A clean energy resource analysis method, device, equipment and storage medium

Abstract

The present invention relates to the field of clean energy technology, and specifically to a clean energy resource analysis method, device, equipment and storage medium. The method comprises: obtaining historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range of a target area; correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data; based on the historical observed meteorological data and the corrected meteorological data, using a pre-constructed clean energy resource analysis model for analysis to obtain clean energy resource analysis results for the target area; when performing clean energy resource analysis, the present invention can simultaneously consider the climate conditions of the historical period and the impact of climate change on clean energy resources, so that the predicted meteorological data can be more accurate, thereby improving the accuracy of clean energy resource analysis.

Description

Technical Field

[0001] The present invention relates to the field of clean energy technology, and in particular to a clean energy resource analysis method, device, equipment and storage medium. Background Art

[0002] In recent years, against the backdrop of global climate change such as global warming and El Niño, climate change will further intensify in the coming decades, affecting the distribution of renewable energy including hydropower, wind power, and solar energy.

[0003] Existing analysis methods for clean energy resources usually calculate recent clean energy resource evaluation results based on historical relevant data; however, the construction of clean energy infrastructure often requires advance site selection and layout, which requires huge investment and high initial investment. Once construction begins, it is difficult to undo: the design service life of wind turbines is generally 20-25 years, the design service life of photovoltaic panels is about 25 years, and the design service life of hydropower stations is even longer; the cost of suspension, delay and reconstruction is also very high. Once a wrong decision is made, it will cause huge losses; therefore, it is necessary to fully consider the impact of climate change in the early planning and design process. Summary of the invention

[0004] In view of this, the present invention provides a clean energy resource analysis method, device, equipment and storage medium to solve the technical problem that the existing analysis method for clean energy resources only considers the climate conditions in the historical period, ignores the impact of climate change on clean energy resources, and leads to inaccurate clean energy resource analysis.

[0005] An embodiment of the present invention provides a clean energy resource analysis method, which is applied to a clean energy power station; the clean energy resource analysis method includes: obtaining historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range of a target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is predicted based on the preset time range under multiple global climate models and multiple climate scenarios; correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data; based on the historical observed meteorological data and the corrected meteorological data, using a pre-constructed clean energy resource analysis model for analysis to obtain a clean energy resource analysis result of the target area; the clean energy resource analysis model is trained based on the historical observed meteorological data and the corresponding historical clean energy resource analysis results.

[0006] In an optional embodiment, before performing analysis based on historical observed meteorological data and corrected meteorological data using a pre-built clean energy resource analysis model, the method also includes: obtaining ecological fragility analysis data for each sub-region within the target area based on the dimensions of ecological sensitivity, ecological pressure, and ecological resilience; performing ecological fragility rating based on the ecological fragility analysis data to obtain actual ecological fragility rating results; and eliminating sub-regions whose actual ecological fragility rating results meet preset ecological fragility rating results.

[0007] In an optional implementation, the predicted meteorological data is corrected based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data, including: performing a first correction and a first downscaling process on the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain first corrected meteorological data; performing a second correction and a second downscaling process on the first corrected meteorological data to obtain corrected meteorological data.

[0008] In an optional implementation, a first correction and a first downscaling process are performed on the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain the first corrected meteorological data, including: performing a first downscaling process on the predicted meteorological data to obtain the downscaled predicted meteorological data; performing a third downscaling process on the historical observed meteorological data and the historical simulated meteorological data to obtain the first downscaled observed meteorological data and the first downscaled simulated meteorological data; calculating the first historical prediction deviation between the first downscaled observed meteorological data and the first downscaled simulated meteorological data; performing a first correction on the downscaled predicted meteorological data based on the first historical prediction deviation to obtain the first corrected meteorological data.

[0009] In an optional implementation, the first corrected meteorological data is subjected to a second correction and a second downscaling process to obtain the corrected meteorological data, including: performing a second downscaling process on the first corrected meteorological data to obtain downscaled first corrected meteorological data; performing a fourth downscaling process on the historical observed meteorological data and the first downscaled simulated meteorological data to obtain second downscaled observed meteorological data and second downscaled simulated meteorological data; calculating a second historical prediction deviation between the second downscaled observed meteorological data and the second downscaled simulated meteorological data; and performing a second correction on the downscaled first corrected meteorological data based on the second historical prediction deviation to obtain the corrected meteorological data.

[0010] In an optional embodiment, the clean energy resource analysis model includes a water energy resource analysis sub-model, a wind energy resource analysis sub-model and a light energy resource analysis sub-model. Based on historical observed meteorological data and corrected meteorological data, a pre-built clean energy resource analysis model is used for analysis to obtain clean energy resource analysis results for the target area, including: extracting water energy resource meteorological elements, wind energy resource meteorological elements and light energy resource meteorological elements from the historical observed meteorological data and the corrected meteorological data respectively; wherein the water energy resource meteorological elements include temperature and precipitation, the wind energy resource meteorological elements include wind speed and temperature, and the light energy resource meteorological elements include total solar radiation and sunshine hours; based on the water energy resource meteorological elements, the wind energy resource meteorological elements and the light energy resource meteorological elements are respectively analyzed using the water energy resource analysis sub-model, the wind energy resource analysis sub-model and the light energy resource analysis sub-model to obtain clean energy resource analysis results.

[0011] In an optional embodiment, the method also includes: extracting historical water resources meteorological elements, historical wind resources meteorological elements and historical light resources meteorological elements from historical observed meteorological data; obtaining historical clean energy resource analysis results, wherein the historical clean energy resource analysis results include historical water resources analysis results, historical wind resources analysis results and historical light resources analysis results; performing model training based on historical water resources meteorological elements and historical water resources analysis results to obtain a water resources analysis sub-model; performing model training based on historical wind resources meteorological elements and historical wind resources analysis results to obtain a wind resources analysis sub-model; performing model training based on historical light resources meteorological elements and historical light resources analysis results to obtain a light resources analysis sub-model.

[0012] In a second aspect, the present invention provides a clean energy resource analysis device, which includes: an acquisition module for acquiring historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range in a target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is obtained by predicting multiple global climate models and multiple climate scenarios based on the preset time range; a meteorological correction module for correcting the predicted meteorological data to adapt to the preset time range to obtain corrected meteorological data; a resource analysis module for performing analysis based on historical observed meteorological data and corrected meteorological data using a pre-built clean energy resource analysis model to obtain clean energy resource analysis results; wherein the clean energy resource analysis results include resource reserves and resource stability.

[0013] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the clean energy resource analysis method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the clean energy resource analysis method of the first aspect or any corresponding embodiment thereof.

[0015] The present invention provides a clean energy resource analysis method, which is applied to a clean energy power station; the clean energy resource analysis method comprises: obtaining historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range in a target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is obtained by predicting under a variety of global climate models and a variety of climate scenarios based on the preset time range; correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data; based on the historical observed meteorological data and the corrected meteorological data, using a pre-constructed clean energy resource analysis model for analysis to obtain a clean energy resource analysis result for the target area; the clean energy resource analysis model is obtained by training based on the historical observed meteorological data and the corresponding historical clean energy resource analysis results; the above technical scheme of the present invention, in the data selection stage, on the one hand, selects historical observed meteorological data, historical simulated meteorological data and preset time The predicted meteorological data within the range can take into account the climate conditions in historical periods and the impact of climate change on clean energy when conducting clean energy resource analysis; on the other hand, the predicted meteorological data is predicted based on a preset time range under a variety of global climate models and a variety of climate scenarios, and a variety of global climate models are based on different ocean and atmosphere coupling theories, with strong structural independence, which can reduce the uncertainty of single climate model predictions, and a variety of climate scenarios can simulate the impact of human activities on the predicted climate from different development paths; in the data processing stage, the inaccuracy of predicted meteorological data when making predictions is considered, and the predicted meteorological data is corrected based on historical observation meteorological data and historical simulation meteorological data to make the predicted meteorological data more accurate, thereby improving the accuracy of clean energy resource analysis and solving the technical problem of inaccurate clean energy resource analysis caused by only considering the climate conditions in historical periods and ignoring the impact of climate change on clean energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a clean energy resource analysis method according to an embodiment of the present invention;

[0018] Figure 2 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention;

[0020] Figure 4 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention;

[0021] Figure 5 is a schematic diagram of a clean energy resource analysis device according to an embodiment of the present invention;

[0022] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] Existing analysis methods for water, wind and solar resources usually calculate recent resource evaluation results based on historical relevant data. However, the construction of clean energy infrastructure often requires advance site selection and layout, which requires huge investment and high initial investment. Once construction begins, it is difficult to undo: the design service life of wind turbines is generally 20-25 years, the design service life of photovoltaic panels is about 25 years, and the design service life of hydropower stations is even longer; the cost of suspension, delay and reconstruction is also very high. Once a wrong decision is made, it will cause huge losses; therefore, it is necessary to fully consider the impact of climate change in the early planning and design process.

