Method and system for evaluating spatio-temporal heterogeneity of wind and light resources, electronic device and medium

Abstract

This invention relates to the field of energy geography and resource optimization technology, and discloses a method, system, electronic device, and medium for assessing the spatiotemporal heterogeneity of wind and solar resources. The method includes: acquiring a multi-source constrained dataset of a target area; obtaining a spatiotemporal distribution matrix of wind and solar resources based on the multi-source constrained dataset; obtaining an optimal development layout scheme on the Pareto front based on the spatiotemporal distribution matrix of wind and solar resources and a preset multi-objective collaborative optimization model; generating a comprehensive evaluation result based on the optimal development layout scheme; the comprehensive evaluation result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecology synergy indicators under multiple scenarios, and a table of key site performance indicators. This invention can improve the scientific nature and comprehensive benefits of wind and solar resource planning.

Description

Technical Field

[0001] This invention relates to the field of energy geography and resource optimization technology, specifically to methods, systems, electronic devices, and media for assessing the spatiotemporal heterogeneity of wind and solar resources. Background Technology

[0002] With the development of technologies in the fields of wind and solar resource assessment and new energy planning, resource assessment methods based on geographic information systems and remote sensing measurements have emerged. These technologies can quickly identify the macroscopic distribution characteristics of wind and solar energy, thus forming a traditional assessment method with site interpolation and spatial modeling as its core.

[0003] Traditional techniques typically rely on historical climate data, combined with basic geographic data, and employ spatial interpolation and static parameter simulation methods to homogenize or estimate regional wind and solar resources at low resolution, neglecting the dynamic changes in resources under the influence of complex terrain and underlying surface.

[0004] However, current traditional assessment methods not only neglect the dynamic heterogeneity of resources caused by complex terrain and underlying surfaces, but also fail to consider the long-term evolution trend of resource endowment under future climate change scenarios. In addition, existing methods are mostly limited to resource potential assessment itself, failing to coordinate and optimize multiple constraints such as grid absorption capacity and ecological protection red lines with power plant layout. This results in low spatiotemporal resolution and large local errors in the assessment results, and makes it difficult to support long-term scientific planning and development decisions for wind and solar power farms under climate adaptability and multi-objective coordination. Summary of the Invention

[0005] This invention provides a method, system, electronic device, and medium for assessing the spatiotemporal heterogeneity of wind and solar resources, in order to solve the problem of insufficient planning scientificity and comprehensive benefits caused by traditional wind and solar resource assessment methods neglecting spatiotemporal heterogeneity, future climate change, and the synergy of multiple constraints.

[0006] In a first aspect, the present invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources, the method comprising: Obtain the multi-source constraint dataset for the target region; Based on the multi-source constrained dataset, the spatiotemporal distribution matrix of wind and solar resources is obtained; Based on the spatiotemporal distribution matrix of wind and solar resources and a pre-set multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto frontier is obtained. Based on the optimal development layout scheme, a comprehensive evaluation result is generated; the comprehensive evaluation result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecology synergy indicators under multiple scenarios, and a table of key site performance indicators.

[0007] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. It constructs a spatiotemporal distribution matrix of wind and solar resources by acquiring a multi-source constraint dataset of the target region. Further, based on the spatiotemporal distribution matrix and a pre-defined multi-objective collaborative optimization model, it obtains the optimal development layout scheme on the Pareto front. Finally, based on the optimal development layout scheme, it generates a comprehensive assessment result. The comprehensive assessment result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecological synergy indicators under multiple scenarios, and a table of key site performance indicators. This significantly improves the spatiotemporal resolution, planning scientificity, and comprehensive economic-grid-ecological benefits of wind and solar resource assessment. It provides systematic and quantifiable technical support for the long-term scientific planning and collaborative optimization decision-making of wind and solar power plants, solving the problem of insufficient planning scientificity and comprehensive benefits caused by traditional wind and solar resource assessment methods that ignore spatiotemporal heterogeneity, future climate change, and the synergy of multiple constraints.

[0008] In one alternative implementation, the multi-source constraint dataset includes multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data; Based on the multi-source constrained dataset, the spatiotemporal distribution matrix of wind and solar resources is obtained, including: Based on multi-scenario climate model data, the data is processed by a dynamic downscaling and bias correction fusion algorithm to obtain wind and solar climate elements. Based on wind and solar climate elements and geographic information data, the theoretical power density distribution sequence of wind and solar resources was obtained; Based on power grid structure data and ecological constraint data, the development suitability correction of the theoretical power density distribution sequence of wind and solar resources is carried out to generate the effective power density distribution sequence of wind and solar resources. Based on the effective power density distribution sequence of wind and solar resources, a spatiotemporal distribution matrix of wind and solar resources is constructed.

[0009] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. Based on multi-scenario climate data and using dynamic downscaling and bias correction techniques, high-precision wind and solar climate elements are extracted. The theoretical power density is then quantified using geographic information data. Furthermore, grid connection costs and ecological constraint coefficients are incorporated to jointly correct the theoretical sequence, generating an effective power density distribution that better reflects actual development conditions and limitations. The final integrated spatiotemporal distribution matrix not only significantly improves the spatiotemporal resolution and accuracy of resource assessment, but more importantly, it internalizes actual constraints such as grid absorption capacity and ecological protection red lines into the basis for resource evaluation. This provides a more realistic and operable spatiotemporal quantitative foundation for wind and solar resources for subsequent planning.

[0010] In one optional implementation, based on power grid structure data and ecological constraint data, the theoretical power density distribution sequence of wind and solar resources is modified for development suitability to generate an effective power density distribution sequence of wind and solar resources, including: Based on the power grid structure data, the power grid access cost coefficient of each spatial grid cell in the target area is obtained; Ecological constraint data is classified into ecological sensitivity levels, and ecological constraint coefficients are generated for each spatial grid cell within the target area. Based on the grid access cost coefficient and the ecological constraint coefficient, a joint correction coefficient is obtained; The effective power density distribution sequence of wind and solar resources is obtained by weighting the theoretical power density distribution sequence of wind and solar resources using joint correction coefficients.

[0011] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. By spatially quantifying the grid connection cost coefficient and the ecological constraint coefficient, and fusing them to generate a joint correction coefficient, the theoretical resource potential is refined and weighted. This internalizes the economic constraints of grid absorption and the red line constraints of ecological protection into the core factors of resource evaluation. The resulting effective power density distribution sequence of wind and solar resources not only reflects the natural endowment of the resources but also more accurately represents their actual exploitable value under real-world conditions, significantly improving the engineering applicability and decision support of the resource assessment results.

[0012] In one alternative implementation, the joint correction factor is calculated using the following formula: ; in, It is a joint correction factor. It is the grid connection cost coefficient. It is the maximum grid connection cost. It is the ecological constraint coefficient. It is the highest level of ecological constraint. It is the attenuation factor.

