Multi-energy complementary system site selection method based on dynamic weight and geographic information system cooperation

Abstract

The invention discloses a multi-energy complementary system site selection method based on dynamic weight and geographic information system (GIS) cooperation, and belongs to the technical field of renewable energy planning. According to the method, a technical framework of'multi-dimensional data acquisition-dynamic weight optimization-collaborative decision verification 'is constructed, so that the core problems of weight staticizing, space-time splitting, insufficient robustness and the like in a traditional site selection method are solved. According to the technical scheme, an analytic hierarchy process and a space-time dynamic correction mechanism are innovatively combined, and self-adaptive weight optimization of multi-dimensional criteria such as climate resources, power grid access and environmental constraints is achieved; through fusion of spatial clustering analysis and Monte Carlo simulation, a site selection verification system considering geographical proximity and system collaboration is established. According to the method, the dynamic response capability of site selection decision to policy change and technical evolution is remarkably improved, the scientificity and implementation reliability of a multi-energy complementary system planning scheme are ensured, and an intelligent decision support tool is provided for efficient deployment of renewable energy sources in a novel power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of renewable energy planning, and more particularly to a multi-energy complementary system site selection method based on dynamic weight and geographic information system (GIS) collaboration. BACKGROUND

[0002] With the acceleration of global energy transformation, as an important solution for efficient utilization of renewable energy, the scientific site selection of multi-energy complementary system has become increasingly prominent. Current site selection research mainly adopts static single-criterion decision-making methods, which have significant theoretical limitations: existing methods excessively focus on climate resource assessment, but systematically ignore key factors such as grid access conditions and infrastructure accessibility; the use of fixed weight systems cannot adapt to dynamic policy environment and technological evolution, resulting in the fragmentation of decision-making in time and space dimensions; and there is a lack of effective collaboration mechanisms to consider power transmission constraints and multi-energy flow coupling requirements. These defects directly lead to three practical difficulties: first, static suitability maps are disconnected from dynamic energy development needs; second, the lack of parameter sensitivity verification mechanisms affects decision-making reliability; and third, traditional methods can only passively select from pre-set candidate areas, and cannot achieve active spatial search and intelligent sorting. This situation seriously hinders the strategic deployment efficiency of multi-energy complementary systems in new power systems, and there is an urgent need to establish a site selection theoretical system that couples time-space dynamic optimization, multi-dimensional collaborative decision-making, and robustness verification, in order to support the sustainable development goal of large-scale development and utilization of renewable energy. SUMMARY

[0003] Therefore, the embodiments of the present application provide a multi-energy complementary system site selection method based on dynamic weight and GIS collaboration, which provides technical support for the development of multi-energy complementary systems.

[0004] To achieve the above-mentioned purpose, the technical solutions adopted by the present application are as follows:

[0005] S1, multi-dimensional data collection and preprocessing: by systematically collecting, integrating and standardizing various geographic data sets, a foundation database is established for multi-energy complementary system site selection. Data sources include multiple data sources such as publicly available meteorological data, resource data, government databases and remote sensing platforms, to ensure full coverage of evaluation standards. Data preprocessing ensures consistency of different measurement scales, time resolution and spatial formats, while maintaining data integrity and accuracy. Standardized data sets are the input basis for all subsequent evaluation processes;

[0006] S2, multi-criteria evaluation based on GIS: combining the weighted analytic hierarchy process with spatial analysis algorithms, a comprehensive suitability score is generated for each potential location. Expert consultation and the opinions of stakeholders provide auxiliary information for the weight determination process, ensuring that the evaluation criteria reflect technical requirements and actual development needs. Spatial interpolation and overlay analysis extend point evaluation to area, visualizing the development potential of the entire study area;

[0007] S3, Dynamic weight optimization based on time correction: By constructing a dynamic weighting mechanism, time adaptability is introduced into the traditional static site evaluation process to cope with changing development background, policy orientation and technology evolution. Among them, the time correction coefficient is calibrated using historical data and expert knowledge;

[0008] S4, Multi-criteria consensus ranking and robustness verification: The spatial quality evaluation, time sequence development focus and synergy consideration are included in the unified ranking framework to balance competitive objectives and stakeholder demands. Cluster algorithm identifies high-quality sites through geographical proximity to determine the optimal development area and achieve coordinated planning at single-site and regional scales. Robustness verification uses sensitivity analysis and Monte Carlo simulation to evaluate the stability of the ranking under parameter uncertainty, providing reliable decision-making basis for practical applications. The synergy verification process integrates power grid access analysis and system integration considerations to ensure that the selected site has both technical feasibility and economic rationality.