[0025] To solve the above problems, according to an embodiment of the present invention, a clean energy resource analysis method embodiment 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 the 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.

[0026] In this embodiment, a clean energy resource analysis method is provided. Figure 1 is a flow chart of a clean energy resource analysis method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0027] Step S101, obtaining historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range for the target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is predicted based on the preset time range under multiple global climate models and multiple climate scenarios.

[0028] The inventors have discovered through research that the current site selection for hydropower, wind and solar power stations often only considers the climate conditions of the historical period, ignoring the impact of climate change on the hydropower, wind and solar power stations; in the present invention, in order to fully consider the impact of climate change in the early planning and design process, the preset time range is not less than the design life of the clean energy power station; as a possible implementation method, the preset time range can be the design life of the clean energy power station, illustratively, it can be 25 years; as another possible implementation method, the preset time range can be 50 years, 60 years, 100 years...; in this embodiment, in the data acquisition stage, the setting method of the preset time range can make it possible to fully consider the impact of climate change on clean energy resources when using predicted meteorological data for clean energy resource analysis.

[0029] In this embodiment, the target area is the area where clean energy resource analysis is to be performed; the historical observed meteorological data can be collected by sensors or obtained by meteorological service agencies.

[0030] In this embodiment, the historical simulated meteorological data is the result of meteorological forecasting within a historical time period, which can be determined by the simulation results of the global climate model for the historical period; the predicted meteorological data is the result of meteorological forecasting for the future period by the global climate model, which is predicted based on a preset time range under multiple global climate models and multiple climate scenarios; the technical solution for predicting with multiple global climate models, on the one hand, the meteorological simulation data from the global climate model in the historical period can be well consistent with the observed meteorological data in the historical period, and provide all key meteorological elements related to clean energy resources; on the other hand, multiple global climate models are based on different ocean and atmosphere coupling theories, have strong structural independence, and can reduce the uncertainty of single climate model predictions; and multiple climate scenarios can simulate the impact of human activities on predicted meteorology from different development paths.

[0031] Specifically, the predicted meteorological data can be determined by the predicted meteorological data of the known global climate model; illustratively, in the present embodiment, the known global climate model can come from the sixth international coupled model intercomparison project (Coupled Model Intercomparison Project 6, CMIP6); the CMIP6 framework has a variety of global climate models and a variety of climate scenarios for providing relevant meteorological data; in the present invention, 5 global climate models and 3 climate scenarios in the CMIP6 framework are selected to provide key meteorological data related to water, wind and light resources; Table 1 is the name of the global climate model selected by the present invention, and Table 2 is the name of the climate scenario selected by the present invention. Referring to Tables 1 and 2, the five global climate models are UKESM1-0-LL, MRI-ESM2-0, MPI-ESM1-2-HR, IPSL-CM6A-LR, and GFDL-ESM4 models, and the three climate scenarios are SSP5-8.5, SSP3-7.0, and SSP1-2.6.

[0032] Table 1

[0033] Global Climate Model Name nation Resolution UKESM1-0-LL U.K. ～1.875°×1.25° MRI-ESM2-0 Japan ～1.4°×1.4° MPI-ESM1-2-HR Germany ～0.9°×0.9° IPSL-CM6A-LR France ～2.5°×1.3° GFDL-ESM4 USA ～1.25°×1°

[0034] Table 2

[0035]

[0036] The technical solutions of the above five global climate models, on the one hand, the meteorological simulation data from the global climate models in historical periods (historical simulated meteorological data) can be well consistent with the observed meteorological data in historical periods (historical observed meteorological data), and can provide all the key meteorological elements related to clean energy resources; on the other hand, these five global climate models are based on different ocean and atmosphere coupling theories, have strong structural independence, and can reduce the uncertainty of single climate model predictions.

[0037] Among the three climate scenarios mentioned above, SSP5-8.5 represents fossil fuel-driven development-shared socioeconomic pathways under high radiative forcing, SSP3-7.0 represents moderate emission development-shared socioeconomic pathways under medium-to-high radiative forcing, and SSP1-2.6 represents sustainable development-shared socioeconomic pathways under low radiative forcing. For the three climate scenarios mentioned above, SSP5-8.5 is selected because it is the only one that can achieve anthropogenic radiative forcing of 8.5 W / m by 2100. 2 SSP3-7.0 is a new radiative forcing scenario, which represents a high scenario of land use change and anthropogenic field forcing factor emissions (especially SO2) for sustainable development, emphasizing the sensitivity of local climate change to land use and aerosol forcing. SSP3-7.0 represents a combination of high social vulnerability and relatively high anthropogenic radiative forcing, which is very important for the study of climate change impacts, mitigation and adaptation; SSP1-2.6 represents a scenario with the combined impact of low vulnerability, low mitigation pressure and low radiative forcing. Under this pathway, the ensemble mean warming of multiple models in 2100 relative to the pre-industrial period may be significantly lower than 2°C, so it can support the study of the 2°C temperature rise target.

[0038] Step S102, correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data.

[0039] The global climate model cannot accurately simulate the future climate, but has a certain deviation from the actual future climate, that is, there is a certain deviation between the predicted meteorological data and the actual future meteorological data. Based on this, after obtaining the predicted meteorological data, it is necessary to correct the predicted meteorological data based on the historical observation meteorological data and the historical simulation meteorological data.

[0040] Exemplarily, after obtaining historical observed meteorological data and historical simulated meteorological data, the degree to which the historical simulated meteorological data deviates from the historical observed meteorological data is determined, and the predicted meteorological data is corrected according to the degree of deviation to reduce the deviation in the predicted meteorological data, making the predicted meteorological data more accurate.

[0041] As a possible implementation method, the degree of deviation can be determined by calculating the difference between the historical observed meteorological data and the historical simulated meteorological data at the same time node; as a possible implementation method, multivariate regression fitting can be performed on the historical observed meteorological data and the historical simulated meteorological data respectively, and the degree of deviation can be determined by comparing the variable coefficients after fitting; as another possible implementation method, the degree of deviation can be determined through machine learning and deep learning methods.

[0042] In this embodiment, after the forecast meteorological data is corrected at a first spatial resolution, it is corrected at a second spatial resolution to obtain more accurate corrected meteorological data; wherein the second spatial resolution is higher than the first spatial resolution.

[0043] Step S103, based on the historical observed meteorological data and the corrected meteorological data, a pre-built clean energy resource analysis model is used to perform analysis to obtain the clean energy resource analysis results of the target area; the clean energy resource analysis model is trained based on the historical observed meteorological data and the corresponding historical clean energy resource analysis results.

[0044] In this embodiment, the input of the clean energy resource analysis model includes corrected meteorological data within a preset time range, so that the impact of climate change on clean energy evaluation is considered when conducting clean energy resource evaluation.

