Wind energy resource assessment method and system based on multi-model set and statistical verification

Abstract

The present application belongs to the field of wind energy resource assessment, and discloses a wind energy resource assessment method and system based on a multi-model set and statistical verification. First, daily average wind speed data of multiple global climate models of CMIP6 and global historical wind field reanalysis data are obtained, after unified spatial resampling, the wind speed anomaly sequence of each model historical period is processed by season cycle removal, and the ability of each model to simulate the historical wind field is objectively quantified by two-sample CVM test, and high-performance models are selected to form a multi-model set. Then, the future wind speed data of the set is non-weighted averaged to generate a multi-model set future wind field data set, and compared with the historical benchmark period to obtain wind field time series change basic data, and then calculate multi-dimensional wind energy resource assessment results, and finally generate spatial distribution map, variability change map and quantitative risk assessment report. The present application significantly improves the reliability and reliability of wind energy resource assessment.

Description

Technical Field

[0001] This invention belongs to the field of wind energy resource assessment technology, and in particular relates to a wind energy resource assessment method and system based on multi-model ensemble and statistical verification. Background Technology

[0002] As the global energy transition deepens, wind energy, as an important type of clean energy, continues to increase its share in the global energy structure, and the wind power industry has entered a stage of large-scale development. Since the formation and distribution of wind energy resources are highly dependent on the state of the climate system, future climate change will trigger long-term evolution of regional wind field characteristics, significantly impacting the power generation economics and operational reliability of existing wind farms, as well as the site selection planning and investment returns of planned wind power projects. It will also pose new challenges to the dispatching, operation, and safety and stability of the power system. Therefore, conducting an assessment of wind energy resource changes under the background of future climate change has significant technical support value for the long-term planning, project development, and risk control of the wind power industry.

[0003] Current wind energy resource assessment technologies in the context of climate change still have several shortcomings. Regarding model application, most assessment methods rely on simulation results from only one or a few climate models, failing to conduct objective and quantitative statistical tests on the models' ability to simulate historical wind fields. The lack of rigorous model screening and validation processes leads to significant uncertainty in the assessment results and low overall reliability. In terms of analytical dimensions, existing assessments primarily focus on the changing trends of average wind speed or wind energy density, paying insufficient attention to the evolution of key dimensions such as the temporal variability, seasonal distribution characteristics, and spatial distribution patterns of wind energy resources. This makes it difficult to comprehensively support wind farm operation optimization and grid dispatch decisions. Regarding the technical system, the data acquisition, model validation, and indicator analysis stages of the assessment process are relatively loose and independent, lacking a standardized and reproducible systematic technical process, making it difficult to effectively guarantee assessment efficiency and the comparability of results.

[0004] It is evident that existing wind energy resource assessment methods under the background of climate change suffer from problems such as a lack of model validation and screening, a single analytical dimension, and an unsystematic technical process, which cannot provide reliable technical support for the long-term planning, project site selection, and risk assessment of the wind power industry. Summary of the Invention

[0005] This invention provides a wind energy resource assessment method and system based on multi-model ensemble and statistical verification. This method can effectively solve the problems of lack of model verification and screening, single analysis dimensions, and unsystematic technical processes in existing wind energy resource assessment methods under the background of climate change. It can provide reliable technical support for the long-term planning, project site selection, and risk assessment of the wind power industry.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A wind energy resource assessment method based on multi-model ensemble and statistical validation includes: Obtain daily mean wind speed data from multiple global climate models in CMIP6 and a global historical wind field reanalysis dataset as validation baseline data; resample all daily mean wind speed data and global historical wind field reanalysis data to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. Based on a wind field dataset with uniform spatial resolution, the daily average wind speed time series of each global climate model is processed to remove seasonal cycles during the historical simulation period to obtain the wind speed anomaly sequences for each global climate model. A two-sample CVM test is performed on the wind speed anomaly sequences of each global climate model and the corresponding wind speed anomaly sequences of the global historical wind field reanalysis data to obtain the global performance score of the global climate model. Based on the global performance score, all global climate models are ranked, and the selected global climate models are combined into a multi-model set. The future wind speed data of each global climate model in the multi-model set are calculated by performing an unweighted average to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field temporal changes. Based on the multi-model ensemble future wind field dataset and historical baseline wind field data, multi-dimensional wind energy resource assessment results are calculated. Based on the multi-dimensional wind energy resource assessment results, a spatial distribution map and a variability map of wind energy resource changes are generated, and high-risk areas, stable areas and opportunity areas of wind energy resources are divided and identified, generating a quantitative wind energy resource risk assessment report.