[0013] In one alternative implementation, the optimal development layout scheme includes the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power plants. Based on the optimal development layout scheme, a comprehensive evaluation result is generated, including: Based on the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power stations, a resource potential level distribution map is generated; Based on power grid structure data and ecological constraint data, a development suitability zoning map is generated through spatial overlay analysis; Based on multi-scenario climate model data and optimal development layout schemes, a radar chart of resource-load-ecological synergy indicators under multiple scenarios is generated. Quantitatively integrate the optimal development layout scheme and the spatiotemporal distribution matrix of wind and solar resources to generate a table of key site performance indicators; By integrating resource potential level distribution maps, development suitability zoning maps, radar charts of resource-load-ecology synergy indicators under multiple scenarios, and performance index tables of key sites, a comprehensive evaluation result is obtained.

[0014] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. By deeply integrating the optimal development layout scheme with multi-source constraint data, it systematically generates four types of visualized and quantitative results: resource potential distribution, development suitability zoning, multi-scenario synergistic benefits, and key site performance. These results are then integrated to transform complex optimization results into multi-level, multi-dimensional, and intuitively readable decision support information. This comprehensively reveals the integrated performance of planning schemes in terms of resource endowment, spatial constraints, long-term climate adaptability, and specific site economics, significantly improving the completeness, interpretability, and practical value of the assessment results for direct use in planning decisions.

[0015] In one optional implementation, a development suitability zoning map is generated based on power grid structure data and ecological constraint data through spatial overlay analysis, including: Perform suitability analysis on the power grid structure data to generate a grid access cost grading map; Based on ecological constraint data, an ecological sensitivity assessment is conducted to generate an ecological protection level zoning map. The grid access cost classification map and the ecological protection level zoning map are merged to generate a development suitability zoning map; the development suitability zoning map includes priority development areas, optimized development areas and restricted development areas.

[0016] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. By conducting spatial hierarchical evaluations of grid access costs and ecological sensitivity, and by overlaying and fusing the two types of zoning maps, it can transform complex grid economic constraints and ecological protection requirements into unified and intuitive spatial development access guidelines. This generates a suitability zoning map that clearly distinguishes between priority, optimization, and restricted development areas, effectively realizing the spatial coupling and visual expression of multiple constraints.

[0017] In one optional implementation, the preset multi-objective collaborative optimization model is obtained in the following manner: Historical sample data of the multi-source constrained dataset within the target area is obtained. Based on the historical sample data, the spatiotemporal dynamic features of wind and solar resources are extracted. The spatiotemporal dynamic features of wind and solar resources are spatiotemporally aligned with the power grid load and ecological background data of the target area to obtain the training sample set. The ecological background data is spatial raster data extracted based on ecological constraint data. Based on the training sample set, an initial mathematical model is generated with the system levelized cost, power grid net load fluctuation rate and ecological cumulative impact as optimization objectives, and power grid transmission capacity and ecological protection scope as constraints. A non-dominated sorting genetic algorithm is used to iteratively solve the initial mathematical model to obtain a set of candidate solutions; Based on the candidate solution set, the Pareto non-dominated solution set is obtained by fast non-dominated sorting and crowding calculation; When the Pareto non-dominated solution set obtained by consecutive iterations satisfies the preset convergence condition and the generalization error of the initial mathematical model is lower than the preset threshold, the iteration stops, and the model parameters corresponding to the final stable Pareto non-dominated solution set are determined as the parameters of the multi-objective collaborative optimization model, thus obtaining the multi-objective collaborative optimization model.

[0018] This invention provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources. It constructs training samples by strictly aligning historical dynamic characteristics of wind and solar resources with grid load and ecological baseline in both time and space, enabling the model to learn the complex coupling patterns between resources, grid, and ecology from real data. Furthermore, it establishes a mathematical model with system cost, grid fluctuations, and ecological impact as core objectives, and grid capacity and protection red lines as hard constraints. A non-dominated sorting genetic algorithm is used for efficient solution and selection. Finally, the model's stability and reliability are ensured through rigorous dual criteria of convergence and generalization error. This results in a collaborative optimization model that can automatically search for economical, stable, and environmentally friendly Pareto optimal solutions under multiple constraints, possessing strong real-world interpretation and decision support capabilities.

[0019] Secondly, this invention provides a system for assessing the spatiotemporal heterogeneity of wind and solar resources, the system comprising: The multi-source data acquisition module is used to acquire multi-source constrained datasets for the target area; The matrix generation module is used to obtain the spatiotemporal distribution matrix of wind and solar resources based on a multi-source constrained dataset; The collaborative optimization module is used to obtain the optimal development layout scheme on the Pareto frontier based on the spatiotemporal distribution matrix of wind and solar resources and a preset multi-objective collaborative optimization model. The evaluation result generation module is used to generate comprehensive evaluation results based on the optimal development layout scheme. The comprehensive evaluation results include at least one of the following: resource potential level distribution map, development suitability zoning map, resource-load-ecology synergy index radar chart under multiple scenarios, and key site performance index table.

[0020] Thirdly, the present invention provides an electronic device, comprising: 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 computer instructions to perform the method for assessing the spatiotemporal heterogeneity of wind and solar resources described in the first aspect or any corresponding embodiment.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for assessing the spatiotemporal heterogeneity of wind and solar resources as described in the first aspect or any corresponding embodiment.

[0022] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the method for assessing the spatiotemporal heterogeneity of wind and solar resources described in the first aspect or any corresponding embodiment. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a system for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0029] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0030] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0031] It should be noted that, Figure 1 This is merely an example of an application scenario and does not limit the scope of protection of this invention.

[0032] The embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the pages shown in the drawings are merely examples, and various page designs are possible in practice. The various graphic elements on the page may have different arrangements and different visual representations, one or more elements may be omitted or replaced, and one or more other elements may also be present; no limitations are imposed in the embodiments of the present invention. Furthermore, the embodiments are mainly described below with respect to terminal device 110. It should be understood that the actions described relative to terminal device 110 can be performed by application 101 on terminal device 110, or by application 101 in conjunction with its server (e.g., server 120). According to an embodiment of the present invention, an embodiment of a method for assessing the spatiotemporal heterogeneity of wind and solar resources is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0033] This embodiment provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources, which can be used in the aforementioned terminal equipment or electronic equipment (e.g., assessment terminal). Figure 2 This is a flowchart of a method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the multi-source constraint dataset for the target region.

[0034] Specifically, the target area refers to the specific geographical range to be evaluated and optimized in the spatiotemporal heterogeneity assessment method for wind and solar resources in this embodiment.

[0035] A multi-source constrained dataset can be a collection of various types, dimensions, and constrained spatial and attribute data used for assessing the spatiotemporal heterogeneity of wind and solar resources. For example, it can include at least: multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data.

[0036] Multi-scenario climate model data can be future climate data simulated based on different greenhouse gas emission pathways (e.g., climate elements directly related to wind and solar resources such as wind speed, solar radiation, and temperature, as well as auxiliary verification data such as precipitation and air pressure); geographic information data can be at least one of the spatial characteristics of the target area, such as topography, land use, vegetation cover, and surface elevation; power grid structure data can be at least one of the data related to power grid topology, equipment parameters, operating status, and access costs; ecological constraint data can be at least one of the information related to ecological protection, such as ecological protection boundaries, ecological sensitivity, and biodiversity distribution in the target area. The assessment terminal can collect or obtain multi-source constraint datasets of the target area in batches or by targeting by connecting to authoritative data platforms and databases in the fields of meteorology, natural resources, power grid, and ecological environment. This dataset includes multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data.