[0045] Exemplarily, the clean energy resource analysis model is trained based on historical observed meteorological data and corresponding historical clean energy resource analysis results; specifically, the clean energy resource analysis model may include a machine learning model, a deep learning model, etc., and the clean energy resource analysis model is used to perform classification tasks, clustering tasks, regression tasks, etc.; during the model training process, the historical observed meteorological data is input into the clean energy resource analysis model, and the model parameters of the clean energy resource analysis model are continuously adjusted so that the clean energy resource analysis model can learn the correspondence between the historical observed meteorological data and the historical clean energy resource analysis results.

[0046] The technical solution of the present invention, in the data selection stage, on the one hand, selects historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range, so that when conducting clean energy resource analysis, it can simultaneously consider the climatic conditions in the historical period and the impact of climate change on clean energy resources; on the other hand, the predicted meteorological data is predicted based on a preset time range under a variety of global climate models and a variety of climate scenarios, and the various global climate models are based on different ocean and atmosphere coupling theories, and have strong structural independence, which can reduce the uncertainty of single climate model predictions, and the various climate scenarios can simulate the impact of human activities on the predicted meteorology from different development paths; in the data processing stage, the inaccuracy of the predicted meteorological data when making predictions is considered, and the predicted meteorological data is corrected based on the historical observed meteorological data and the historical simulated meteorological data to make the predicted meteorological data more accurate, thereby solving the technical problem that the analysis method for clean energy resources only considers the climatic conditions in the historical period, ignores the impact of climate change on clean energy resources, and leads to inaccurate analysis of clean energy resources.

[0047] In the present invention, the main purpose of performing resource analysis on clean energy is to select the site for a clean energy power station; however, when a clean energy power station is constructed, it usually affects the ecological environment at the site; for example, the construction of a hydropower station often involves the construction of a dam and a large-scale reservoir, which will not only change the natural water flow, but also affect the surrounding ecological environment; the site selection of a wind power station usually needs to avoid wildlife habitats, nature reserves, scenic spots and other areas. In order to avoid causing the deterioration of the ecological environment at the site, clean energy is not developed in the above-mentioned areas, so clean energy resource analysis is not performed; based on this, in an optional implementation, in step S103, based on historical observed meteorological data and corrected meteorological data, before using a pre-constructed clean energy resource analysis model for analysis, the method also includes: obtaining ecological fragility analysis data of each sub-area in the target area based on the ecological sensitivity dimension, the ecological pressure dimension and the ecological resilience dimension; performing ecological fragility rating based on the ecological fragility analysis data to obtain an actual ecological fragility rating result; and eliminating the sub-area whose actual ecological fragility rating result meets the preset ecological fragility rating result.

[0048] In this embodiment, when the actual ecological vulnerability rating result meets the preset ecological vulnerability rating result, it indicates that the ecology of the sub-region is fragile and is not suitable for building a clean energy power station.

[0049] In this embodiment, based on the ecological fragility analysis data, the "ecological sensitivity-ecological resilience-ecological pressure" conceptual model (i.e., SRP model) can be used to perform ecological fragility rating to obtain the actual ecological fragility rating results; wherein, the SRP model is a comprehensive evaluation model specifically used to evaluate the ecological fragility of a specific region. The SRP model includes the constituent indicators of ecological fragility. By setting the weight value and index of each different indicator, the results of multiplying all the weight values ​​and indexes are accumulated, and finally the ecological fragility index is obtained.

[0050] Specifically, the indicators characterizing the ecological sensitivity dimension in the present invention are elevation, slope, slope aspect, terrain undulation, soil erosion intensity, landscape diversity index, average annual temperature, average annual precipitation, and dryness; the indicators characterizing the ecological resilience dimension are vegetation coverage and vegetation net primary productivity; and the indicators for the ecological pressure dimension are population density, proportion of cultivated land, and per capita GDP.

[0051] In this embodiment, the index data representing the ecological sensitivity dimension, the index data representing the ecological resilience dimension, and the index data representing the ecological pressure dimension are directly obtained from relevant websites, or calculated by software / model / formula; specifically, Table 3 is the main data of the SRP model of the present invention and its source, as shown in Table 3:

[0052] Table 3

[0053]

[0054]

[0055] After obtaining the above data, each indicator is standardized according to its specific properties, and principal component analysis is performed. Based on the results of principal component analysis, the calculation formula of the ecological vulnerability index is as follows:

[0056]

[0057] In formula (1), E represents the ecological vulnerability index; m j is the contribution rate corresponding to the jth principal component; P j is the jth principal component; n is the first n principal components greater than the specified cumulative contribution rate; wherein the specified cumulative contribution rate can be set according to actual conditions. In the present invention, the specified cumulative contribution rate is 85%.

[0058] After calculating the ecological vulnerability index, the larger the ecological vulnerability index is, the more fragile the ecological environment of the sub-region is; conversely, the better the ecological environment of the sub-region is.

[0059] In order to more intuitively compare the ecological fragility of each sub-region, the ecological fragility index is standardized and the calculation formula is:

[0060]

[0061] In formula (2): S E is the standardized value of the ecological vulnerability index, ranging from 0 to 10; E max is the maximum value of the ecological vulnerability index of the sub-region in the target area; E min is the minimum value of the ecological vulnerability index of the sub-regions in the target area; according to the characteristics of the study area in this embodiment, the ecological vulnerability can be classified, see Table 4 for details:

[0062] Table 4

[0063] Ecological vulnerability grade Standardized value of ecological vulnerability index Slightly fragile Ⅰ ﹤2.0 Mildly fragile Ⅱ 2.0～4.0 Moderately vulnerable Ⅲ 4.0～6.0 Severe fragility Ⅳ 6.0～8.0 Extremely vulnerable Ⅴ ≥8.0

[0064] In this embodiment, in order to eliminate the influence of different dimensions of each factor, the selected evaluation indicators are first standardized. The above ecological vulnerability evaluation indicators are divided into quantitative indicators and qualitative indicators, and the two indicators are processed into corresponding standardized values ​​using the range method and the graded value assignment method respectively; specifically, the positive indicators in the quantitative indicators include slope, slope aspect, surface undulation, landscape diversity index, population density and cultivated land proportion, and the negative indicators include elevation, average annual precipitation, average annual temperature, dryness, vegetation coverage, vegetation net primary productivity and per capita GDP; the quantitative indicator standardization calculation formula is:

[0065] Positive indicators:

[0066]

[0067] Negative indicators:

[0068]

[0069] In formulas (3) and (4): Y i represents the standardized value of the i-th initial index, ranging from 0 to 10; X i represents the data value of the i-th indicator; X max , X min Respectively represent the maximum and minimum data values ​​of indicator i.

[0070] The qualitative index is soil erosion intensity. In the present invention, a graded value assignment method is used to standardize the soil erosion intensity, and the range is set to 0-10 (as shown in Table 5).

[0071] Table 5

[0072]

[0073] In order to avoid the problem that the correlation between the indicators is too large, resulting in repeated impact on the ecological vulnerability assessment results, or the correlation between the indicators and the ecological vulnerability is too small, it is necessary to use the spatial principal component analysis method to extract the first few principal components whose cumulative contribution rate exceeds the specified cumulative contribution rate as alternative indicators for analysis. Among them, the specified cumulative contribution rate can be set according to actual conditions. In the present invention, the specified cumulative contribution rate is 85%. The calculation formula of the alternative indicator is:

[0074]

[0075] In formula (5): P j is the jth alternative indicator (principal component); Y i is the standardized value of the i-th initial indicator; m is the number of initial indicators; Z ij is the eigenvector corresponding to the jth principal component of the i-th initial indicator.

[0076] The P calculated by formula (5) j Substituting the value into formula (1), the ecological vulnerability index E can be calculated.

[0077] On this basis, the data of ecological protection areas that have been designated at this stage are superimposed (the ecological protection areas that have been designated at this stage are directly assigned a V level) to obtain the results of the comprehensive ecological evaluation, that is, the higher the level of the region, the higher the demand for ecological protection.

