The main evaluation indicators of wind energy resources in feasibility studies, preliminary design assessments and post-commissioning project operation evaluations are shown in Table 1 below:

[0111] Table 1:

[0112] The main quantitative indicators and their unit definitions are shown in Table 2 below:

[0113] Table 2:

[0114] The review plan is designed as follows:

[0115] (1) Quantitative indicators: Based on the actual quantitative indicator data of the project, a quantitative score is given using a 0-10 point system.

[0116] (2) Qualitative indicators: Confidence is given based on qualitative indicators. Resource stability is mainly judged by long-sequence resource characteristic data, monthly and quarterly resource data. The credibility of the assessment results is mainly judged by the data quality, algorithm, key parameters, etc. used in the resource measurement process. The confidence parameter is given based on the comprehensive resource stability and the credibility of the assessment conclusion.

[0117] The weight ratio of each indicator is set according to its impact on the investment return of the entire project decision. (Confidence * Σ (Score * Weight)) is the final score result corresponding to the evaluation content of this part.

[0118] The total weight of each section is 100%, and the total score is on a scale of 0-10. The system defaults to a total score of 60 points or less as "poor," indicating that the section is not recommended for approval; 60-85 points (inclusive) is "good," indicating that the section can be conditionally approved after rectification and optimization; and 85 points or above is "excellent," indicating that the section has passed the assessment.

[0119] This embodiment also provides a wind energy resource assessment device for implementing the above-described embodiments and optional implementations. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0120] This embodiment provides a wind energy resource evaluation device, as shown in FIG5 , comprising:

[0121] The first generating module 501 is used to obtain wind energy resource data, perform qualitative indicator evaluation on the wind energy resource data, and generate qualitative indicator confidence; wherein the qualitative indicators include a wind resource evaluation result credibility indicator and a wind resource stability indicator;

[0122] The second generation module 502 is used to perform quantitative index evaluation on the wind energy resource data and generate a quantitative index score; wherein the quantitative indicators include an average wind speed index at hub height, an average wind power density index, an effective wind hour index, a wind shear index, a turbulence intensity index, and a wind power density level index;

[0123] The evaluation module 503 is used to obtain the quantitative indicator weights and determine the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score and the quantitative indicator weights.

[0124] In some optional implementations, the first generating module 501 includes:

[0125] The first generating unit is configured to judge the credibility of the wind resource assessment result in the wind energy resource data by using the resource measurement parameters, and generate a credibility index value of the wind resource assessment result;

[0126] The second generating unit is used to judge the stability of wind resource data in the wind energy resource data by using the long sequence resource characteristic data, the monthly resource data and the quarterly resource data, and generate a wind resource stability index value;

[0127] The third generating unit is used to determine the qualitative indicator confidence based on the wind resource assessment result credibility indicator value and the wind resource stability indicator value.

[0128] In some optional implementations, the second generation module 502 includes:

[0129] A first determining unit is configured to determine a hub height average wind speed index value based on a wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in a target period;

[0130] The second determining unit is used to determine the average wind power density index value based on the monthly average air density, wind speed sequence and the number of wind speed sequences in the target period in the wind energy resource data;

[0131] A third determining unit is configured to determine an effective wind hours index value based on the effective hours of wind speed of the wind turbine within the target period in the wind energy resource data;

[0132] a fourth determining unit, configured to determine a wind shear index indicator value based on the current wind speed and the wind speed in the wind energy resource data;

[0133] a fifth determining unit, configured to determine a turbulence intensity index value based on the fluctuating wind speed value and the average wind speed value in the wind energy resource data;

[0134] a sixth determining unit, configured to compare the annual effective wind power density, the annual cumulative hours of wind speed, and the annual average wind speed in the wind energy resource data with preset thresholds, respectively, to determine a wind power density level index value;

[0135] A matching unit is used to match the hub height average wind speed index value, the average wind power density index value, the effective wind hour index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value with the scoring rule database to generate a quantitative index score; the quantitative index score includes the hub height average wind speed index score, the average wind power density index score, the effective wind hour index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

[0136] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0137] In this embodiment, a wind energy resource evaluation device is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0138] An embodiment of the present application also provides a computer device having a wind energy resource evaluation device as shown in FIG5 above.

[0139] Please refer to Figure 6, which is a structural diagram of a computer device provided by an optional embodiment of the present application. As shown in Figure 6, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 takes a processor 10 as an example.

[0140] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may also optionally include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0141] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0142] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device 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 combinations thereof.

[0143] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0144] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0145] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; optionally, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0146] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A method for evaluating wind energy resources, characterized in that: The method comprises: Acquire wind energy resource data, perform qualitative indicator evaluation on the wind energy resource data, and generate qualitative indicator confidence; wherein the qualitative indicators include wind resource evaluation result credibility indicators and wind resource stability indicators; Performing quantitative index evaluation on the wind energy resource data to generate a quantitative index score; wherein the quantitative indexes include an average wind speed index at hub height, an average wind power density index, an effective wind hour index, a wind shear index, a turbulence intensity index, and a wind power density grade index; The quantitative indicator weight is obtained, and the total wind energy resource score is determined based on the qualitative indicator confidence, the quantitative indicator score and the quantitative indicator weight.

2. The method according to claim 1, characterized in that The step of performing qualitative indicator evaluation on the wind energy resource data to generate qualitative indicator confidence includes: Performing credibility judgment on the wind resource assessment results in the wind energy resource data through resource measurement parameters to generate a credibility index value of the wind resource assessment results; Performing stability judgment on wind resource data in the wind energy resource data through long-sequence resource characteristic data, monthly resource data and quarterly resource data, and generating a wind resource stability index value; The qualitative indicator confidence is determined based on the wind resource assessment result credibility indicator value and the wind resource stability indicator value.