[0037] Step S202: Based on the multi-source constrained dataset, obtain the spatiotemporal distribution matrix of wind and solar resources.

[0038] Among them, the spatiotemporal distribution matrix of wind and solar resources can be presented in matrix form to quantify the effective power density of wind and solar energy in different time dimensions (such as hours, days, and seasons) and spatial grid cells.

[0039] Specifically, the evaluation terminal can use the acquired multi-source constraint dataset as the core basis, integrate key information from multi-scenario climate model data, geographic information data, power grid structure data and ecological constraint data, and through data integration, feature extraction and spatiotemporal dimension mapping, the system quantifies the distribution characteristics of wind and solar resources in the target area at different time dimensions and spatial locations, and finally generates a spatiotemporal distribution matrix of wind and solar resources.

[0040] Step S203: Based on the spatiotemporal distribution matrix of wind and solar resources and the preset multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto frontier is obtained.

[0041] The pre-defined multi-objective collaborative optimization model can be composed of a training sample processing unit, a multi-objective mathematical modeling unit, an iterative solution unit, and a solution set selection unit. It achieves multi-objective collaborative optimization through a non-dominated sorting genetic algorithm. The input is the spatiotemporal distribution matrix of wind and solar resources, and it also integrates historical sample data from a multi-source constrained dataset (including spatiotemporal dynamic characteristics of wind and solar resources, power grid load data, and ecological background data). The input data needs to undergo spatiotemporal alignment preprocessing. The output is the optimal development layout scheme on the Pareto frontier.

[0042] Step S204: Based on the optimal development layout scheme, generate a comprehensive evaluation result; the comprehensive evaluation result includes at least one of the following: resource potential level distribution map, development suitability zoning map, resource-load-ecology synergy index radar chart under multiple scenarios, and key site performance index table.

[0043] The comprehensive evaluation result can be a collection of various visualization and analysis results generated based on the optimal development layout scheme.

[0044] Specifically, the evaluation terminal can, based on the optimal development layout scheme and combined with key information from multi-source constraint datasets, generate at least one of the following results through data visualization, spatial analysis and quantitative integration: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecological synergy indicators under multiple scenarios, and a table of key site performance indicators. Then, the various results are integrated and summarized to generate a comprehensive evaluation result that fully reflects the development potential, suitability, synergistic benefits and site performance of wind and solar resources in the target area.

[0045] The method for assessing the spatiotemporal heterogeneity of wind and solar resources provided in this embodiment constructs a spatiotemporal distribution matrix of wind and solar resources by acquiring a multi-source constraint dataset of the target area. Further, based on the spatiotemporal distribution matrix of wind and solar resources and a preset multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto front is obtained. Finally, based on the optimal development layout scheme, a comprehensive assessment result is generated. The comprehensive assessment result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecological synergy indicators under multiple scenarios, and a table of key site performance indicators. This significantly improves the spatiotemporal resolution, planning scientificity, and comprehensive economic-grid-ecological benefits of wind and solar resource assessment. It provides systematic and quantifiable technical support for the long-term scientific planning and collaborative optimization decision-making of wind and solar power plants, solving the problem of insufficient planning scientificity and comprehensive benefits caused by traditional wind and solar resource assessment methods neglecting spatiotemporal heterogeneity, future climate change, and the synergy of multiple constraints.

[0046] This embodiment provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources, which can be used in the aforementioned terminal equipment or electronic equipment (e.g., assessment terminal). Figure 3 This is a flowchart of a method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain the multi-source constraint dataset for the target region. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0047] Step S302: Based on the multi-source constrained dataset, obtain the spatiotemporal distribution matrix of wind and solar resources.

[0048] Specifically, the multi-source constraint dataset includes multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data; step S302 above includes: Step S3021: Based on multi-scenario climate model data, the multi-scenario climate model data is processed by a dynamic downscaling and bias correction fusion algorithm to obtain wind and solar climate elements.

[0049] Among them, wind and solar climate elements can be meteorological parameters extracted from raw climate data that can be directly used to calculate the theoretical power generation capacity of wind and solar energy.

[0050] Specifically, the assessment terminal can first refine the low-resolution global / regional climate model data into high-precision spatial raster-scale data of the target area using a dynamic downscaling algorithm, based on the acquired multi-scenario climate model data, to accurately capture the impact of complex terrain and underlying surfaces on climate elements; then, it can use a bias correction method to correct the systematic bias between model data and actual values ​​by combining historical measured meteorological data of the target area, thereby improving data reliability; through the collaborative processing of the above fusion algorithms, wind and solar climate elements such as wind speed, solar radiation, and temperature that can be directly used to calculate the theoretical power generation capacity of wind and solar energy can be accurately extracted from the original climate data.

[0051] Step S3022: Based on wind and solar climate elements and geographic information data, obtain the theoretical power density distribution sequence of wind and solar resources.

[0052] Among them, the theoretical power density distribution sequence of light resources can be a dataset of the maximum exploitable power density of wind and solar energy at each spatial location (grid) within the target area, varying over time.

[0053] Specifically, the evaluation terminal can perform spatiotemporal correlation matching between extracted wind, solar and climate elements (such as wind speed, solar radiation, temperature, etc.) and geographic information data of the target area (such as topography, surface elevation, land use type, etc.) to quantify the theoretical power generation capacity of each spatial grid unit under different time dimensions; then, by integrating the wind and solar power density data of each grid unit that dynamically changes over time, a theoretical power density distribution sequence of wind and solar resources is generated.

[0054] Step S3023: Based on power grid structure data and ecological constraint data, the development suitability correction is performed on the theoretical power density distribution sequence of wind and solar resources to generate the effective power density distribution sequence of wind and solar resources.

[0055] Specifically, the evaluation terminal can calculate the grid access cost coefficient of each spatial grid unit in the target area based on the grid structure data, and then classify the ecological constraint data into ecological sensitivity levels to determine the ecological constraint coefficient of each grid unit. By fusing the two types of coefficients, a joint correction coefficient is obtained. Subsequently, the joint correction coefficient is used to weight and correct the theoretical power density distribution sequence of wind and solar resources, and invalid power density data that do not meet the grid access conditions and ecological protection requirements are eliminated. Finally, an effective power density distribution sequence of wind and solar resources that can truly reflect the actual development feasibility is generated.

[0056] In some optional implementations, step S3023 above includes: Step a1: Based on the power grid structure data, obtain the power grid access cost coefficient for each spatial grid cell within the target area.

[0057] Among them, the grid access cost coefficient can be used to quantify the relative ease and cost of connecting the electricity generated by a wind farm or photovoltaic power station to the existing grid system when a wind farm or photovoltaic power station is built in a specific geographical location (usually in spatial grid units) within the target area.