[0007] Furthermore, the daily average wind speed data of the multiple global climate models are daily average wind speed data at a height of 10m near the Earth's surface, specifically including historical simulation data and future scenario data; wherein, the future scenario data includes at least the SSP2-4.5 intermediate emission path scenario and the SSP5-8.5 high emission path scenario; the global historical wind field reanalysis dataset adopts the ERA-5 reanalysis dataset; The same spatial grid is a 1.5° × 1.5° spatial grid.

[0008] Furthermore, the process of deseasoning the daily average wind speed time series for each global climate model includes: Calculate the historical average wind speed for the corresponding month in the daily average wind speed time series of the global climate model and the global historical wind field reanalysis data, respectively. Subtract the historical average wind speed for the corresponding month from the original daily average wind speed to obtain the corrected wind speed anomaly series. The calculation formula is as follows: U_anom(t) = U(t) - U_clim(month(t)) In the formula, U(t) is the original daily average wind speed; U_clim(month(t)) is the historical average wind speed for the corresponding month; and U_anom(t) is the wind speed anomaly sequence.

[0009] Furthermore, the two-sample CVM test is performed on the wind speed anomaly sequence of each global climate model and the corresponding wind speed anomaly sequence of the global historical wind field reanalysis data, including: At a preset significance level, for each spatial grid point, it is determined whether the statistic of the two-sample CVM test is less than the critical value. If so, the grid point is determined to have passed the test. The percentage of grid points that pass the test in the global spatial scope for each global climate model is counted, and this percentage is used as the global performance score of the global climate model. After screening, the number of global climate models that constitute the multi-model set is 5 to 10.

[0010] Furthermore, the future period includes two time periods: the medium term 2041-2060 and the long term 2081-2100; the historical baseline wind field data adopts historical simulated wind field data from 2000 to 2020; the unweighted average calculation is performed on the wind speed time series data of each spatial grid point, generating a multi-model set of future wind field datasets corresponding to each climate scenario.

[0011] Furthermore, the multi-dimensional wind energy resource assessment results data include wind energy density mean change index, coefficient of variation change index, and time scale decomposition index; wherein, the wind energy density mean change index is the percentage change in wind energy density in the future period relative to the historical baseline period; the coefficient of variation change index is the difference in coefficient of variation between the future and historical wind energy density time series; and the time scale decomposition index includes seasonal wind energy density change results and monthly wind energy density change results.

[0012] Furthermore, the evaluation method also includes a regional adaptive optimization step, as follows: When conducting wind energy resource assessments for a specific geographical area, two-sample CVM tests and model performance rankings are performed using spatial grid points within that area. Based on the regional performance rankings, a customized multi-model set for the corresponding region is re-selected and constructed. Then, subsequent future scenario analysis, multi-dimensional indicator calculations, and risk assessment processes are executed.

[0013] A wind energy resource assessment system based on multi-model ensemble and statistical validation includes: The acquisition module is used to acquire daily mean wind speed data from multiple global climate models in CMIP6 and the global historical wind field reanalysis dataset as validation benchmark data; all daily mean wind speed data and global historical wind field reanalysis data are uniformly resampled to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. The filtering module, based on a wind field dataset with uniform spatial resolution, performs deseasonalized cyclic processing on the daily average wind speed time series of each global climate model within the historical simulation period to obtain the corresponding wind speed anomaly sequences for each global climate model. A two-sample CVM test is then performed on the wind speed anomaly sequences of each global climate model and the corresponding wind speed anomaly sequences from the global historical wind field reanalysis data to obtain a global performance score for the global climate model. Based on the global performance score, all global climate models are ranked, and the filtered global climate models are combined into a multi-model ensemble. The generation module is used to perform unweighted average calculation on the future wind speed data of each global climate model in the multi-model set to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field time series changes; The calculation module is used to calculate multi-dimensional wind energy resource assessment results based on the multi-model ensemble future wind field dataset and historical baseline wind field data. The output module is used to generate spatial distribution maps and variability maps of wind energy resource changes based on multi-dimensional wind energy resource assessment results data, delineate and identify high-risk areas, stable areas and opportunity areas of wind energy resources, and generate quantitative wind energy resource risk assessment reports.