3. The method according to claim 1, characterized in that The step of performing quantitative indicator evaluation on the wind energy resource data to generate a quantitative indicator score includes: Determine the average wind speed index value at hub height based on the wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in the target period; Determine the average wind power density index value based on the monthly average air density, wind speed sequence and the number of wind speed sequences in the target period in the wind energy resource data; Determine an effective wind hours index value based on the effective hours of wind speed of the wind turbine within the target period in the wind energy resource data; Determining a wind shear index indicator value based on the current wind speed and the wind speed high speed in the wind energy resource data; Determine a turbulence intensity index value based on the pulsating wind speed value and the average wind speed value in the wind energy resource data; The annual effective wind power density, the annual cumulative hours of wind speed and the annual average wind speed in the wind energy resource data are compared with the preset thresholds to determine the wind power density level index value; The hub height average wind speed index value, the average wind power density index value, the effective wind hours index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value are matched with the scoring rule database respectively to generate the quantitative index score; the quantitative index score includes the hub height average wind speed index score, the average wind power density index score, the effective wind hours index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

4. The method according to claim 3, characterized in that The total wind energy resource score is determined based on the qualitative indicator confidence, the quantitative indicator score and the quantitative indicator weight. The calculation formula of the total wind energy resource score is as follows: S=C*Σ(x1*w1+x2*w2+x3*w3+x4*w4+x5*w5+x6*w6) Among them, S represents the total score of wind energy resources, C represents the confidence of qualitative indicators, x1 represents the average wind speed index score at hub height, w1 represents the weight of the average wind speed index at hub height, x2 represents the average wind power density index score, w2 represents the weight of the average wind power density index, x3 represents the effective wind hours index score, w3 represents the effective wind hours index weight, x4 represents the wind shear index index score, w4 represents the wind shear index index weight, x5 represents the turbulence intensity index score, w5 represents the turbulence intensity index weight, x6 represents the wind power density level index score, and w6 represents the wind power density level index weight.

5. A wind energy resource evaluation device, characterized in that: The device comprises: A first generating module is used to obtain wind energy resource data, perform qualitative indicator evaluation on the wind energy resource data, and generate qualitative indicator confidence; wherein the qualitative indicators include wind resource evaluation result credibility indicators and wind resource stability indicators; The second generation module is used to perform quantitative index evaluation on the wind energy resource data and generate quantitative index scores; wherein the quantitative indicators include an average wind speed index at hub height, an average wind power density index, an effective wind hour index, a wind shear index, a turbulence intensity index and a wind power density level index; The evaluation module is used to obtain the quantitative indicator weight and determine the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score and the quantitative indicator weight.

6. The device according to claim 5, characterized in that The first generating module comprises: A first generating unit is used to judge the credibility of the wind resource assessment result in the wind energy resource data by using the resource measurement parameter, and generate a credibility index value of the wind resource assessment result; A second generating unit is used to judge the stability of wind resource data in the wind energy resource data through long-sequence resource characteristic data, monthly resource data and quarterly resource data, and generate a wind resource stability index value; The third generating unit is used to determine the qualitative indicator confidence based on the wind resource assessment result credibility indicator value and the wind resource stability indicator value.

7. The device according to claim 5, characterized in that The second generating module comprises: A first determination unit is used to determine the average wind speed index value at the hub height based on the wind speed observation sequence in the wind energy resource data and the number of wind speed sequences in the target period; A second determination unit is used to determine the average wind power density index value based on the monthly average air density, wind speed sequence and the number of wind speed sequences in the target period in the wind energy resource data; A third determining unit, configured to determine an effective wind hour index value based on the effective hours of wind speed of the wind turbine within the target period in the wind energy resource data; a fourth determining unit, configured to determine a wind shear index indicator value based on the current wind speed and the wind speed in the wind energy resource data; a fifth determining unit, configured to determine a turbulence intensity index value based on a pulsating wind speed value and an average wind speed value in the wind energy resource data; A sixth determination unit, configured to compare the annual effective wind power density, the annual cumulative hours of wind speed and the annual average wind speed in the wind energy resource data with preset thresholds, respectively, to determine a wind power density level index value; A matching unit is used to match the average wind speed index value at the hub height, the average wind power density index value, the effective wind hours index value, the wind shear index index value, the turbulence intensity index value and the wind power density level index value with the scoring rule database respectively to generate the quantitative index score; the quantitative index score includes the average wind speed index score at the hub height, the average wind power density index score, the effective wind hours index score, the wind shear index index score, the turbulence intensity index score and the wind power density level index score.

8. The device according to claim 5, characterized in that The evaluation module is specifically used to determine the total wind energy resource score based on the qualitative indicator confidence, the quantitative indicator score and the quantitative indicator weight. The calculation formula of the total wind energy resource score is as follows: S=C*Σ(x1*w1+x2*w2+x3*w3+x4*w4+x5*w5+x6*w6) Among them, S represents the total score of wind energy resources, C represents the confidence of qualitative indicators, x1 represents the average wind speed index score at hub height, w1 represents the weight of the average wind speed index at hub height, x2 represents the average wind power density index score, w2 represents the weight of the average wind power density index, x3 represents the effective wind hours index score, w3 represents the effective wind hours index weight, x4 represents the wind shear index index score, w4 represents the wind shear index index weight, x5 represents the turbulence intensity index score, w5 represents the turbulence intensity index weight, x6 represents the wind power density level index score, and w6 represents the wind power density level index weight.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes a wind energy resource evaluation method according to any one of claims 1 to 4 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute a wind energy resource evaluation method according to any one of claims 1 to 4.

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