[0058] Specifically, the assessment terminal can systematically analyze the acquired ecological constraint data, extract core constraint information such as ecological protection boundaries, ecological sensitivity levels, and biodiversity distribution, and then use the spatial grid units of the target area as the basic assessment unit. Based on the preset ecological sensitivity evaluation standards, it can classify the ecological protection importance and development restriction degree of each grid unit, and then quantify and generate ecological constraint coefficients that can reflect the strength of ecological constraints of each spatial grid unit.

[0059] Step a2: Classify the ecological constraint data into ecological sensitivity levels and generate the ecological constraint coefficients of each spatial grid cell within the target area.

[0060] Among them, the ecological constraint coefficient can be used to quantify the pressure or risk level that wind and solar power plant development may cause to the ecological environment in a specific geographical location (usually in spatial grid units) within the target area, as well as the protection priority of the ecological value of the area.

[0061] Specifically, the assessment terminal can extract key information such as the ecological protection boundary range, biodiversity distribution area, and core evaluation factors of ecological sensitivity. Using the spatial grid unit of the target area as the basic assessment unit, and based on the preset ecological protection priority and development risk classification standards, it comprehensively judges and classifies the ecological value importance, potential ecological pressure and risk level of each grid unit. Subsequently, through standardized quantitative processing, the ecological constraint characteristics of different levels are transformed into numerical forms that can be used for subsequent calculations, generating ecological constraint coefficients that can accurately reflect the strength of ecological constraints of each spatial grid unit.

[0062] Step a3: Based on the grid access cost coefficient and the ecological constraint coefficient, obtain the joint correction coefficient.

[0063] Among them, the joint correction coefficient can be a correction index that integrates the constraints of the grid access cost coefficient and the ecological constraint coefficient.

[0064] Specifically, the evaluation terminal can use the maximum grid access cost and the maximum ecological constraint level in the target area as benchmark parameters, and then normalize the grid access cost coefficient and ecological constraint coefficient of each spatial grid unit to eliminate the dimensional differences of data from different dimensions. Subsequently, a preset fusion calculation rule is used to couple the two types of coefficients after normalization to finally generate a joint correction coefficient.

[0065] In one alternative implementation, the joint correction factor is calculated using the following formula: (1); in, It is a joint correction factor. It is the grid connection cost coefficient. It is the maximum grid connection cost. It is the ecological constraint coefficient. It is the highest level of ecological constraint. It is the attenuation factor.

[0066] Step a4: Use joint correction coefficients to perform weighted calculations on the theoretical power density distribution sequence of wind and solar resources to obtain the effective power density distribution sequence of wind and solar resources.

[0067] Among them, the effective power density distribution sequence of wind and solar resources can be a dataset of the actual exploitable power density of wind and solar energy at each spatial location (grid) within the target area, varying over time.

[0068] Specifically, the assessment terminal can identify the joint correction coefficients corresponding to each spatial grid cell within the target area, and then match them with the theoretical power density data of the corresponding grid and time dimension in the theoretical power density distribution sequence of wind and solar resources. Subsequently, the theoretical power density data of each spatiotemporal cell are weighted using the joint correction coefficients as weights, fully integrating the dual constraints of grid access cost and ecological protection, and eliminating invalid power density values ​​that do not meet the actual development conditions. By systematically integrating the weighted calculation results of all spatiotemporal cells, a dataset of the actual exploitable power density of wind and solar energy that dynamically changes over time on each spatial grid cell within the target area is generated, namely, the effective power density distribution sequence of wind and solar resources.

[0069] In this embodiment, the evaluation terminal quantifies the development suitability of each spatial location by introducing a mathematical formula that integrates the dual constraints of the power grid and the ecology. This transforms theoretical resource potential into effective potential that reflects actual development conditions, providing a basis for optimizing the layout.

[0070] Step S3024: Based on the effective power density distribution sequence of wind and solar resources, integrate and construct the spatiotemporal distribution matrix of wind and solar resources.

[0071] Specifically, the evaluation terminal can clearly define the spatial grid division standards and time dimension division rules of the target area (such as dividing time units by hour, day, and season), and then map the generated effective power density distribution sequence of wind and solar resources to each spatial grid unit and time unit according to the corresponding rules; by systematically integrating, aligning and reconstructing the effective power density data of different spatiotemporal units, a spatiotemporal distribution matrix of wind and solar resources is constructed.

[0072] In this embodiment, the evaluation terminal quantifies grid access costs and ecological constraints, generates joint correction coefficients to accurately correct theoretical resource potential, and obtains an effective power density that reflects actual development feasibility, providing a data foundation for the scientific planning of wind and solar resources.

[0073] Step S303: Based on the spatiotemporal distribution matrix of wind and solar resources and the preset multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto frontier is obtained.

[0074] The optimal development layout scheme can be the Pareto front optimal solution output by the multi-objective collaborative optimization model, including the latitude and longitude coordinates, installed capacity and energy storage capacity configuration of wind farms and photovoltaic power plants.

[0075] Specifically, the evaluation terminal takes the constructed spatiotemporal distribution matrix of wind and solar resources as input, and integrates historical sample data from a multi-source constrained dataset (including spatiotemporal dynamic characteristics of wind and solar resources, power grid load data, and ecological background data) that has undergone spatiotemporal alignment preprocessing. This data is then input into a pre-trained multi-objective collaborative optimization model. This model consists of a training sample processing unit, a multi-objective mathematical modeling unit, an iterative solution unit, and a solution set selection unit. It achieves multi-objective collaborative optimization through a non-dominated sorting genetic algorithm, ultimately outputting the optimal development layout scheme on the Pareto frontier.

[0076] Step S304: Based on the optimal development layout scheme, generate a comprehensive evaluation result. The comprehensive evaluation result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecology synergy indicators under multiple scenarios, and a table of key site performance indicators. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0077] The spatiotemporal heterogeneity assessment method for wind and solar resources provided in this embodiment uses an assessment terminal that integrates multi-source data from climate, geography, power grid, and ecology to progressively refine the theoretical potential of wind and solar resources into effective potential reflecting actual development feasibility. This provides a precise and reliable spatiotemporal quantitative basis for subsequent optimized layout. The assessment terminal quantifies the development suitability of each spatial location by introducing mathematical formulas that integrate both power grid and ecological constraints, transforming theoretical resource potential into effective potential reflecting actual development conditions and providing a basis for optimal layout.

[0078] This embodiment provides a method for assessing the spatiotemporal heterogeneity of wind and solar resources, which can be used in the aforementioned terminal equipment or electronic equipment (e.g., assessment terminal). Figure 4 This is a flowchart of a method for assessing the spatiotemporal heterogeneity of wind and solar resources according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: Obtain the multi-source constraint dataset for the target region. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0079] Step S402: Based on the multi-source constrained dataset, obtain the spatiotemporal distribution matrix of wind and solar resources. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0080] Step S403: Based on the spatiotemporal distribution matrix of wind and solar resources and the preset multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto frontier is obtained.

