A method and system for quantitatively detecting local climate effects of new energy stations

Abstract

The application provides a method and system for quantitatively detecting local climate effect of a new energy station, which comprises the following steps: establishing a local climate numerical model of the new energy station; carrying out numerical simulation tests to obtain test results under two conditions of having and not having the new energy station based on the same test conditions; determining parameter values of a local climate effect component index relationship according to a pre-set local climate effect component index relationship and the obtained test results and real climate observation data; and determining a relationship between the new energy station and local climate change according to the parameter values. The method and system can effectively detect the local climate effect caused by the new energy station and its quantitative degree, which is beneficial to evaluating whether large-scale new energy stations can significantly change the local climate environment, and is helpful for reasonable planning and environmental protection of the new energy stations in China.

Description

Technical Field

[0001] This invention relates to the field of detection technology, and more specifically, to a method and system for quantitatively detecting the local climate effects of new energy power plants. Background Technology

[0002] Large-scale development and utilization of new energy sources is the only way to address the increasingly severe environmental and climate change pressures and achieve low-carbon, green development. As the proportion of new energy in energy consumption gradually increases, the large-scale development of new energy power plants will inevitably alter the surface environment and local climate conditions. For example, wind farms will change surface roughness and atmospheric boundary layer height, while photovoltaic power plants will change surface roughness, surface albedo, and atmospheric boundary layer height, further impacting local climate. Current research indicates that after the construction of new energy power plants, local temperatures rise, precipitation increases, and wind speeds decrease. However, the local climate change caused by new energy power plants comprises two parts: local natural climate change and anthropogenic climate change caused by the power plants. Current methods can only detect both as a whole, not separately. Therefore, it is currently impossible to determine the quantitative extent of the local climate effects of new energy power plants, nor can it be inferred whether the construction of large-scale new energy power plants will cause significant changes in local climate. Summary of the Invention

[0003] To address the limitation of existing technologies for detecting local climate change at renewable energy power plants, which can only comprehensively detect both local natural climate change and anthropogenic climate change caused by the power plants, this invention provides a method and system for quantitatively detecting the local climate effects of renewable energy power plants.

[0004] According to one aspect of the present invention, the present invention provides a method for quantitatively detecting the local climate effects of new energy power stations, the method comprising:

[0005] Modeling of new energy power stations is based on existing local climate numerical models, and local climate numerical models of new energy power stations are constructed.

[0006] Based on the local climate numerical model of the new energy power station, under the same experimental conditions, multiple sets of numerical simulation experiments based on random perturbations were carried out for two scenarios: with and without new energy power stations, and the corresponding experimental results X were obtained. art，i and X nat，i The identical test conditions include the same test area, the same test time period, and the same random perturbation parameters, where 1 ≤ i ≤ N, and N is a natural number.

[0007] Obtain real climate observation data Y for the region where the numerical simulation experiment is conducted during the experimental period. i ;

[0008] Based on the established relationships between local climate effect component indicators, the data Y... i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the parameters include local natural climate change factors, anthropogenic change factors of new energy power stations, and climate noise factors.

[0009] The relationship between new energy power plants and local climate change is determined based on the parameter values ​​of the local climate effect component index relationship.

[0010] Optionally, a local climate numerical model for new energy power plants can be constructed based on existing local climate numerical models, including:

[0011] A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or

[0012] A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

[0013] Optionally, based on the established relationships between local climate effect component indicators, the data Y is... i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the expression for the relationship between the local climate effect component indicators is:

[0014] Y i =β nat X nat，i +β art X art，i +ε i

[0015] In the formula, β nat ,β art , ε i These include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors.

[0016] Optionally, the relationship between new energy power plants and local climate change is determined based on the parameter values ​​of the local climate effect component index relationship, including:

[0017] For the local natural climate change factor β nat And the artificially altered factor β of new energy power stations art The value of β was determined by performing a significance t-test. natand β art The p-value;

[0018] When β art When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station.

[0019] When β art Not equal to 0, and β art The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

[0020] According to another aspect of the present invention, the present invention provides a system for quantitatively detecting the local climate effects of new energy power stations, the system comprising:

[0021] The model building unit is used to model new energy power stations based on existing local climate numerical models and construct local climate numerical models for new energy power stations.