[0078] In the present invention, when conducting clean energy resource assessment, sub-regions with ecological fragility levels IV and V are not evaluated because the above sub-regions are areas with relatively fragile ecosystems and are not suitable for building clean energy power stations.

[0079] In an optional embodiment, Figure 2 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention; Figure 2 As shown, step S102, based on the historical observed meteorological data and the historical simulated meteorological data, the predicted meteorological data is corrected to obtain the corrected meteorological data, including:

[0080] Step S1021, performing a first correction and a first downscaling process on the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain first corrected meteorological data;

[0081] In the present invention, the global climate model cannot simulate the future climate completely accurately, but has a certain deviation from the actual future climate. To eliminate this deviation, illustratively, the difference between the historical observed meteorological data and the historical simulated meteorological data at a specified time node is calculated using the global or national spatial scale to determine the degree of deviation, and then the predicted meteorological data is corrected according to the degree of deviation.

[0082] As a possible implementation method, multivariate regression fitting can be performed on historical observed meteorological data and historical simulated meteorological data respectively, and the variable coefficients after fitting can be compared to determine the degree of deviation; as another possible implementation method, the degree of deviation can be determined through machine learning and deep learning methods, so as to perform a first correction and a first downscaling processing on the predicted meteorological data to obtain the first corrected meteorological data.

[0083] In this embodiment, an interpolation method can be used to perform a first downscaling process on the predicted meteorological data; specifically, the interpolation method is based on a statistical method to convert meteorological elements from a large scale to a small scale; illustratively, the interpolation method can be a regression method, a cluster analysis method, a pattern nesting method, an inverse distance weighted method, and the like.

[0084] For the meteorological forecast module, a slight increase in spatial resolution will greatly increase the amount of computation for the model, increase the complexity of the coupling relationship between regional climate elements, and lead to inaccurate model simulation. Therefore, the predicted meteorological data or the first corrected meteorological data can only provide meteorological data with lower spatial resolution; in order to obtain more subtle and high spatial resolution meteorological data, it also includes: step S1022, performing a second correction and a second downscaling process on the first corrected meteorological data to obtain corrected meteorological data.

[0085] In this embodiment, an interpolation method can be used to perform a second downscaling process on the first corrected meteorological data; specifically, the interpolation method is based on a statistical method to convert meteorological elements from a large scale to a small scale; illustratively, the interpolation method can be a regression method, a cluster analysis method, a pattern nesting method, an inverse distance weighted method, and the like.

[0086] In an optional embodiment, Figure 3 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention. Figure 3 As shown, step S1021, based on the historical observed meteorological data and the historical simulated meteorological data, performs a first correction and a first downscaling process on the predicted meteorological data to obtain the first corrected meteorological data, including:

[0087] Step S10211, performing a first downscaling process on the predicted meteorological data to obtain downscaled predicted meteorological data.

[0088] In this embodiment, an interpolation method can be used to perform a first downscaling process on the predicted meteorological data; specifically, the interpolation method is based on a statistical method to convert meteorological elements from a large scale to a small scale; illustratively, the interpolation method can be a regression method, a cluster analysis method, a pattern nesting method, an inverse distance weighted method, and the like.

[0089] As a possible implementation method, the predicted meteorological data is first downscaled using the inverse distance weighted method to obtain the downscaled predicted meteorological data; specifically, the calculation formula of the inverse distance weighted (IDW) method is as follows:

[0090]

[0091] In formula (6), Z is the specified meteorological element value of the grid to be estimated, i is the grid with the i-th known specified meteorological element value near the grid to be estimated, n is the total number of known grids, and d i is the distance between the grid to be estimated and the i-th known grid near the grid to be estimated (the distance between grids is represented by the distance between two grid center points), p is an interpolation parameter, and the value of p is any positive real number. In this embodiment, p=2.

[0092] The downscaled forecast meteorological data with a spatial resolution of 0.5°×0.5° were obtained by using the inverse distance weighted method.

[0093] In order to make the historical observed meteorological data and the historical simulated meteorological data adapt to the time scale of the downscaled predicted meteorological data, the method also includes: step S10212, performing a third downscaling process on the historical observed meteorological data and the historical simulated meteorological data to obtain first downscaled observed meteorological data and first downscaled simulated meteorological data.

[0094] In this embodiment, the inverse distance weighted method may be used to perform the third downscaling process on the historical observed meteorological data and the historical simulated meteorological data, and other optional implementation methods may also be used, which are not limited here.

[0095] Step S10213, calculating a first historical prediction deviation between the first downscaled observed meteorological data and the first downscaled simulated meteorological data.

[0096] Exemplarily, the first downscaled observed meteorological data and the first downscaled simulated meteorological data may be fitted respectively to obtain expressions of the first downscaled observed meteorological data and the first downscaled simulated meteorological data, and the first historical prediction deviation may be calculated through coefficients between the expressions.

[0097] Step S10214: Perform a first correction on the downscaled predicted meteorological data based on the first historical prediction deviation to obtain first corrected meteorological data.

[0098] Exemplarily, the method provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) can be used to make the first correction to the predicted meteorological data; specifically, the ISIMIP3b method is used in the present invention to make the first correction to the above 15 sets of CMIP6 future climate data (5 global climate models × 3 climate scenarios). This method better retains the spatial distribution pattern of precipitation, radiation intensity, and average temperature, and better retains the frequency trend of rainless days, which is more beneficial in the climate background of increasingly concentrated global precipitation. The first downscaling and the first correction are performed by the inverse distance weighted method and the ISIMIP3b method respectively; after the first downscaling and the first correction, the global first corrected meteorological data with a daily spatial resolution of 0.5°×0.5° are obtained.

[0099] Due to the complexity of the model, the global first-corrected meteorological data after the first downscaling and first correction by the inverse distance weighted method and the ISIMIP3b method can only provide meteorological forecast data with a spatial resolution of 0.5°×0.5°, because a slight increase in spatial resolution will greatly increase the amount of calculation for the model, enhance the complexity of the coupling relationship between regional climate elements, and lead to inaccurate model simulation; therefore, in an optional implementation, Figure 4 is a flow chart of another clean energy resource analysis method according to an embodiment of the present invention. Figure 4 As shown, step S1022, performing a second correction and a second downscaling process on the first corrected meteorological data to obtain corrected meteorological data, includes:

[0100] Step S10221: Perform a second downscaling process on the first corrected meteorological data to obtain downscaled first corrected meteorological data.

[0101] In this embodiment, an interpolation method can be used to perform a second downscaling process on the first corrected meteorological data; specifically, the interpolation method is based on a statistical method to convert meteorological elements from a large scale to a small scale; illustratively, the interpolation method can be a regression method, a cluster analysis method, a pattern nesting method, an inverse distance weighted method, etc.; through the interpolation method, the downscaled first corrected meteorological data with a spatial resolution of 0.25°×0.25° is obtained.

[0102] The ISIMIP3b method is based on the global historical meteorological data for the deviation correction of CMIP6 data. In order to ensure the accuracy of the data, after being processed by the ISIMIP3b method and the inverse distance weighted method, the present invention further performs a second correction based on the historical observation meteorological data; illustratively, the historical observation meteorological data can be China's historical observation meteorological data or historical observation meteorological data of a certain region; the accuracy of the historical observation meteorological data used in step S10222 should be higher than that of the historical observation meteorological data used in step S10212.

[0103] Step S10222: Perform a fourth downscaling process on the historical observed meteorological data and the first downscaled simulated meteorological data to obtain second downscaled observed meteorological data and second downscaled simulated meteorological data.

[0104] In this embodiment, the first corrected meteorological data is downscaled to a spatial resolution of 0.25°×0.25° by interpolation to obtain the downscaled first corrected meteorological data, and the spatial resolution of the historical observed meteorological data and the first downscaled simulated meteorological data is inconsistent with the downscaled first corrected meteorological data. Therefore, it is necessary to downscale the historical observed meteorological data and the first downscaled simulated meteorological data so that the second historical prediction deviation calculated by the historical observed meteorological data and the first downscaled simulated meteorological data can adapt to the spatial resolution of the downscaled first corrected meteorological data.