[0081] Specifically, the multi-objective collaborative optimization model preset in step S403 above is obtained in the following way: Step S4031: Obtain historical sample data of the multi-source constraint dataset within the target area, extract the spatiotemporal dynamic features of wind and solar resources based on the historical sample data, and align the spatiotemporal dynamic features of wind and solar resources with the corresponding power grid load and ecological background data of the target area in a spatiotemporal manner to obtain the training sample set; the ecological background data is spatial raster data extracted based on ecological constraint data.

[0082] Among them, the spatiotemporal dynamic characteristics of wind and solar resources can be the changing patterns, fluctuation modes, and distribution differences of wind energy and resources in the time and space dimensions.

[0083] The training sample set can be a collection of known inputs and expected outputs used to train a multi-objective collaborative optimization model.

[0084] Specifically, the evaluation terminal can retrieve historical sample data from a multi-source constraint dataset, including historical multi-scenario climate model data, geographic information data, power grid structure data, and historical records of ecological constraint data. It then extracts the fluctuation patterns, seasonal variations, and regional distribution differences of wind and solar energy at different times to form spatiotemporal dynamic features of wind and solar resources. Following this, using timestamps and spatial grid coordinates as a benchmark, the extracted spatiotemporal dynamic features are precisely spatiotemporally aligned with historical power grid load data and ecological baseline data within the corresponding target area, ensuring that all types of data within the same spatiotemporal unit are matched one-to-one. Finally, the aligned data is cleaned and standardized, and integrated to form a training sample set containing input features (spatiotemporal dynamic features, power grid load, and ecological baseline data) and corresponding label information.

[0085] The process of realizing ecological baseline data is as follows: First, the original ecological constraint data of the target area (such as ecological protection red lines, nature reserve boundaries, biodiversity distribution, etc.) is analyzed and evaluated, and divided into different levels according to the ecological sensitivity criteria; then, the evaluation results of this level are mapped and transformed with the unified spatial raster system of the target area, and each raster unit is assigned a quantitative ecological constraint coefficient, thereby generating a spatially continuous and numerical raster dataset, which is the ecological baseline data that can be directly used for subsequent spatial analysis and model calculation.

[0086] Step S4032: Based on the training sample set, an initial mathematical model is generated with the system levelized cost, power grid net load fluctuation rate and ecological cumulative impact as optimization objectives, and the power grid transmission capacity and ecological protection scope as constraints.

[0087] The levelized cost of a system can be the ratio of the total investment (including construction, operation, maintenance, and energy storage costs) over the entire life cycle of a wind and solar power plant in the target area to the total expected power generation over the entire life cycle. It is used to quantitatively evaluate the comprehensive cost per unit of electricity of a wind and solar power generation system.

[0088] The net load fluctuation rate of the power grid can be the ratio of the fluctuation range of the actual load of the power grid in the target area and the difference between the output of wind and solar power plants (net load) to the average value over a certain time scale.

[0089] Ecological cumulative impact can be the sum of the superimposed and long-term impacts of the layout, construction, power generation and other activities of wind and solar power plants on the target area's ecosystem (such as biodiversity, ecological red lines and ecosystem service functions) throughout the entire life cycle of wind and solar power plant development (including construction, operation and decommissioning).

[0090] The power grid transmission capacity can be the maximum power load limit that the existing or planned power grid in the target area can accept and transmit under the premise of safe and stable operation.

[0091] Ecological protection areas can be boundaries of regions with special ecological value or requiring key protection, defined based on ecological constraint data. These include ecological protection red lines, core distribution areas of biodiversity, and ecologically sensitive areas.

[0092] The initial mathematical model can be a multi-objective optimization mathematical expression built based on the training sample set, which is the basic framework of the multi-objective collaborative optimization model.

[0093] Specifically, the evaluation terminal can identify core decision variables based on the training sample set, namely the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power plants. Then, it constructs mathematical expressions for three types of optimization objectives: minimizing system levelized cost by quantifying the ratio of total lifecycle investment to total power generation; minimizing grid net load volatility by characterizing the proportional relationship between net load volatility and average value; and minimizing cumulative ecological impact by integrating the cumulative impact on the ecosystem throughout the entire development cycle. Simultaneously, it embeds constraint expressions, limiting grid transmission capacity to no more than the maximum load limit and ensuring that power plant layout does not exceed ecological protection boundaries, ultimately integrating these to generate an initial mathematical model.

[0094] Step S4033: The initial mathematical model is iteratively solved using a non-dominated sorting genetic algorithm to obtain a candidate solution set.

[0095] Among them, the non-dominated sorting genetic algorithm is an evolutionary algorithm used to solve multi-objective optimization problems. By simulating biological genetic and evolutionary mechanisms, it encodes, selects, crosses, and mutates the solutions of the initial mathematical model. Combining non-dominated sorting and crowding calculation, it selects the optimal solution and finally obtains the Pareto non-dominated solution set. It is the core solution tool for training multi-objective collaborative optimization models.

[0096] The candidate solution set can be a set of multiple potential optimal solutions that satisfy constraints such as power grid transmission capacity and ecological protection range, generated in each iteration when the initial mathematical model is solved iteratively using a non-dominated sorting genetic algorithm.

[0097] Specifically, the evaluation terminal can encode the core decision variables of the initial mathematical model (latitude and longitude coordinates of wind farms and photovoltaic power stations, installed capacity, and energy storage capacity configuration) into binary to construct an initial population. Then, based on the optimization objectives of system levelized cost, grid net load volatility, and cumulative ecological impact, combined with the constraints of grid transmission capacity and ecological protection scope, the fitness value of each individual in the population is calculated. High-quality individuals are selected by roulette wheel selection, and a new generation of population is generated by single-point crossover and random mutation strategies, while retaining the best individual in each generation. The above selection, crossover, and mutation iteration process is repeated, and all potential optimization solutions that meet the constraints in each iteration are integrated and summarized to obtain a candidate solution set.

[0098] Step S4034: Based on the candidate solution set, the Pareto non-dominated solution set is obtained by fast non-dominated sorting and crowding calculation.

[0099] The Pareto nondominated solution set can be the optimal solution set obtained from the candidate solution set through fast nondominated sorting and crowding calculation.

[0100] Specifically, the evaluation terminal can determine the dominance relationship of system leveling cost, grid net load fluctuation rate and ecological cumulative impact. It divides the solution set into different levels through rapid non-dominated sorting and selects a subset of non-dominated solutions that have no other solutions to dominate. Then, it calculates the crowding degree of each solution in the subset, quantifies the density of its distribution in the target space, and retains solutions with high crowding degree and uniform distribution. Finally, it integrates to generate the Pareto non-dominated solution set.

[0101] Step S4035: When the Pareto non-dominated solution set obtained by multiple consecutive iterations satisfies the preset convergence condition and the generalization error of the initial mathematical model is lower than the preset threshold, the iteration is stopped, and the model parameters corresponding to the final stable Pareto non-dominated solution set are determined as the parameters of the multi-objective collaborative optimization model, thus obtaining the multi-objective collaborative optimization model.