[0014] A wind energy resource assessment device based on multi-model ensemble and statistical validation, comprising: Memory, used to store computer programs; A processor is used to implement the aforementioned wind energy resource assessment method based on multi-model set and statistical verification when executing the computer program.

[0015] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the above-described wind energy resource assessment method based on a multi-model set and statistical verification.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a wind energy resource assessment method based on a multi-model ensemble and statistical validation. First, daily average wind speed data and global historical wind field reanalysis data from multiple CMIP6 global climate models are acquired. After unified spatial resampling, the wind speed anomaly sequences of each model for historical periods are deseasonally cyclically processed. A two-sample CVM test is used to objectively quantify the ability of each model to simulate historical wind fields, thereby selecting high-performance models to form a multi-model ensemble. Subsequently, the future wind speed data of this ensemble is unweighted to generate a multi-model ensemble future wind field dataset, which is compared with historical baseline data to obtain basic data on wind field temporal changes. Multi-dimensional wind energy resource assessment results are then calculated, ultimately generating spatial distribution maps, variability maps, and a quantitative risk assessment report. This method utilizes statistical testing to rigorously screen models, effectively reducing the uncertainty introduced by a single model, while enhancing prediction robustness through multi-model ensemble averaging. The multi-dimensional analysis covers temporal variability, seasonal distribution, and spatial patterns, overcoming the shortcomings of traditional assessment methods that rely on a single dimension. This method significantly improves the credibility and reliability of wind energy resource assessment, fully supports wind farm operation optimization, grid dispatching decisions and long-term planning, and forms a standardized, reproducible, and systematic technical process to ensure the comparability and efficiency of assessment results. Attached Figure Description

[0017] Figure 1 This invention provides an implementation flowchart of a wind energy resource assessment method based on multiple model sets and statistical verification. Figure 2 A flowchart of a wind energy resource assessment method based on multi-model ensemble and statistical verification provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the structure of a wind energy resource assessment system based on multi-model ensemble and statistical verification, provided in an embodiment of the present invention. Detailed Implementation

[0018] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0019] The technical terms involved in this invention are explained as follows: CMIP6: the sixth phase of the Coupled Model Intercomparison Project.

[0020] GCMs: Global Climate Models.

[0021] CVM Test: Cramér-von Mises Test, also known as the CVM test.

[0022] As described in the background section, existing assessment methods often suffer from the following limitations: First, they rely on single or unverified models: they frequently use results from a single climate model or a few models without objectively and quantitatively verifying the models' ability to simulate historical wind fields, leading to high uncertainty and low reliability in the assessment results. Second, they lack a single analytical dimension: they primarily focus on changes in average wind speed or wind energy density, neglecting other evolutions such as wind resource variability and seasonality, which are crucial for wind farm operation and grid dispatch. Third, they lack a systematic process: the various stages of the assessment process (data acquisition, verification, and analysis) are loosely connected, failing to form a standardized and reproducible technical system.

[0023] To address the aforementioned issues, this embodiment provides a wind energy resource assessment method based on multi-model ensembles and statistical validation. This method constructs a screened multi-model ensemble and employs a multi-dimensional analysis index system to comprehensively quantify the mean, variability, and spatiotemporal distribution evolution of wind energy resources under climate change. Specifically, this method integrates the latest CMIP6 climate scenario data and pioneers the use of a high-performance multi-model ensemble screened by CVM validation. Combined with a multi-dimensional index system including wind energy density, coefficient of variation (COV), and seasonal decomposition, it systematically and quantitatively assesses the long-term mean changes, increased temporal variability, and intra-annual distribution shifts of future global or regional wind energy resources.