[0105] Exemplarily, the fourth downscaling process can be performed on the historical observed meteorological data and the first downscaled simulated meteorological data based on the inverse distance weighted method to obtain the second downscaled observed meteorological data and the second downscaled simulated meteorological data. The fourth downscaling process can also be performed in other optional ways, which are not limited in this embodiment.

[0106] Step S10223, calculating a second historical prediction deviation between the second downscaled observed meteorological data and the second downscaled simulated meteorological data.

[0107] For the calculation of forecast deviation, for meteorological elements such as temperature that can be negative, the absolute deviation (i.e., the second downscaled simulated meteorological data minus the second downscaled observed meteorological data) is used to calculate the absolute deviation of the monthly mean of each meteorological element in the specified historical time period:

[0108] Ye1=Yb gcm –Yo (7)

[0109] In formula (7), Ye1 represents the monthly absolute deviation of a specified meteorological element within a specified time period, and Yb gcm It represents the monthly mean of the second downscaled simulated meteorological data of the specified meteorological element within the specified time period, and Yo represents the monthly mean of the second downscaled observed meteorological data of the specified meteorological element within the specified time period.

[0110] For other meteorological elements such as precipitation, wind speed and solar radiation, relative deviations (i.e., the second downscaled simulated meteorological data divided by the second downscaled observed meteorological data) are used to calculate the relative deviations of the monthly means of each meteorological element in the specified historical period:

[0111] Ye2=Yb gcm / Yo (8)

[0112] In formula (8), Ye2 represents the monthly relative deviation of a specified meteorological element within a specified time period, and Yb gcm It represents the monthly mean of the second downscaled simulated meteorological data of the specified meteorological element within the specified time period, and Yo represents the monthly mean of the second downscaled observed meteorological data of the specified meteorological element within the specified time period.

[0113] Step S10224: Perform a second correction on the downscaled first corrected meteorological data based on the second historical prediction deviation to obtain corrected meteorological data.

[0114] In this embodiment, for meteorological elements such as temperature that can be negative in the downscaled first corrected meteorological data, the second historical prediction deviation is subtracted from such data downscaled to a spatial resolution of 0.25°×0.25°. For daily forecast meteorological data, the subtracted second historical prediction deviation is the absolute deviation of the historical period prediction of the corresponding variable of the month to which the day belongs, that is, Ye1, and the calculation formula is as follows:

[0115] Yf cor =Yf gcm -Ye1(9)

[0116] For other meteorological elements in the downscaled first corrected meteorological data, such as precipitation, wind speed, and solar radiation, the data downscaled to a spatial resolution of 0.25°×0.25° are divided by the second historical prediction deviation. For daily forecast meteorological data, the second historical prediction deviation is the relative deviation of the historical period prediction of the corresponding variable in the month to which the day belongs, that is, Ye2. The calculation formula is as follows:

[0117] Yf cor =Yf gcm / Ye2 (10)

[0118] In an optional embodiment, the clean energy resource analysis model includes a water energy resource analysis sub-model, a wind energy resource analysis sub-model and a light energy resource analysis sub-model. Based on historical observed meteorological data and corrected meteorological data, a pre-built clean energy resource analysis model is used for analysis to obtain clean energy resource analysis results for the target area, including: extracting water energy resource meteorological elements, wind energy resource meteorological elements and light energy resource meteorological elements from the historical observed meteorological data and the corrected meteorological data respectively; wherein the water energy resource meteorological elements include temperature and precipitation, the wind energy resource meteorological elements include wind speed and temperature, and the light energy resource meteorological elements include total solar radiation and sunshine hours; based on the water energy resource meteorological elements, the wind energy resource meteorological elements and the light energy resource meteorological elements are respectively analyzed using the water energy resource analysis sub-model, the wind energy resource analysis sub-model and the light energy resource analysis sub-model to obtain clean energy resource analysis results.

[0119] In the present invention, a water, wind and light resource evaluation index system is established for the evaluation of water, wind and light resources; specifically, for the water energy resource analysis indicators, the meteorological elements directly related to the water energy resource analysis are: temperature and precipitation; in addition, other meteorological elements with important contributions can be added according to the unique geographical attributes of the target area; for example, glacier elements and snow accumulation elements also have important contributions to water energy resources. When the target area is located in the above-mentioned geographical location, glacier elements and snow accumulation elements can be added for water energy resource evaluation.

[0120] Based on the above-mentioned meteorological elements and other data, as well as hydrological models, scheduling procedures, calculation formulas, etc., the comprehensive evaluation indicators of water resources that can be constructed include: runoff, theoretical reserves of water resources, hydropower generation, concentration, unevenness coefficient, and complete regulation coefficient, which can be used to evaluate the future water resources situation in the basin.

[0121] Therefore, in the present invention, the meteorological elements of water resources include temperature and precipitation, and the output of the water resources analysis sub-model includes water resources reserves and water resources stability.

[0122] In the present invention, the method for calculating the water energy resource reserves specifically includes the method for calculating the runoff, the theoretical reserves of water energy resources, and the amount of hydroelectric power generation.

[0123] Exemplarily, the method for calculating the runoff is specifically: calculating the basin runoff based on meteorological data through a hydrological model or a statistical model.

[0124] For example, the calculation method of theoretical reserves of water resources is as follows:

[0125] One of the evaluation indicators of water resources reserves is the theoretical reserves of water resources, which refers to the value of water resources existing in rivers or lakes. Its calculation is usually based on data such as runoff and elevation difference (Guidelines for Survey and Evaluation of Water Resources, SL562-2011). The theoretical reserves of water resources should be expressed in annual electricity and average power. When expressed in annual electricity, it can be calculated according to formula (11):

[0126] E 理论 =KWHg (11)

[0127] In formula (11), E 理论 is the annual electricity of theoretical reserves of hydropower resources, in kW·h; K is the conversion coefficient, K=2.778×10 -4 ; W is the average value of the multi-year average runoff of the upper and lower sections of the river, in m 3The runoff of a designated section in the historical period can be directly obtained from observation data, or simulated by a hydrological model and obtained through confluence. The runoff of a designated section in the future period can be simulated by a hydrological model and obtained through confluence. H is the water level difference between the upper and lower sections of the river section, in meters, which is calculated by analyzing the elevation data. g is the acceleration of gravity, g = 9.81 m / s 2 Expressed in terms of average power, it can be calculated using formula (12):

[0128] P 理论 =E 理论 / 8760 (12)

[0129] In formula (12), P 理论 It is the average power of theoretical reserves of hydropower resources, in kW.

[0130] Exemplarily, the method for calculating the hydroelectric power generation is as follows:

[0131] Taking into account hydrological, hydrodynamic, geographical and other factors, the daily inflow, outflow, power generation efficiency, reservoir water level and other factor indicators under different runoff conditions are calculated. With the maximum power generation benefit as the goal, a hydropower station power generation calculation model is constructed to estimate the hydropower generation of each hydropower station under runoff changes. Among them, the daily inflow in the historical period can be obtained directly from the observation data, or simulated by the hydrological model and obtained by confluence, and the inflow in the future period can be simulated by the hydrological model and obtained by confluence. The monthly water level operation status of each reservoir is relatively stable. Therefore, the historical water level of the reservoir is averaged on a monthly basis and regarded as the control water level of each reservoir. When predicting power generation, the reservoir operates according to the control water level. Without considering the loss flow of each reservoir and other water use, according to the water level and storage capacity curve of each reservoir (based on historical measured data, the corresponding relationship between reservoir storage capacity and reservoir water level is established by using the multivariate regression method), the change of storage capacity in different periods can be obtained, so as to calculate the daily outflow (Formula 13). Finally, the power generation of each reservoir in the past and future periods is deduced according to formula (14):

[0132] S t+1 =S t +I t -Q t (13)

[0133] In formula (13), S t is the reservoir water storage at the beginning of day t, in m 3 ; S t+1 is the reservoir water storage at the end of day t, in m 3 ;I t is the reservoir runoff on day t, in m 3 ;Q t is the outflow of the reservoir on day t, in m 3 .