[0102] Specifically, the evaluation terminal can set iterative convergence criteria. In multiple iterations, the difference in objective function values ​​of the Pareto non-dominated solution set before and after iterations is calculated. If the difference is less than a preset convergence threshold, the convergence condition is satisfied. Simultaneously, cross-validation is used to calculate the model's generalization error, comparing the error value with the preset threshold. If both conditions are met, iteration immediately stops, and the core parameters, such as decision variable weights and constraint coefficients, corresponding to the final stable Pareto non-dominated solution set are extracted and solidified as the final parameters of the multi-objective collaborative optimization model, completing model construction.

[0103] In this embodiment, the evaluation terminal constructs a multi-objective collaborative optimization model that satisfies multiple objectives and multiple constraints through the entire process of data processing, model building, algorithm solving, solution set screening, and convergence verification, providing model support for the output of the optimal development layout scheme for wind and solar power plants.

[0104] Step S404: Based on the optimal development layout scheme, generate a comprehensive evaluation result; the comprehensive evaluation result includes at least one of the following: resource potential level distribution map, development suitability zoning map, radar chart of resource-load-ecology synergy indicators under multiple scenarios, and performance indicators of key sites.

[0105] Specifically, the optimal development layout scheme includes the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power plants; the above step S404 includes: Step S4041: Based on the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power stations, generate a resource potential level distribution map.

[0106] Among them, the resource potential level distribution map can be a visual assessment result generated based on the optimal development layout scheme, presenting the wind and solar resource development potential level of each location (grid unit) in the target area in the form of a spatial map.

[0107] Specifically, the assessment terminal can extract core information such as the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power stations in the optimal development layout scheme, and match them with the spatial grid system of the target area. Then, by combining the effective power density data in the spatiotemporal distribution matrix of wind and solar resources, it can quantitatively assess the theoretical development value and actual developable scale of each grid unit, and classify different resource potential levels such as high, medium, and low. Finally, using visualization mapping technology, with spatial map as the carrier, it can distinguish the regions of each level through differentiated color labels or legends, and generate a resource potential level distribution map.

[0108] Step S4042: Based on power grid structure data and ecological constraint data, a development suitability zoning map is generated through spatial overlay analysis.

[0109] Among them, the development of suitability zoning maps can be a visual assessment result generated by spatial overlay analysis based on power grid structure data and ecological constraint data.

[0110] Specifically, the assessment terminal performs targeted processing on power grid structure data and ecological constraint data: For power grid structure data, a suitability analysis is conducted, and cost levels are classified based on information such as transmission line distribution and access point capacity, generating a power grid access cost grading map; for ecological constraint data, an ecological sensitivity assessment is performed, and protection levels are determined based on ecological protection boundaries and biodiversity distribution. Subsequently, through spatial overlay analysis technology, the two types of zoning maps are merged and matched, comprehensively considering the convenience of power grid access and ecological protection requirements, dividing priority development areas, optimized development areas, and restricted development areas, ultimately generating a development suitability zoning map that intuitively presents the suitability of wind and solar power plant development in each region.

[0111] In an optional implementation, step S4042 includes: Step b1: Perform a suitability analysis on the power grid structure data and generate a grid access cost grading map.

[0112] Among them, the grid access cost grading map can be based on grid structure data and spatially visualize the relative costs required to connect to the existing grid system in different geographical locations within the target area in an intuitive grading format.

[0113] Specifically, the assessment terminal can comprehensively analyze the power grid structure data of the target area, extract core information such as the distribution of transmission lines, the location and capacity of substations, the main grid topology, and the accessibility of access points. Using spatial grid cells as the basic assessment unit, it calculates the estimated costs of line construction and equipment expansion for each cell to access the existing power grid. Based on the cost calculation results, it classifies the power grid access costs of each grid cell according to preset grading standards (such as high, medium, and low levels). Finally, it presents grid cells of different cost levels on the spatial map with differentiated colors or legends, generating a power grid access cost grading map.

[0114] Step b2: Based on the ecological constraint data, conduct an ecological sensitivity assessment to obtain an ecological protection level zoning map.

[0115] Among them, the ecological protection level zoning map can divide different geographical locations into different protection levels according to their ecological protection importance and sensitivity, and display them in spatial visualization.

[0116] Specifically, the assessment terminal extracts key information such as the scope of ecological protection red lines, core distribution areas of biodiversity, and boundaries of ecologically sensitive areas. Using spatial grid units as the basic evaluation unit, and combining core evaluation dimensions such as ecosystem service value, species protection priority, and ecological vulnerability, the ecological protection importance and sensitivity of each grid unit are comprehensively and quantitatively scored. Based on the scoring results, each spatial grid unit in the target area is divided into different protection levels such as core protected areas, general protected areas, and non-protected areas. Finally, grid units of different protection levels are presented on a spatial map with differentiated colors or legends to generate an ecological protection level zoning map.

[0117] Step b3 involves fusing the grid access cost grading map and the ecological protection level zoning map to generate a development suitability zoning map. The development suitability zoning map includes priority development areas, optimized development areas, and restricted development areas.

[0118] Specifically, the assessment terminal can spatially align the generated grid access cost classification map with the ecological protection level zoning map. Then, it will correlate and match the grid access cost level and ecological protection level of each spatial grid unit to comprehensively determine the development suitability of each grid: areas with low grid access costs and weak ecological constraints are classified as priority development areas, areas with moderate grid access costs or ecological constraints are classified as optimized development areas, and areas with high grid access costs or strong ecological constraints are classified as restricted development areas. Finally, different areas are clearly identified with differentiated colors or legends to generate a development suitability zoning map.

[0119] In this embodiment, the evaluation terminal performs spatial superposition and fusion analysis of grid access costs and ecological protection levels to divide priority, optimized and restricted development areas, providing a clear basis for development access and zoning control for the site selection and layout of wind and solar power plants.

[0120] Step S4043: Based on multi-scenario climate model data and optimal development layout scheme, generate a radar chart of resource-load-ecological synergy indicators under multiple scenarios.

[0121] Among them, the multi-scenario resource-load-ecology synergy index radar chart can be based on multi-scenario climate model data and optimal development layout schemes, and visually present the assessment results of the synergistic performance of different core indicators of resource development, power grid load adaptation and ecological protection under different climate scenarios in the form of radar chart.

[0122] Specifically, the assessment terminal first extracts key parameters for different future climate scenarios from multi-scenario climate model data. Combined with the installed capacity of power plants, energy storage configuration and spatiotemporal distribution information in the optimal development and layout scheme, it screens core collaborative indicators such as system levelized cost, grid net load volatility, and cumulative ecological impact. Then, it standardizes each indicator to eliminate dimensional differences, and uses multiple dimensions of the radar chart to correspond to different collaborative indicators, and plots indicator numerical curves according to scenarios. Finally, it generates a radar chart of resource-load-ecology collaborative indicators under multiple scenarios.

[0123] Step S4044: Quantitatively integrate the optimal development layout scheme and the spatiotemporal distribution matrix of wind and solar resources to generate a table of key site performance indicators.