[0024] For example, such as Figure 2 As shown, this embodiment provides a wind energy resource assessment method based on multi-model ensembles and statistical verification, including: Obtain daily mean wind speed data from multiple global climate models in CMIP6 and a global historical wind field reanalysis dataset as validation baseline data; resample all daily mean wind speed data and global historical wind field reanalysis data to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. Based on a wind field dataset with uniform spatial resolution, the daily average wind speed time series of each global climate model is processed to remove seasonal cycles during the historical simulation period to obtain the wind speed anomaly sequences for each global climate model. A two-sample CVM test is performed on the wind speed anomaly sequences of each global climate model and the corresponding wind speed anomaly sequences of the global historical wind field reanalysis data to obtain the global performance score of the global climate model. Based on the global performance score, all global climate models are ranked, and the selected global climate models are combined into a multi-model set. The future wind speed data of each global climate model in the multi-model set are calculated by performing an unweighted average to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field temporal changes. Based on the multi-model ensemble future wind field dataset and historical baseline wind field data, multi-dimensional wind energy resource assessment results are calculated. Based on the multi-dimensional wind energy resource assessment results, a spatial distribution map and a variability map of wind energy resource changes are generated, and high-risk areas, stable areas and opportunity areas of wind energy resources are divided and identified, generating a quantitative wind energy resource risk assessment report.

[0025] The wind energy resource assessment method based on multi-model ensemble and statistical verification provided in this embodiment will be further explained below with reference to the accompanying drawings: For example, such as Figure 1 As shown in the figure, this embodiment provides a wind energy resource assessment method based on multiple model sets and statistical verification. The specific steps are as follows: Step 1: Data Preparation and Preprocessing S11. Obtain daily average wind speed data at a height of 10m above the Earth's surface from multiple global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), including historical simulations and future scenarios (the selected climate change scenarios must include at least SSP2-4.5 and SSP5-8.5). SSP2-4.5 represents a moderate emission pathway scenario, indicating that current policies will largely continue, the climate change trend will be relatively stable, and the radiative forcing by 2100 will be 4.5 W / m². 2 SSP5-8.5 represents a high-emission pathway scenario, signifying fossil fuel-intensive development, with radiative forcing reaching 8.5 W / m² by 2100. 2 These two scenarios encompass both "relative stability" and "drastic change," enhancing the robustness of the conclusions.

[0026] S12. Obtain a global historical wind field reanalysis dataset with high confidence (such as ERA-5) as a validation benchmark.

[0027] S13. Resample all the wind field data of the above GCMs and the ERA-5 reanalysis data to the same spatial grid (e.g., 1.5°×1.5°).

[0028] Step 2: Model Performance Validation and Screening For each GCM, the following operations are performed on the wind speed data for each spatial grid point during its historical simulation period (e.g., 2000–2020): S21. Remove seasonal cycles. Remove the seasonal cycle mean from the daily average wind speed time series of GCMs and ERA-5 to obtain the debiased series.

[0029] U_anom(t) = U(t) - U_clim(month(t)) Where U(t) is the original wind speed, and U_clim(month(t)) is the historical climate average for the corresponding month.

[0030] S22. Distribution Consistency Test. The two-sample Cramér-von Mises test (CVM test) is applied to the debiased GCMs wind speed series and the ERA-5 wind speed series. The Cramér-von Mises test is used to determine the goodness of fit between the cumulative distribution function and a given empirical distribution function, or to compare whether two empirical distributions are consistent. The CVM test statistic measures the integral of the squared distance between the empirical cumulative distribution function and the target cumulative distribution function. That is, the squared differences of each data point are summed to obtain the total distribution bias, thus considering the differences of all data points. Its test statistic is:

[0031] In a single sample, This is the theoretical cumulative distribution function. It is the empirical cumulative distribution function. At a pre-defined significance level (e.g., α If the calculated value is 0.05, then... If the value is less than the critical value, it is considered that the two samples come from the same distribution, that is, the GCM can simulate the historical wind field statistical characteristics well at this grid point.

[0032] S23. Calculate the proportion of grid points for each GCM whose wind speed anomaly sequence distribution is not significantly different from the ERA-5 data (i.e., passes the CVM test) globally, and use this as the global performance score of the model.