[0134] The formula for calculating daily hydroelectric power generation is as follows:

[0135]

[0136] In formula (15), q t Represents the outflow of the reservoir through the turbine, in m 3 / s, Q calculated by formula (13) t Substitute into formula (14) to calculate; H represents the water head height, in m, which is the elevation difference between the upstream and downstream of the dam, that is, the difference between the reservoir control water level and the downstream elevation of the dam. The downstream elevation of the dam is the basic characteristic parameter of the reservoir and can be obtained by reference; η represents the power generation efficiency of the turbine, with a value of 0-1. This embodiment uses historical measured data to calculate and verify the power generation efficiency of each power station; ρ represents the density of water, with a value of 10 3 kg / m 3 ; g represents the acceleration due to gravity, and its value is 9.806m / s 2 ; t represents the time step, which is 24h when calculating daily power generation; P is power generation, in TWh.

[0137] Exemplarily, the calculation method of water energy resource stability is as follows:

[0138] Concentration, non-uniformity coefficient and complete adjustment coefficient are used to analyze the stability of water resources.

[0139] The concentration reflects the concentration of annual runoff in the study area. When the value is close to 1, it means that the annual runoff distribution is concentrated, and when it is close to 0, it means that the annual runoff distribution is evenly distributed. The calculation is shown in formula (16-18):

[0140]

[0141] In formula (16) to formula (18), RCD year is the concentration of runoff in each year; R year is the annual total runoff, in m 3 ; R x , R y is the composite vector of annual runoff in horizontal and vertical directions; r i is the runoff in the ith month of the year, in m 3 , i is the time series (i=1,2,3,…,11,12); θ i is the vector angle of the corresponding month, θ i 0°, 30°, 60°, …, 300°, 330°, corresponding to January, February, March, …, November, December respectively.

[0142] Uneven coefficient of runoff distribution in one year C vIndicates the balance of the average monthly runoff in each year, C v The larger the value, the greater the difference in the average monthly runoff within the year, and the more uneven the distribution within the year. The calculation formula is as follows:

[0143]

[0144] In formula (19), C v is the annual uneven distribution coefficient; r i is the runoff in the ith month of the year, in m 3 , i is the time series (i=1,2,3,…,11,12); is the average monthly runoff in a year, in m 3 .

[0145] Full adjustment coefficient C r The larger the value, the greater the difference in runoff volume between months and years, and the more concentrated the runoff distribution within the year, that is, the more uneven the runoff distribution within the year. The calculation formula is as follows:

[0146]

[0147] In formula (20-21), C r is the complete adjustment coefficient; r i is the runoff in the ith month of the year, in m 3 , i is the time series (i=1,2,3,…,11,12); is the average monthly runoff in a year.

[0148] Regarding the analysis of wind energy resources, the meteorological elements directly related to the analysis of wind energy resources are: wind speed and temperature.

[0149] Based on the above meteorological elements, the comprehensive evaluation indicators that can be constructed include: average wind power density, wind turbine output power, wind energy resource reserves, wind speed variability and turbulence intensity, etc., which can be used to evaluate wind energy resource reserves and wind energy resource stability.

[0150] In the present invention, the average wind power density E and the wind turbine generator set output power P are used. i The wind energy resource reserves G represent the wind energy resource reserves; specifically, for the calculation of the average wind power density E, when the wind speed is constant, the average wind power density E within a certain period of time is proportional to the cube of the average wind speed, and the calculation formula is:

[0151]

[0152] In formula (22), E represents the average wind power density, in W / m 2 ; n is the number of records within the statistical time; ρ is the air density, the unit is kg / m3 ;v i is the wind speed value of the ith record, in m / s.

[0153] In formula (22), wind speed refers to the wind speed at the hub height of the wind turbine. The wind speed data provided by the Meteorological Bureau or CMIP6 are generally the wind speed data at 10m above the ground surface. Therefore, it is necessary to use the wind shear index to uniformly convert all wind speed data to the hub height H of the wind turbine. Specifically, the calculation formula is:

[0154]

[0155] In formula (23), α is the wind shear index, dimensionless; v0, v H The wind speed height H0 and the wind turbine hub height H are respectively known. H The wind speed at the location is in m / s.

[0156] The wind shear index α is determined as follows: When there are wind speed data at multiple heights, the least square method is used to fit the power-law wind speed profile to determine the wind shear index. This method is based on the complete wind speed profiles at multiple heights and can comprehensively reflect the wind shear conditions. The wind speed at any other unmeasured height can also be found on the wind speed profile. When there are only wind speed data at two heights, the wind shear index is calculated according to the following wind shear power law formula (24). Based on the calculated wind shear index and the known wind speed at a certain height, the wind speed at any other height is calculated by formula (23), that is, the vertical extrapolation of the wind speed.

[0157]

[0158] Where α is the wind shear index, dimensionless; H1 and H2 are the heights, in meters; V1 is the wind speed at height H1, in meters per second; V2 is the wind speed at height H2, in meters per second.

[0159] Air density also varies due to regional and climate changes. Air density is related to air temperature and altitude. Air density (unit: kg / m 3 ) is calculated as:

[0160] ρ=(353.05 / T)exp -0.034(z / T) (25)

[0161] In formula (25), T is the annual average air absolute temperature in Kelvin, in K, and z is the altitude, in m.

[0162] For wind turbine output power P i The output power P of the wind turbine generator set is calculated by the unit kW. i The calculation formula is:

[0163]

[0164] In formula (26), R is the impeller radius of the wind turbine generator set, in meters; C p is the wind energy utilization coefficient of the wind turbine generator set, which is dimensionless and can be calculated using the impeller radius, wind speed and unit output power with actual measurement records in historical periods; v i is the wind speed, in m / s; ρ is the air density, in kg / m 3 .

[0165] For the calculation of wind energy resource reserves G, the wind energy resource reserves in a certain area can be calculated by the distribution of the local average wind power density. The average wind power density is calculated as 50W / m 2 The intervals are increased and divided into contour lines. The calculation formula is:

[0166]

[0167] In formula (27), G is the wind energy resource reserves, in kW; m is the average wind power density level, dimensionless; s i is the area between the average wind power density contour lines in the average wind power density distribution diagram, in m 2 ; E i is the representative value of wind power density between the average wind power density contour lines. In this embodiment, the average wind power density is 50W / m 2 The interval level increases, so the representative value of the average wind power density can be taken as: E1 = 25W / m 2 (0-50W / m 2 Regional average wind power density representative value), E2 = 75W / m 2 (50-100W / m 2 Regional average wind power density representative value), and so on.

[0168] In the present invention, the wind speed variation rate SI and turbulence intensity I are used. r Characterize the stability of wind energy resources. Specifically, since China is mainly affected by the monsoon climate, the seasonal variation of near-surface wind speed in China is significant. In the present invention, the index defined by Walsh is used to calculate the fluctuation of wind speed on a monthly scale to characterize the wind speed variability SI. Specifically, the calculation formula is:

[0169]

[0170] In formula (28), v is the multi-year average wind speed in the study area within the preset time period, in m / s; iIt is the multi-year average wind speed in the study area in the i-th month within the preset time period, in m / s. The smaller the SI value, the smaller the wind speed difference on the monthly scale.

[0171] For turbulence intensity I r In the present invention, the turbulence intensity I r It is used to express the relative level of fluctuating wind speed during atmospheric movement, describing the degree of wind speed variation over time and space, that is, the standard deviation σ of wind speed and the average wind speed The ratio of is as follows:

[0172]

[0173] In formula (29), σ is the standard deviation of the fluctuating wind speed, in m / s; is the local annual average wind speed in m / s.

[0174] Therefore, in the present invention, the meteorological elements of wind energy resources include wind speed and temperature, and the output of the wind energy resource analysis sub-model is the wind energy resource reserves and wind energy resource stability.