[0124] Among them, the key site performance index table can be a quantitative integration of the optimal development layout scheme and the core information of the spatiotemporal distribution matrix of wind and solar resources, presenting the analysis results of key operation and benefit indicators of each wind farm and photovoltaic power station in tabular form.

[0125] Specifically, the evaluation terminal can extract basic information such as latitude and longitude coordinates, installed capacity, and energy storage configuration of each site from the optimal development layout scheme, and then associate it with the effective power density data of the corresponding site in the spatiotemporal distribution matrix of wind and solar resources to screen core performance indicators such as annual equivalent full load hours, power generation fluctuation coefficient, energy storage matching efficiency, and unit investment cost. Subsequently, the values ​​of each indicator are quantified, and after being classified and organized by site, the performance parameters of each site are clearly presented in tabular form to generate a key site performance indicator table.

[0126] Step S4045: Integrate the resource potential level distribution map, development suitability zoning map, radar chart of resource-load-ecology synergy indicators under multiple scenarios, and key site performance index table to obtain comprehensive evaluation results.

[0127] Specifically, the assessment terminal can standardize the format of the generated resource potential level distribution map, development suitability zoning map, multi-scenario resource-load-ecology synergy index radar chart, and key site performance index table to ensure that the data dimensions, spatial benchmarks, and time scales of various assessment results are consistent. Then, the spatial feature information in the distribution map, the synergy benefit data in the radar chart, and the quantitative parameters in the index table are cross-type correlated and integrated to generate an assessment system. Finally, the integrated results are systematically summarized and visualized to generate a comprehensive assessment result that fully covers resource potential, development suitability, synergy benefits, and site performance.

[0128] The spatiotemporal heterogeneity assessment method for wind and solar resources provided in this embodiment generates comprehensive and intuitive integrated assessment results by integrating multi-dimensional visualization results such as resource potential, development suitability, multi-scenario synergistic benefits, and site performance. This provides systematic support for the scientific planning and decision-making of wind and solar resources.

[0129] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0130] This embodiment also provides a system for assessing the spatiotemporal heterogeneity of wind and solar resources. This system is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0131] This embodiment provides a system for assessing the spatiotemporal heterogeneity of wind and solar resources, such as... Figure 5 As shown, it includes: The multi-source data acquisition module 501 is used to acquire multi-source constrained datasets of the target area.

[0132] The matrix generation module 502 is used to obtain the spatiotemporal distribution matrix of wind and solar resources based on a multi-source constrained dataset.

[0133] The collaborative optimization module 503 is used to obtain the optimal development layout scheme on the Pareto frontier based on the spatiotemporal distribution matrix of wind and solar resources and the preset multi-objective collaborative optimization model.

[0134] The evaluation result generation module 504 is used to generate comprehensive evaluation results based on the optimal development layout scheme. The comprehensive evaluation results include at least one of the following: resource potential level distribution map, development suitability zoning map, resource-load-ecology synergy index radar chart under multiple scenarios, and key site performance index table.

[0135] In some optional implementations, the multi-source constraint dataset includes multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data; the matrix generation module 502 includes: The element extraction unit is used to process multi-scenario climate model data based on multi-scenario climate model data through a dynamic downscaling and bias correction fusion algorithm to obtain wind and solar climate elements.

[0136] The sequence generation unit is used to obtain the theoretical power density distribution sequence of wind and solar resources based on wind, solar and climate elements and geographic information data.

[0137] The sequence correction unit is used to perform development suitability correction on the theoretical power density distribution sequence of wind and solar resources based on power grid structure data and ecological constraint data, and generate the effective power density distribution sequence of wind and solar resources.

[0138] The matrix construction unit is used to integrate and construct the spatiotemporal distribution matrix of wind and solar resources based on the effective power density distribution sequence of wind and solar resources.

[0139] In some optional implementations, the sequence correction unit includes: The coefficient calculation subunit is used to obtain the grid access cost coefficient of each spatial grid cell in the target area based on the grid structure data.

[0140] The constraint coefficient generation sub-unit is used to classify the ecological constraint data into ecological sensitivity levels and generate the ecological constraint coefficients of each spatial raster unit within the target area.

[0141] The coefficient solving sub-unit is used to obtain the joint correction coefficient based on the grid access cost coefficient and the ecological constraint coefficient.

[0142] The weighted calculation subunit is used to perform weighted calculations on the theoretical power density distribution sequence of wind and solar resources using joint correction coefficients, so as to obtain the effective power density distribution sequence of wind and solar resources.

[0143] In some alternative implementations, the joint correction factor is calculated using the following formula: ; in, It is a joint correction factor. It is the grid connection cost coefficient. It is the maximum grid connection cost. It is the ecological constraint coefficient. It is the highest level of ecological constraint. It is the attenuation factor.

[0144] In some alternative implementations, the optimal development layout includes the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power plants; In some optional implementations, the evaluation result generation module 504 includes: The distribution map generation unit is used to generate a resource potential level distribution map based on the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power plants.

[0145] The zoning map generation unit is used to generate development suitability zoning maps based on power grid structure data and ecological constraint data through spatial overlay analysis.

[0146] The radar chart generation unit is used to generate radar charts of resource-load-ecological synergy indicators under multiple scenarios based on multi-scenario climate model data and optimal development layout schemes.

[0147] The indicator table generation unit is used to quantitatively integrate the optimal development layout scheme and the spatiotemporal distribution matrix of wind and solar resources to generate a key site performance indicator table.

[0148] The results integration unit is used to integrate resource potential level distribution maps, development suitability zoning maps, resource-load-ecology synergy index radar charts under multiple scenarios, and key site performance index tables to obtain comprehensive evaluation results.

[0149] In some optional implementations, the partition map generation unit includes: The cost grading chart generation sub-unit is used to perform suitability analysis on power grid structure data and generate a power grid access cost grading chart.

[0150] The graded zoning map sub-unit is used to conduct ecological sensitivity assessment based on ecological constraint data, and generate an ecological protection graded zoning map.

[0151] The zoning map fusion generation sub-unit is used to merge the grid access cost classification map and the ecological protection level zoning map to generate a development suitability zoning map; the development suitability zoning map includes priority development areas, optimized development areas and restricted development areas.

[0152] In some optional implementations, the preset multi-objective collaborative optimization model is obtained in the following manner: Historical sample data of the multi-source constrained dataset within the target area is obtained. Based on the historical sample data, the spatiotemporal dynamic features of wind and solar resources are extracted. The spatiotemporal dynamic features of wind and solar resources are spatiotemporally aligned with the power grid load and ecological background data of the target area to obtain the training sample set. The ecological background data is spatial raster data extracted based on ecological constraint data. Based on the training sample set, an initial mathematical model is generated with the system levelized cost, power grid net load fluctuation rate and ecological cumulative impact as optimization objectives, and power grid transmission capacity and ecological protection scope as constraints. A non-dominated sorting genetic algorithm is used to iteratively solve the initial mathematical model to obtain a set of candidate solutions; Based on the candidate solution set, the Pareto non-dominated solution set is obtained by fast non-dominated sorting and crowding calculation; When the Pareto non-dominated solution set obtained by consecutive iterations satisfies the preset convergence condition and the generalization error of the initial mathematical model is lower than the preset threshold, the iteration stops, and the model parameters corresponding to the final stable Pareto non-dominated solution set are determined as the parameters of the multi-objective collaborative optimization model, thus obtaining the multi-objective collaborative optimization model.