[0033] S24. Based on the above scores, sort all available GCMs and select the N best-performing GCMs (e.g., the top 5-10) to form a multi-model set for all subsequent analyses.

[0034] Step 3: Construction of Multi-Model Aggregate and Future Scenario Analysis: S31. Perform an unweighted average of the wind speed data for the future periods (e.g., medium-term 2041-2060, long-term 2081-2100) of the selected N GCMs to generate a multi-model ensemble future wind field dataset corresponding to each climate scenario (SSP2-4.5, SSP5-8.5).

[0035] S32. Compare the future wind field dataset with its own historical baseline data (e.g., 2000-2020).

[0036] Step 4: Calculation and analysis of multi-dimensional wind energy resource indicators: Based on future and historical wind speed data from a multi-mode ensemble, the following core indicators are calculated to comprehensively assess changes in wind energy resources.

[0037] S41. Risk analysis of wind energy density and mean variation: Wind energy density P is a core indicator for assessing total resources, and its calculation formula is as follows: P = 1 / 2 ×ρ× U 3 Where U is wind speed and ρ is air density. The average wind energy density P_hist and P_fut are calculated for historical baseline periods (e.g., 2000-2020) and future target periods (e.g., medium-term 2041-2060, long-term 2081-2100). The risk of change in the mean resource value is quantified as the percentage change in wind energy density in future periods relative to the historical baseline period. ΔP_mean = (P_fut - P_hist) / P_hist×100% By spatially mapping ΔP_mean, high-risk areas with significantly reduced wind resources and areas with potential for increased development can be identified.

[0038] S42. Variability Analysis: The coefficient of variation (COV) is introduced as a key indicator to measure the temporal volatility of wind resources. The COV of wind energy density time series for historical and future periods is calculated. COV = σ(P) / μ(P) Where σ(P) is the standard deviation of the wind energy density sequence, and μ(P) is its average value.

[0039] The future change in COV, ΔCOV, is calculated to assess the evolution of resource stability. An increase in COV indicates increased volatility and poses a higher risk to grid absorption.

[0040] S43. Time-scale decomposition analysis: (1) Seasonal analysis: Calculate and compare the average wind energy density and its changes in the historical and future spring, summer, autumn and winter seasons. Divide the year into four seasons (e.g. DJF, MAM, JJA, SON), calculate the average wind energy density of each season in the historical and future seasons, analyze ΔP_season, reveal the seasonal shift of resource distribution within the year (e.g., resources decrease sharply in summer and do not change much in winter), which is crucial for the seasonal balance planning of the power system.

[0041] (2) Monthly analysis: Conduct more detailed monthly-scale calculations and analyses to reveal the detailed changing patterns of resource distribution throughout the year, providing a reference for wind farm operation and maintenance plans.

[0042] Step 5: Visualization of Results and Generation of Risk Assessment Report: S51. Generate spatial distribution maps of wind energy density changes, coefficient of variation maps, and seasonal variation sequence maps for global and key regions.

[0043] S52. Based on the above analysis, identify the "high-risk area" where wind energy resources have significantly decreased, the "stable area" where resources have not changed much, and the "opportunity area" where resources may increase.

[0044] S53, by integrating information such as spatial changes, increased variability, and seasonal shifts, generates quantitative risk assessment reports to support energy policy formulation and wind farm lifecycle planning.

[0045] Furthermore, as another preferred embodiment, the evaluation method further includes a regional adaptability optimization step: When conducting in-depth evaluations for specific geographic regions (such as North America, Europe, and East Asia), repeat step two, but only use grid points within that region for CVM testing and model performance ranking. Based on the regional ranking, re-select and construct a customized multi-model set for that region, and then perform the analysis in Steps 3-5.