[0175] The analysis of light energy resources is usually divided into light energy resource reserves and light energy resource stability. The former is expressed by annual total solar radiation, and the latter is expressed by the ratio of the maximum and minimum values ​​of the number of days with sunshine hours greater than 6 hours in each month. In the present invention, the meteorological elements related to light energy resource analysis include total solar radiation and sunshine hours.

[0176] When evaluating the reserves of light energy resources in a target area, the most direct and accurate method is to use the total solar radiation data obtained by actual observation. The inventors have found that there are often problems with insufficient number and uneven distribution of solar radiation observation sites, and the measured data produced is not sufficient to meet the needs of light energy resource evaluation. Therefore, in the present invention, the total solar radiation reaching the ground in a region is obtained by indirect calculation, see formula 30 and formula 31 for details:

[0177] GHR m =EHR m ·(a+b·s) (30)

[0178] s=n / N (31)

[0179] Compared with solar radiation observation stations, meteorological stations are more numerous and widely distributed. The sunshine hours are the observation variables of meteorological stations. Therefore, the sunshine hours n (unit: hour) and the sunshine hours N (unit: hour) can be used to calculate the sunshine percentage s at the corresponding time (Formula 31), and then the total solar radiation can be estimated (Formula 30).

[0180] In formula (30), GHR mis the monthly total horizontal radiation (unit: MJ / m 2 ), namely the total solar radiation on a monthly scale; EHR m is the solar radiation on the lunar surface (unit: MJ / m 2 );a, b are empirical coefficients; s is the monthly sunshine percentage (unit: %).

[0181] EHR m It can be obtained by summing up the solar radiation of the horizontal surface outside the Earth every day of the month. d The calculation formula is:

[0182]

[0183] In formula (32): EHR d is the solar radiation on the horizontal surface outside the sun (unit: MJ / m 2 ); is the local latitude (unit: °); δ is the solar declination (unit: °), and the calculation formula is:

[0184]

[0185] ENDI is the extraterrestrial normal solar irradiance (unit: W / m 2 ), the calculation formula is:

[0186]

[0187] In formula (33) and formula (34), d is the cumulative day, that is, the ordinal number of the date in a year. January 1st takes the ordinal number 1, and so on; E n is the solar constant, E n =1366.1W / m 2 .

[0188] ω is the hour angle of sunset (unit: °), and the calculation formula is:

[0189]

[0190] The monthly total solar radiation and corresponding sunshine percentage of the solar radiation observation station within a certain period are obtained, and the empirical coefficients a and b are fitted using the least squares method; further, the sunshine percentage data of the surrounding meteorological stations can be used to adopt the fitted formula to calculate the corresponding total solar radiation of the surrounding meteorological stations.

[0191] In the present invention, for the evaluation of light energy resource stability, due to the earth's revolution, the solar radiation intensity will change periodically on a year-round scale. Therefore, the ratio of the maximum to the minimum number of days with sunshine hours greater than 6h in each month (K) is usually used to represent the light energy resource stability, and the calculation formula is:

[0192]

[0193] In formula (36), K is the light energy resource stability index, dimensionless; M1, M2, … , M 12 is the number of days with sunshine duration greater than 6h in each month from January to December; max() is the standard function for finding the maximum value; min() is the standard function for finding the minimum value. The smaller the K value, the more stable the light energy resources are on a yearly scale, and the more conducive to the utilization of light energy resources.

[0194] Therefore, in the present invention, the meteorological elements of light energy resources include total solar radiation and sunshine hours, and the output of the light energy resource analysis sub-model is the light energy resource reserves and light energy resource stability.

[0195] In an optional embodiment, the method also includes: extracting historical water resources meteorological elements, historical wind resources meteorological elements and historical light resources meteorological elements from historical observed meteorological data; obtaining historical clean energy resource analysis results, wherein the historical clean energy resource analysis results include historical water resources analysis results, historical wind resources analysis results and historical light resources analysis results; performing model training based on historical water resources meteorological elements and historical water resources analysis results to obtain a water resources analysis sub-model; performing model training based on historical wind resources meteorological elements and historical wind resources analysis results to obtain a wind resources analysis sub-model; performing model training based on historical light resources meteorological elements and historical light resources analysis results to obtain a light resources analysis sub-model.

[0196] In this embodiment, the water energy resource analysis sub-model, the wind energy resource analysis sub-model and the light energy resource analysis sub-model may include a machine learning model, a deep learning model, etc.; specifically, when training the model, the historical water energy resource meteorological elements are input into the water energy resource analysis sub-model, and the model parameters of the water energy resource analysis sub-model are continuously adjusted so that the output results of the model are more inclined to correspond to the historical water energy resource analysis results; the historical wind energy resource meteorological elements are input into the wind energy resource analysis sub-model, and the model parameters of the wind energy resource analysis sub-model are continuously adjusted so that the output results of the model are more inclined to correspond to the historical wind energy resource analysis results; the historical light energy resource meteorological elements are input into the light energy resource analysis sub-model, and the model parameters of the light energy resource analysis sub-model are continuously adjusted so that the output results of the model are more inclined to correspond to the historical light energy resource analysis results.

[0197] In this embodiment, a clean energy resource analysis device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0198] This embodiment provides a clean energy resource analysis device, such as Figure 5 As shown, including:

[0199] Acquisition module 501 is used to acquire historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range in the target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is predicted by multiple global climate models and multiple climate scenarios based on the preset time range.

[0200] The weather correction module 502 is used to correct the predicted weather data to adapt to a preset time range to obtain corrected weather data.

[0201] The resource analysis module 503 is used to perform analysis based on historical observed meteorological data and corrected meteorological data using a pre-built clean energy resource analysis model to obtain clean energy resource analysis results; wherein the clean energy resource analysis results include resource reserves and resource stability.

[0202] 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.

[0203] The clean energy resource analysis device in this embodiment 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.

[0204] The embodiment of the present invention also provides a computer device having the above Figure 5 The clean energy resource analysis device shown.

[0205] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, 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. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the 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 A processor 10 is taken as an example.

[0206] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated 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.

[0207] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0208] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to 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 arranged 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.

[0209] 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.

[0210] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0211] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, 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 hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. 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.