[0153] The wind and solar resource spatiotemporal heterogeneity assessment system provided in this embodiment of the invention can execute the wind and solar resource spatiotemporal heterogeneity assessment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0154] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0155] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0156] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0157] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the spatiotemporal heterogeneity assessment method for wind and solar resources according to embodiments of the present invention.

[0158] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0159] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the spatiotemporal heterogeneity assessment method for wind and solar resources shown in the above embodiments is implemented.

[0160] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0161] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing the spatiotemporal heterogeneity of wind and solar resources, characterized in that, The method includes: Obtain the multi-source constraint dataset for the target region; Based on the multi-source constrained dataset, the spatiotemporal distribution matrix of wind and solar resources is obtained; Based on the spatiotemporal distribution matrix of wind and solar resources and the preset multi-objective collaborative optimization model, the optimal development layout scheme on the Pareto frontier is obtained. Based on the optimal development layout scheme, a comprehensive evaluation result is generated; the comprehensive evaluation result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecology synergy indicators under multiple scenarios, and a table of key site performance indicators.

2. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 1, characterized in that, The multi-source constraint dataset includes multi-scenario climate model data, geographic information data, power grid structure data, and ecological constraint data; The process of obtaining the spatiotemporal distribution matrix of wind and solar resources based on the multi-source constrained dataset includes: Based on the multi-scenario climate model data, the multi-scenario climate model data is processed by a dynamic downscaling and bias correction fusion algorithm to obtain wind and solar climate elements. Based on the aforementioned wind and solar climate elements and the aforementioned geographic information data, a theoretical power density distribution sequence of wind and solar resources is obtained; Based on the power grid structure data and the ecological constraint data, the development suitability correction is performed on the theoretical power density distribution sequence of wind and solar resources to generate the effective power density distribution sequence of wind and solar resources. Based on the effective power density distribution sequence of wind and solar resources, a spatiotemporal distribution matrix of wind and solar resources is constructed.

3. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 2, characterized in that, Based on the power grid structure data and the ecological constraint data, the theoretical power density distribution sequence of wind and solar resources is modified for development suitability to generate an effective power density distribution sequence of wind and solar resources, including: Based on the power grid structure data, the power grid access cost coefficient of each spatial grid unit in the target area is obtained; The ecological constraint data is classified into ecological sensitivity levels to generate ecological constraint coefficients for each spatial grid cell within the target area. Based on the power grid access cost coefficient and the ecological constraint coefficient, a joint correction coefficient is obtained; The effective power density distribution sequence of wind and solar resources is obtained by weighting the theoretical power density distribution sequence of wind and solar resources using the joint correction coefficient.

4. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 3, characterized in that, The joint correction coefficient is calculated using the following formula: ； in, It is a joint correction factor. It is the grid connection cost coefficient. It is the maximum grid connection cost. It is the ecological constraint coefficient. It is the highest level of ecological constraint. It is the attenuation factor.

5. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 2, characterized in that, The optimal development layout scheme includes the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of wind farms and photovoltaic power stations; Based on the optimal development layout scheme, a comprehensive evaluation result is generated, including: Based on the latitude and longitude coordinates, installed capacity, and energy storage capacity configuration of the wind farm and the photovoltaic power station, a resource potential level distribution map is generated; Based on the power grid structure data and the ecological constraint data, a development suitability zoning map is generated through spatial overlay analysis; Based on the multi-scenario climate model data and the optimal development layout scheme, a radar chart of resource-load-ecological synergy indicators under multiple scenarios is generated. The optimal development layout scheme and the spatiotemporal distribution matrix of wind and solar resources are quantitatively integrated to generate a table of key site performance indicators. By integrating the resource potential level distribution map, the development suitability zoning map, the resource-load-ecology synergy index radar chart under multiple scenarios, and the key site performance index table, a comprehensive evaluation result is obtained.

6. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 5, characterized in that, Based on the power grid structure data and the ecological constraint data, a development suitability zoning map is generated through spatial overlay analysis, including: A suitability analysis is performed on the power grid structure data to generate a grid access cost grading map; Based on the aforementioned ecological constraint data, an ecological sensitivity assessment is conducted to generate an ecological protection level zoning map. The power grid access cost classification map and the ecological protection level zoning map are merged to generate a development suitability zoning map; the development suitability zoning map includes priority development areas, optimized development areas and restricted development areas.

7. The method for assessing the spatiotemporal heterogeneity of wind and solar resources according to claim 2, characterized in that, The preset multi-objective collaborative optimization model is obtained through the following method: Historical sample data of a multi-source constrained dataset within the target area is obtained. Based on the historical sample data, spatiotemporal dynamic features of wind and solar resources are extracted. The spatiotemporal dynamic features of wind and solar resources are spatiotemporally aligned with the power grid load and ecological background data corresponding to the target area to obtain a training sample set. The ecological background data is spatial raster data extracted based on ecological constraint data. Based on the training sample set, an initial mathematical model is generated with system levelized cost, power grid net load fluctuation rate and ecological cumulative impact as optimization objectives, and power grid transmission capacity and ecological protection scope as constraints. The initial mathematical model is iteratively solved using a non-dominated sorting genetic algorithm to obtain a candidate solution set; Based on the candidate solution set, the Pareto non-dominated solution set is obtained by fast non-dominated sorting and crowding calculation; When the Pareto non-dominated solution set obtained by consecutive iterations satisfies the preset convergence condition and the generalization error of the initial mathematical model is lower than the preset threshold, the iteration stops, and the model parameters corresponding to the final stable Pareto non-dominated solution set are determined as the parameters of the multi-objective collaborative optimization model, thus obtaining the multi-objective collaborative optimization model.

8. A system for assessing the spatiotemporal heterogeneity of wind and solar resources, characterized in that, The system includes: The multi-source data acquisition module is used to acquire multi-source constrained datasets for the target area; The matrix generation module is used to obtain the spatiotemporal distribution matrix of wind and solar resources based on the multi-source constrained dataset; The collaborative optimization module is used to obtain the optimal development layout scheme on the Pareto frontier based on the spatiotemporal distribution matrix of wind and solar resources and a preset multi-objective collaborative optimization model. The evaluation result generation module is used to generate a comprehensive evaluation result based on the optimal development layout scheme; the comprehensive evaluation result includes at least one of the following: a resource potential level distribution map, a development suitability zoning map, a radar chart of resource-load-ecology synergy indicators under multiple scenarios, and a table of key site performance indicators.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for assessing the spatiotemporal heterogeneity of wind and solar resources as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for assessing the spatiotemporal heterogeneity of wind and solar resources as described in any one of claims 1 to 7.

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