[0022] The simulation test unit is used to conduct multiple sets of numerical simulation tests based on random perturbations under the same test conditions, for both scenarios with and without new energy power stations, based on the local climate numerical model of the new energy power station, and to obtain the corresponding test results X. art，i and X nat，i The same test conditions include the same test area, the same test time period, and the same random perturbation parameters, where 1≤i≤N and N is a natural number;

[0023] Climate observation units are used to acquire real climate observation data Y for the region where numerical simulation experiments are conducted during the experimental period. i ;

[0024] The parameter determination unit is used to determine the data Y based on the set local climate effect component index relationship. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the parameters include local natural climate change factors, anthropogenic change factors of new energy power stations, and climate noise factors.

[0025] The quantitative detection unit is used to determine the relationship between new energy power plants and local climate change based on the parameter values ​​of the local climate effect component index relationship.

[0026] Optionally, the model building unit models the new energy power station based on existing local climate numerical models, constructing a local climate numerical model for the new energy power station, including:

[0027] A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or

[0028] A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

[0029] Optionally, the parameter determination unit determines the data Y based on the set local climate effect component index relationship. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the expression for the relationship between the local climate effect component indicators is:

[0030] Y i =β nat X nat，i +β art X art，i +ε i

[0031] In the formula, β nat ,β art , ε i These include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors.

[0032] Optionally, the quantitative detection unit includes:

[0033] The T-detection subunit is used to detect the local natural climate change factor β. nat And the artificially altered factor β of new energy power stations art The value of β was determined by performing a significance t-test. nat and β art The p-value;

[0034] The detection results sub-unit is used to determine the relationship between local climate change and renewable energy power plants, including:

[0035] When β art When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station.

[0036] When β art Not equal to 0, and β art The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

[0037] The method and system for quantitatively detecting the local climate effects of renewable energy power plants provided by this invention establishes a local climate numerical model of renewable energy power plants. Numerical simulation experiments are conducted under the same experimental conditions in both scenarios—with and without renewable energy power plants—to obtain experimental results. Based on pre-set relationships between local climate effect components and indices, as well as the obtained experimental results and real climate observation data, parameter values ​​for these relationships are determined. The relationship between renewable energy power plants and local climate change is then determined based on these parameter values. This method and system utilize the established local climate model of renewable energy power plants to simulate the effects of climate change. Based on the established model, climate observation data, multiple linear regression analysis, and the constructed relationships between local climate effect components of renewable energy power plants, the method can effectively detect the local climate effects caused by renewable energy power plants and their quantitative extent. This is beneficial for assessing whether large-scale renewable energy power plants can significantly alter the local climate environment and contributes to the rational planning and environmental protection of renewable energy power plants in my country. Attached Figure Description

[0038] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0039] Figure 1 This is a flowchart illustrating a method for quantitatively detecting the local climate effects of new energy power stations according to a preferred embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of a system for quantitatively detecting the local climate effects of new energy power stations according to a preferred embodiment of the present invention. Detailed Implementation

[0041] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0042] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0043] Figure 1 This is a flowchart illustrating a method for quantitatively detecting the local climate effects of new energy power plants according to a preferred embodiment of the present invention. Figure 1As shown, the method for quantitatively detecting the local climate effects of new energy power stations according to this preferred embodiment begins with step 101.

[0044] In step 101, the new energy power station is modeled based on the existing local climate numerical model to construct the local climate numerical model of the new energy power station.

[0045] Preferably, the local climate numerical model of the new energy power station is constructed based on the existing local climate numerical model, including:

[0046] A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or

[0047] A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

[0048] In one embodiment, the existing local climate numerical model may be WRF or RegCM. The renewable energy power station includes at least one of wind farms and photovoltaic power stations.

[0049] In step 102, based on the local climate numerical model of the new energy power station, under the same experimental conditions, multiple sets of numerical simulation experiments based on random perturbations are carried out for two scenarios: with and without new energy power stations, and the corresponding experimental results X are obtained. art，i and X nat，i The same test conditions include the same test area, the same test time period, and the same random perturbation parameters, where 1≤i≤N and N is a natural number.

[0050] In one embodiment, using the established local climate numerical model for renewable energy power stations, numerical simulations are conducted for both scenarios with and without renewable energy power stations, with the experimental area, experimental time period, and random disturbance parameters set to the same values. Assuming the simulation period is N years, a set of experimental results can be obtained for each year's numerical simulation, denoted as X. art，i and X nat，i , where X art，i X represents the experimental results for year i obtained through numerical simulation under the condition of having renewable energy power plants. nat，i The results are from the numerical simulation conducted in the i-th year, assuming no renewable energy power plants are available.