[0212] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A clean energy resource analysis method, characterized in that: Applied in clean energy power stations; The clean energy resource analysis method comprises: Acquire historical observed meteorological data, historical simulated meteorological data, and predicted meteorological data within a preset time range for the target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is predicted based on the preset time range under multiple global climate models and multiple climate scenarios; Correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data; Based on the historical observed meteorological data and the corrected meteorological data, a pre-built clean energy resource analysis model is used to perform analysis to obtain a clean energy resource analysis result of the target area; the clean energy resource analysis model is trained based on the historical observed meteorological data and the corresponding historical clean energy resource analysis results; Before performing analysis based on the historical observed meteorological data and the corrected meteorological data using a pre-built clean energy resource analysis model, the method further includes: Based on the dimensions of ecological sensitivity, ecological pressure and ecological resilience, obtain ecological vulnerability analysis data of each sub-region within the target area; Performing an ecological vulnerability rating based on the ecological vulnerability analysis data to obtain an actual ecological vulnerability rating result; Eliminate sub-areas whose actual ecological vulnerability rating results meet the preset ecological vulnerability rating results; The clean energy resource analysis model includes a water resource analysis sub-model, a wind resource analysis sub-model and a light resource analysis sub-model. The clean energy resource analysis results of the target area are obtained by using a pre-built clean energy resource analysis model based on the historical observed meteorological data and the corrected meteorological data, including: Extracting water resources meteorological elements, wind resources meteorological elements and light resources meteorological elements from the historical observation meteorological data and the corrected meteorological data respectively; wherein the water resources meteorological elements include temperature, precipitation, glacier elements and snow accumulation, the wind resources meteorological elements include wind speed and temperature, and the light resources meteorological elements include total solar radiation and sunshine hours; Based on the meteorological elements of water resources, wind resources and light resources, the water resources analysis sub-model, wind resources analysis sub-model and light resources analysis sub-model are used to perform analysis respectively, so as to obtain the analysis results of the clean energy resources; wherein the indicators of clean energy resources include meteorological element indicators and comprehensive evaluation indicators, and the reserves and stability of clean energy are evaluated, while considering the impact of climate change on clean energy resources; The step of correcting the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain corrected meteorological data includes: Based on the historical observed meteorological data and the historical simulated meteorological data, the predicted meteorological data is subjected to a first correction and a first downscaling process to obtain first corrected meteorological data; Performing a second correction and a second downscaling process on the first corrected meteorological data to obtain the corrected meteorological data; The first correction and the first downscaling process are performed on the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain the first corrected meteorological data, including: Performing a first downscaling process on the predicted meteorological data to obtain downscaled predicted meteorological data; Performing a third downscaling process on the historical observed meteorological data and the historical simulated meteorological data to obtain first downscaled observed meteorological data and first downscaled simulated meteorological data; Calculating a first historical prediction deviation between the first downscaled observed meteorological data and the first downscaled simulated meteorological data; performing a first correction on the downscaled predicted meteorological data based on the first historical prediction deviation to obtain first corrected meteorological data; The predicted meteorological data is subjected to a first downscaling process to obtain downscaled predicted meteorological data, including: Wherein, Z is the specified meteorological element value of the grid to be estimated, i is the grid with the i-th known specified meteorological element value near the grid to be estimated, n is the total number of known grids, di is the distance between the grid to be estimated and the i-th known grid near the grid to be estimated, p is the interpolation parameter, and the value of p is any positive real number, and Z(xi) is the specified meteorological element value of the known grid; The performing a second correction and a second downscaling process on the first corrected meteorological data to obtain the corrected meteorological data includes: Performing a second downscaling process on the first corrected meteorological data to obtain downscaled first corrected meteorological data; Performing a fourth downscaling process on the historical observed meteorological data and the first downscaled simulated meteorological data to obtain second downscaled observed meteorological data and second downscaled simulated meteorological data; wherein, after the predicted meteorological data is corrected at the first spatial resolution, the second spatial resolution is corrected to obtain an accurate downscaled first corrected meteorological number, and the second spatial resolution is greater than the first spatial resolution; calculating a second historical prediction deviation between the second downscaled observed meteorological data and the second downscaled simulated meteorological data; The downscaled first corrected meteorological data is subjected to a second correction based on the second historical prediction deviation to obtain the corrected meteorological data; wherein the accuracy of the historical observed meteorological data subjected to the first downscaling process is lower than the accuracy of the historical observed meteorological data subjected to the second downscaling process.

2. The clean energy resource analysis method according to claim 1, characterized in that: The method further comprises: Extracting historical hydropower resource meteorological elements, historical wind energy resource meteorological elements, and historical light energy resource meteorological elements from the historical observed meteorological data; Obtaining historical clean energy resource analysis results, wherein the historical clean energy resource analysis results include historical water energy resource analysis results, historical wind energy resource analysis results, and historical light energy resource analysis results; Performing model training based on the historical hydropower resources meteorological elements and the historical hydropower resources analysis results to obtain the hydropower resources analysis sub-model; Performing model training based on the historical wind energy resource meteorological elements and the historical wind energy resource analysis results to obtain the wind energy resource analysis sub-model; Model training is performed based on the historical solar energy resource meteorological elements and the historical solar energy resource analysis results to obtain the solar energy resource analysis sub-model.

3. A clean energy resource analysis device, characterized in that: The device comprises: An acquisition module is used to acquire historical observed meteorological data, historical simulated meteorological data and predicted meteorological data within a preset time range of a target area; wherein the preset time range is not less than the design life of the clean energy power station, and the predicted meteorological data is predicted by multiple global climate models and multiple climate scenarios based on the preset time range; A meteorological correction module, used for correcting the predicted meteorological data to adapt to the preset time range to obtain corrected meteorological data; A resource analysis module, for performing analysis based on the historical meteorological observation data and the corrected meteorological data using a pre-built clean energy resource analysis model to obtain a clean energy resource analysis result; wherein the clean energy resource analysis result includes resource reserves and resource stability; wherein, before performing analysis based on the historical meteorological observation data and the corrected meteorological data using a pre-built clean energy resource analysis model, it also includes: Based on the dimensions of ecological sensitivity, ecological pressure and ecological resilience, obtain ecological vulnerability analysis data of each sub-region within the target area; Performing an ecological vulnerability rating based on the ecological vulnerability analysis data to obtain an actual ecological vulnerability rating result; Eliminate sub-areas whose actual ecological vulnerability rating results meet the preset ecological vulnerability rating results; The clean energy resource analysis model includes a water resource analysis sub-model, a wind resource analysis sub-model and a light resource analysis sub-model. The clean energy resource analysis results of the target area are obtained by using a pre-built clean energy resource analysis model based on the historical observed meteorological data and the corrected meteorological data, including: Extracting water resources meteorological elements, wind resources meteorological elements and light resources meteorological elements from the historical observation meteorological data and the corrected meteorological data respectively; wherein the water resources meteorological elements include temperature, precipitation, glacier elements and snow accumulation, the wind resources meteorological elements include wind speed and temperature, and the light resources meteorological elements include total solar radiation and sunshine hours; Based on the meteorological elements of water resources, wind resources and light resources, the water resources analysis sub-model, wind resources analysis sub-model and light resources analysis sub-model are used to perform analysis respectively, so as to obtain the analysis results of the clean energy resources; wherein the indicators of clean energy resources include meteorological element indicators and comprehensive evaluation indicators, and the reserves and stability of clean energy are evaluated, while considering the impact of climate change on clean energy resources; A meteorological correction module, configured to perform a first correction and a first downscaling process on the predicted meteorological data based on the historical observed meteorological data and the historical simulated meteorological data to obtain first corrected meteorological data; Performing a second correction and a second downscaling process on the first corrected meteorological data to obtain the corrected meteorological data; The weather correction module is also used for: Performing a first downscaling process on the predicted meteorological data to obtain downscaled predicted meteorological data; Performing a third downscaling process on the historical observed meteorological data and the historical simulated meteorological data to obtain first downscaled observed meteorological data and first downscaled simulated meteorological data; Calculating a first historical prediction deviation between the first downscaled observed meteorological data and the first downscaled simulated meteorological data; Based on the first historical prediction deviation, the downscaled predicted meteorological data is first corrected to obtain first corrected meteorological data; wherein, Z is the specified meteorological element value of the grid to be estimated, i is the grid with the i-th known specified meteorological element value near the grid to be estimated, n is the total number of known grids, d i is the distance between the grid to be estimated and the i-th known grid near the grid to be estimated, p is the interpolation parameter, and the value of p is any positive real number. i ) is a specified meteorological element value of a known grid; wherein, the first corrected meteorological data is subjected to a second downscaling process to obtain downscaled first corrected meteorological data; the historical observed meteorological data and the first downscaled simulated meteorological data are subjected to a fourth downscaling process to obtain second downscaled observed meteorological data and second downscaled simulated meteorological data; wherein, after the predicted meteorological data is corrected at a first spatial resolution, it is corrected at a second spatial resolution to obtain accurate corrected meteorological data, and the second spatial resolution is greater than the first spatial resolution; the second historical prediction deviation between the second downscaled observed meteorological data and the second downscaled simulated meteorological data is calculated; based on the second historical prediction deviation, the downscaled first corrected meteorological data is subjected to a second correction to obtain the corrected meteorological data; wherein the accuracy of the historical observed meteorological data targeted by the first downscaling process is lower than the accuracy of the historical observed meteorological data targeted by the second downscaling process.

4. 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 the clean energy resource analysis method according to any one of claims 1 to 2 by executing the computer instructions.

5. 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 the clean energy resource analysis method according to any one of claims 1 to 2.

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