[0046] For example, such as Figure 3 As shown, this embodiment also provides a wind energy resource assessment system based on multi-model ensembles and statistical verification, including: The acquisition module is used to acquire daily mean wind speed data from multiple global climate models in CMIP6 and the global historical wind field reanalysis dataset as validation benchmark data; all daily mean wind speed data and global historical wind field reanalysis data are uniformly resampled to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. The filtering module, based on a wind field dataset with uniform spatial resolution, performs deseasonalized cyclic processing on the daily average wind speed time series of each global climate model within the historical simulation period to obtain the corresponding wind speed anomaly sequences for each global climate model. A two-sample CVM test is then performed on the wind speed anomaly sequences of each global climate model and the corresponding wind speed anomaly sequences from the global historical wind field reanalysis data to obtain a global performance score for the global climate model. Based on the global performance score, all global climate models are ranked, and the filtered global climate models are combined into a multi-model ensemble. The generation module is used to perform unweighted average calculation on the future wind speed data of each global climate model in the multi-model set to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field time series changes; The calculation module is used to calculate multi-dimensional wind energy resource assessment results based on the multi-model ensemble future wind field dataset and historical baseline wind field data. The output module is used to generate spatial distribution maps and variability maps of wind energy resource changes based on multi-dimensional wind energy resource assessment results data, delineate and identify high-risk areas, stable areas and opportunity areas of wind energy resources, and generate quantitative wind energy resource risk assessment reports.

[0047] The present invention also provides a wind energy resource assessment device based on multi-model ensemble and statistical verification, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the wind energy resource assessment method based on multi-model ensemble and statistical verification.

[0048] The present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the wind energy resource assessment method based on multi-model set and statistical verification.

[0049] When the processor executes the computer program, it implements the above-mentioned steps for wind energy resource assessment based on multiple model sets and statistical verification, such as: acquiring daily average wind speed data from multiple global climate models of CMIP6 and a global historical wind field reanalysis dataset as verification benchmark data; uniformly resampling all daily average wind speed data and global historical wind field reanalysis data to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. Based on a wind field dataset with uniform spatial resolution, the daily average wind speed time series of each global climate model is processed to remove seasonal cycles during the historical simulation period to obtain the wind speed anomaly sequences for each global climate model. A two-sample CVM test is performed on the wind speed anomaly sequences of each global climate model and the corresponding wind speed anomaly sequences of the global historical wind field reanalysis data to obtain the global performance score of the global climate model. Based on the global performance score, all global climate models are ranked, and the selected global climate models are combined into a multi-model set. The future wind speed data of each global climate model in the multi-model set are calculated by performing an unweighted average to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field temporal changes. Based on the multi-model ensemble future wind field dataset and historical baseline wind field data, multi-dimensional wind energy resource assessment results are calculated. Based on the multi-dimensional wind energy resource assessment results, a spatial distribution map and a variability map of wind energy resource changes are generated, and high-risk areas, stable areas and opportunity areas of wind energy resources are divided and identified, generating a quantitative wind energy resource risk assessment report.

[0050] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, wherein the instruction segments describe the execution process of the computer program in the wind energy resource assessment device based on multi-model sets and statistical verification. For example, the computer program can be divided into an acquisition module, a filtering module, a generation module, a calculation module, and an output module; the specific functions are as follows: The acquisition module is used to acquire daily average wind speed data from multiple global climate models of CMIP6 and a global historical wind field reanalysis dataset as validation benchmark data; all daily average wind speed data and global historical wind field reanalysis data are uniformly resampled to the same spatial grid to obtain a wind field dataset with uniform spatial resolution; The filtering module is used to perform deseasonalized cyclic processing on the daily average wind speed time series of each global climate model within the historical simulation period based on the wind field dataset with uniform spatial resolution to obtain the wind speed anomaly sequence corresponding to each global climate model; a two-sample CVM test is performed on the wind speed anomaly sequence of each global climate model and the wind speed anomaly sequence of the corresponding global historical wind field reanalysis data to obtain the globality of the global climate model. The system comprises the following modules: a performance scoring module; a global performance scoring module; a ranking module; a generation module; a generation module; a generation module; a calculation module; a calculation module; and an output module. The output module is used to generate a spatial distribution map and a variability map of wind energy resource changes, delineate and identify high-risk, stable, and opportunity zones for wind energy resources, and generate a quantitative wind energy resource risk assessment report.

[0051] The wind energy resource assessment device based on multi-model ensembles and statistical verification can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above examples of wind energy resource assessment devices based on multi-model ensembles and statistical verification do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the wind energy resource assessment device based on multi-model ensembles and statistical verification may also include input / output devices, network access devices, buses, etc.