[0051] In step 103, obtain the actual climate observation data Y of the region where the numerical simulation experiment is conducted during the experimental period. i .

[0052] In one embodiment, when the time period for conducting the numerical module experiment is historical time, the real climate observation data for each year within the corresponding time period can be obtained based on existing climate observations.

[0053] In step 104, based on the established relationships between local climate effect component indicators, the data Y is processed. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect components, wherein the parameters include local natural climate change factors, anthropogenic change factors of new energy power stations, and climate noise factors.

[0054] Preferably, the data Y is processed according to the established relationships between local climate effect component indicators. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the expression for the relationship between the local climate effect component indicators is:

[0055] Y i =β nat X nat，i +β art X art，i +ε i

[0056] In the formula, β nat ,β art , ε i These include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors.

[0057] In step 105, the relationship between new energy power plants and local climate change is determined based on the parameter values ​​of the relationship between the local climate effect component indicators.

[0058] Preferably, determining the relationship between new energy power plants and local climate change based on the parameter values ​​of the local climate effect component index relationship includes:

[0059] For the local natural climate change factor β nat And the artificially altered factor β of new energy power stations art The value of β was determined by performing a significance t-test. nat and β art The p-value;

[0060] When β art When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station.

[0061] When β artNot equal to 0, and β art The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

[0062] In one embodiment, by adjusting the local natural climate change factor β nat And the artificially altered factor β of new energy power stations art A significance t-test was performed on these two factors, taking into account the local natural climate change factor β. nat And the artificially altered factor β of new energy power stations art The impact on local climate change is thus fully guaranteed based on the anthropogenic alteration factor β of new energy power plants. art The value of , and its P-value, are used to determine the accuracy of identifying a correlation between local climate change and renewable energy power plants.

[0063] In summary, the method for quantitatively detecting the local climate effects of new energy power stations described in this preferred embodiment utilizes the principle of simulating climate change through an established local climate model of the new energy power stations. Based on the established model, climate observation data, multiple linear regression analysis, and the constructed relationships between the components of the local climate effects of new energy power stations, the method can effectively detect the local climate effects caused by new energy power stations and their quantitative extent. This is beneficial for assessing whether large-scale new energy power stations can significantly alter the local climate environment and contributes to the rational planning and environmental protection of new energy power stations in my country.

[0064] Figure 2 This is a schematic diagram of a system for quantitatively detecting the local climate effects of new energy power stations according to a preferred embodiment of the present invention. Figure 2 As shown, the system for quantitatively detecting the local climate effects of new energy power stations according to this preferred embodiment includes:

[0065] Model building unit 201 is used to model new energy power stations based on existing local climate numerical models and construct local climate numerical models for new energy power stations.

[0066] Simulation test unit 202 is used to conduct multiple sets of numerical simulation tests based on random perturbations under the same test conditions, for both scenarios with and without new energy power stations, based on the local climate numerical model of the new energy power station, and to obtain the corresponding test results X. art，i and X nat，i The same test conditions include the same test area, the same test time period, and the same random perturbation parameters, where 1≤i≤N and N is a natural number;

[0067] Climate observation unit 203 is used to acquire real climate observation data Y of the region where the numerical simulation experiment is conducted during the experimental period. i ;

[0068] Parameter determination unit 204 is used to determine the data Y according to the set local climate effect component index relationship. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the parameters include local natural climate change factors, anthropogenic change factors of new energy power stations, and climate noise factors.

[0069] The quantitative detection unit 205 is used to determine the relationship between new energy power plants and local climate change based on the parameter values ​​of the relationship between the local climate effect component indicators.

[0070] Preferably, the model building unit 201 models the new energy power station based on an existing local climate numerical model, constructing a local climate numerical model for the new energy power station, including:

[0071] A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or

[0072] A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

[0073] Preferably, the parameter determination unit 204 determines the data Y based on the set local climate effect component index relationship. i Experimental results X art，i and X nat，i Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect component indicators, wherein the expression for the relationship between the local climate effect component indicators is:

[0074] Y i =β nat X nat，i +β art X art，i +ε i

[0075] In the formula, β nat ,β art , ε i These include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors.