[0052] The processor can 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. The general-purpose processor can be a microprocessor, or any conventional processor. This processor is the control center of the wind energy resource assessment based on multi-model ensembles and statistical verification, connecting all parts of the wind energy resource assessment equipment using various interfaces and lines.

[0053] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the wind energy resource assessment device based on multi-model set and statistical verification by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0054] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0055] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wind energy resource assessment method based on multiple model sets and statistical verification.

[0056] If the modules / units of the wind energy resource assessment system based on multi-model set and statistical verification are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0057] Based on this understanding, the present invention can implement all or part of the processes in the wind energy resource assessment method based on multi-model ensembles and statistical verification. This can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the wind energy resource assessment method based on multi-model ensembles and statistical verification. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form.

[0058] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0059] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and methodological practice in the jurisdiction. For example, in some jurisdictions, according to legislation and methodological practice, the computer-readable storage medium may not include electrical carrier signals and telecommunication signals.

[0060] Compared with existing assessment methods, this invention provides a wind energy resource assessment method and system based on multiple model sets and statistical verification, which has the following advantages: This invention employs a quantitative method combining deseasonalization preprocessing and Cramér-von Mises statistical tests to score and screen global climate models, thereby constructing a high-performance multi-model ensemble for wind energy resource assessment. This reduces model simulation errors and improves the reliability of simulated wind speed data. In wind resource assessment under climate change, this invention constructs a multi-dimensional indicator system and method that includes relative changes in wind energy density (ΔP_mean), changes in the coefficient of variation (ΔCOV), and seasonal changes (ΔP_season), forming a triple analytical dimension of "mean-variance-seasonality." This overcomes the limitation of focusing only on changes in the mean of wind energy density, achieving the following technical advantages: First, this invention ensures that the climate model used to predict the future has a good ability to simulate the statistical characteristics of historical wind fields through objective screening based on CVM test, which significantly reduces the simulation error caused by differences in model performance.

[0061] Secondly, this invention breaks through the limitation of focusing only on mean changes. For the first time, it incorporates the evolution of resource variability (COV) and temporal distribution (seasonal / monthly) into the risk assessment system within a systematic framework, which is more in line with the actual needs of power grid operation and more comprehensively reflects the potential challenges of climate change to the power system.

[0062] Third, this invention is based on the latest CMIP6 data and SSP scenario, and utilizes the latest achievements in international climate science research, making the assessment results more forward-looking and of greater policy reference value.

[0063] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A wind energy resource assessment method based on multi-model ensembles and statistical validation, characterized in that, include: Obtain daily mean wind speed data from multiple global climate models in CMIP6 and a global historical wind field reanalysis dataset as validation baseline data; resample all daily mean wind speed data and global historical wind field reanalysis data to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. Based on a wind field dataset with uniform spatial resolution, the daily average wind speed time series of each global climate model is processed by deseasonalization during the historical simulation period to obtain the wind speed anomaly sequence of each global climate model. A two-sample CVM test is performed on the wind speed anomaly sequence of each global climate model and the wind speed anomaly sequence of the corresponding global historical wind field reanalysis data to obtain the global performance score of the global climate model. All global climate models are ranked based on global performance scores, and the selected global climate models are combined into a multi-model set. The future wind speed data of each global climate model in the multi-model set are calculated by performing an unweighted average to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field temporal changes. Based on the multi-model ensemble future wind field dataset and historical baseline wind field data, multi-dimensional wind energy resource assessment results are calculated. Based on the multi-dimensional wind energy resource assessment results, a spatial distribution map and a variability map of wind energy resource changes are generated, and high-risk areas, stable areas and opportunity areas of wind energy resources are divided and identified, generating a quantitative wind energy resource risk assessment report.

2. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The daily average wind speed data from the multiple global climate models are daily average wind speed data at a height of 10m above the Earth's surface, specifically including historical simulation data and future scenario data; wherein, the future scenario data includes at least the SSP2-4.5 intermediate emission path scenario and the SSP5-8.5 high emission path scenario; the global historical wind field reanalysis dataset adopts the ERA-5 reanalysis dataset; The same spatial grid is a 1.5° × 1.5° spatial grid.

3. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The process of removing seasonal cycles from the daily average wind speed time series of each global climate model includes: Calculate the historical average wind speed for the corresponding month in the daily average wind speed time series of the global climate model and the global historical wind field reanalysis data, respectively. Subtract the historical average wind speed for the corresponding month from the original daily average wind speed to obtain the corrected wind speed anomaly series. The calculation formula is as follows: U_anom(t) = U(t) - U_clim(month(t)) In the formula, U(t) is the original daily average wind speed; U_clim(month(t)) is the historical average wind speed for the corresponding month; and U_anom(t) is the wind speed anomaly sequence.

4. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The two-sample CVM test is performed on the wind speed anomaly sequence of each global climate model and the corresponding wind speed anomaly sequence of global historical wind field reanalysis data, including: At a preset significance level, for each spatial grid point, it is determined whether the statistic of the two-sample CVM test is less than the critical value. If so, the grid point is determined to have passed the test. The percentage of grid points that pass the test in the global spatial scope for each global climate model is counted, and this percentage is used as the global performance score of the global climate model. After screening, the number of global climate models that constitute the multi-model set is 5 to 10.

5. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The future period includes two time periods: the medium term 2041-2060 and the long term 2081-2100; the historical baseline wind field data uses historical simulated wind field data from 2000 to 2020; the unweighted average calculation is performed on the wind speed time series data of each spatial grid point, generating a multi-model set of future wind field datasets corresponding to each climate scenario.

6. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The multi-dimensional wind energy resource assessment results include wind energy density mean change index, coefficient of variation change index, and time scale decomposition index; wherein, the wind energy density mean change index is the percentage change in wind energy density in the future period relative to the historical baseline period; the coefficient of variation change index is the difference in the coefficient of variation of the wind energy density time series between the future and historical periods; the time scale decomposition index includes wind energy density change results on a seasonal scale and wind energy density change results on a monthly scale.

7. The wind energy resource assessment method based on multi-model ensemble and statistical verification according to claim 1, characterized in that, The evaluation method also includes a regional adaptive optimization step, as follows: When conducting wind energy resource assessments for a specific geographical area, two-sample CVM tests and model performance rankings are performed using spatial grid points within that area. Based on the regional performance rankings, a customized multi-model set for the corresponding region is re-selected and constructed. Then, subsequent future scenario analysis, multi-dimensional indicator calculations, and risk assessment processes are executed.

8. A wind energy resource assessment system based on multi-model ensembles and statistical verification, characterized in that, include: The acquisition module is used to acquire daily mean wind speed data from multiple global climate models in CMIP6 and the global historical wind field reanalysis dataset as validation benchmark data; all daily mean wind speed data and global historical wind field reanalysis data are uniformly resampled to the same spatial grid to obtain a wind field dataset with uniform spatial resolution. The filtering module is used to perform deseasonalized cyclic processing on the daily average wind speed time series of each global climate model based on a wind field dataset with uniform spatial resolution, and obtain the wind speed anomaly sequence of each global climate model. A two-sample CVM test is performed on the wind speed anomaly sequence of each global climate model and the wind speed anomaly sequence of the corresponding global historical wind field reanalysis data to obtain the global performance score of the global climate model. All global climate models are ranked based on global performance scores, and the selected global climate models are combined into a multi-model set. The generation module is used to perform unweighted average calculation on the future wind speed data of each global climate model in the multi-model set to generate a multi-model set future wind field dataset corresponding to each climate scenario; the multi-model set future wind field dataset is compared with the historical baseline wind field data under the corresponding climate scenario to obtain the basic data of wind field time series changes; The calculation module is used to calculate multi-dimensional wind energy resource assessment results based on the multi-model ensemble future wind field dataset and historical baseline wind field data. The output module is used to generate spatial distribution maps and variability maps of wind energy resource changes based on multi-dimensional wind energy resource assessment results data, delineate and identify high-risk areas, stable areas and opportunity areas of wind energy resources, and generate quantitative wind energy resource risk assessment reports.

9. A wind energy resource assessment device based on multi-model ensemble and statistical verification, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the wind energy resource assessment method based on multi-model set and statistical verification as described in any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the wind energy resource assessment method based on multi-model set and statistical verification as described in any one of claims 1-7.

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