[0076] Preferably, the quantitative detection unit 205 includes:

[0077] T-detection subunit 251 is used to detect the local natural climate change factor β. nat And the artificially altered factor β of new energy power stations art The value of β was determined by performing a significance t-test. nat and β art The p-value;

[0078] Subunit 252 of the detection results is used to determine the relationship between local climate change and new energy power plants, wherein: when β art When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station.

[0079] When β art Not equal to 0, and β art The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

[0080] The steps of the system for quantitatively detecting the local climate effect of new energy power stations described in this preferred embodiment to determine the relationship between new energy power stations and local climate change are the same as those of the method for quantitatively detecting the local climate effect of new energy power stations described in this invention, and the technical effects achieved are also the same, so they will not be repeated here.

[0081] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0082] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively detecting the local climate effects of new energy power stations, characterized in that, The method includes: Modeling of new energy power stations is based on existing local climate numerical models, and local climate numerical models of new energy power stations are constructed. Based on the local climate numerical model for renewable energy power stations, under the same experimental conditions, multiple sets of numerical simulation experiments based on random perturbations were conducted for both scenarios with and without renewable energy power stations, and the corresponding experimental results were obtained. and The identical test conditions include the same test area, the same test time period, and the same random perturbation parameters. , It is a natural number; Obtain real-world climate observation data for the region where the numerical simulation experiment will be conducted during the experimental period. ; Based on the established relationships between local climate effect component indicators, the data... Test results and Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect components, wherein the parameters include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors. The expression for the relationship between the local climate effect components is as follows: In the formula, , , These include local natural climate alteration factors, anthropogenic alteration factors from new energy power plants, and climate noise factors. The relationship between new energy power plants and local climate change is determined based on the parameter values ​​of the local climate effect component index relationship, including: For the aforementioned local natural climate change factors And artificially altered factors at new energy power stations The value was subjected to a significance t-test to determine... and The p-value; when When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station. when Not equal to 0, and The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

2. The method according to claim 1, characterized in that, Based on existing local climate numerical models, a model for the local climate of new energy power stations is constructed, including: A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

3. A system for quantitatively detecting the local climate effects of new energy power plants, characterized in that, The system includes: The model building unit is used to model new energy power stations based on existing local climate numerical models and construct local climate numerical models for new energy power stations. The simulation test unit is used to conduct multiple sets of numerical simulation tests based on random perturbations under the same test conditions, for both scenarios with and without new energy power stations, based on the local climate numerical model of the new energy power station, and to obtain the corresponding test results. and The identical test conditions include the same test area, the same test time period, and the same random perturbation parameters. , It is a natural number; Climate observation units are used to acquire real-world climate observation data for the region where numerical simulation experiments are conducted during the experimental period. ; The parameter determination unit is used to determine the data based on the set relationships between local climate effect component indicators. Test results and Multiple linear regression analysis was performed to determine the parameter values ​​of the relationship between the local climate effect components, wherein the parameters include local natural climate change factors, anthropogenic change factors from new energy power plants, and climate noise factors. The expression for the relationship between the local climate effect components is as follows: In the formula, , , These include local natural climate alteration factors, anthropogenic alteration factors from new energy power plants, and climate noise factors. A quantitative detection unit is used to determine the relationship between new energy power plants and local climate change based on the parameter values ​​of the local climate effect component index relationship. The quantitative detection unit includes: The T-detection subunit is used to detect the local natural climate change factors. And artificially altered factors at new energy power stations The value was subjected to a significance t-test to determine... and The p-value; The detection results sub-unit is used to determine the relationship between local climate change and renewable energy power plants, including: when When the value equals 0, it is determined that no significant human-induced changes can be detected, and local climate change is unrelated to the renewable energy power station. when Not equal to 0, and The p-value is less than 0.1, which confirms that information about anthropogenic changes can be detected in climate observations, and the detection has a 90% confidence level, indicating that new energy power plants can cause local climate change.

4. The system according to claim 3, characterized in that, The model building unit models new energy power plants based on existing local climate numerical models, constructing local climate numerical models for these power plants, including: A local climate numerical model for wind farms is constructed by modifying the surface drag coefficient relationship, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models; and / or A local climate numerical model for photovoltaic power plants is constructed by modifying the surface albedo, surface roughness coefficient, and surface turbulent kinetic energy in existing local climate numerical models.

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