[0009] Optionally, S1 includes the following steps:

[0010] S11, Determine and systematically classify four categories of key spatial data sets, including: climate conditions, geographic location, environmental factors, and terrain characteristics;

[0011] S12, Establish a standardized data collection process to obtain meteorological data, infrastructure data, and administrative boundary data;

[0012] S13, Collect the following high-resolution data sets: digital elevation model, land use classification data, and transportation network data;

[0013] S14, Identify and handle missing values, outliers, and data inconsistencies, unify the coordinate systems of all data sets, adjust the spatial resolution to ensure consistency, and finally normalize the data to ensure comparability;

[0014] S15, Create a binary exclusion mask to identify environmentally restricted areas (such as ecologically sensitive areas) and protected areas (such as nature reserves).

[0015] Optionally, S2 includes the following steps:

[0016] S21, Build a comprehensive evaluation matrix covering technical feasibility, economic rationality, environmental compatibility, and social acceptability;

[0017] S22, Determine the initial weights through expert-stakeholder collaboration, then apply the analytic hierarchy process to calculate the quantitative weight values of each evaluation index, and finally perform weighted superposition operations on the multi-source spatial data layer based on the established weight system to calculate the basic suitability scores of all candidate sites in the study area.

[0018] S23, based on environmental protection requirements and policy and regulation restrictions, applying exclusion criteria to eliminate unsuitable areas;

[0019] S24, using spatial interpolation technology to generate continuous suitability distribution surface.

[0020] Optionally, the S3 includes the following steps:

[0021] S31, the system analyzes the historical data of multi-energy complementary projects, and identifies key time sequence factors affecting site selection decisions;

[0022] S32, based on the technology evolution track and the policy evolution trend, the sensitivity coefficient of each criterion is defined, and a time sequence weight adjustment model is constructed.

[0023] Optionally, the S4 includes the following steps:

[0024] S41, a multi-dimensional evaluation system coupling spatial distribution characteristics, time sequence development needs and synergistic benefits is constructed, and the standardization integration and priority ordering of heterogeneous evaluation dimensions are realized;

[0025] S42, a density clustering algorithm is applied to identify the optimal development area, and site grouping is carried out based on geographical proximity and quality similarity;

[0026] S43, global sensitivity analysis is carried out, and the disturbance degree of index weight change on the ordering result is evaluated;

[0027] S44, through Monte Carlo random simulation, the uncertainty propagation path in the evaluation process is tracked;

[0028] S45, the power grid infrastructure constraint is included in the final ordering, and the whole life cycle economic evaluation is carried out, and finally the uncertainty quantification site selection scheme with confidence interval is generated.

[0029] The beneficial effects of the present application are as follows:

[0030] The application significantly improves the accuracy and adaptability of site selection decision by introducing dynamic weight optimization and collaborative decision mechanism. Specifically, the technical scheme realizes three major innovation breakthroughs: (1) a systematic integration framework of spatial suitability assessment and time sequence development priority is established, which responds to time-varying factors such as policy changes, technology evolution and market environment in real time through dynamic weight adjustment algorithm; (2) the grid connection feasibility analysis is innovatively integrated into the multi-criteria decision system, and a collaborative optimization model is developed, which includes key parameters such as power transmission capacity constraint, network topology structure and grid connection cost; (3) a dual verification mechanism based on Monte Carlo simulation and global sensitivity analysis is constructed, which quantifies the disturbance degree of parameter uncertainty on the ranking results, ensuring the robustness of the decision scheme in the variable development environment. These technical features together constitute a dynamic site selection method system that adapts to the development needs of new power systems, providing a decision support tool with both technical feasibility and strategic foresight for the scientific deployment of multi-energy complementary systems. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the implementation of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description.

[0032] Figure 1 A multi-energy complementary system site selection method flowchart based on dynamic weight and GIS collaboration is provided for the embodiment of the present application.

[0033] Figure 2 A multi-dimensional data acquisition and preprocessing flowchart is provided for the embodiment of the present application.

[0034] Figure 3 A multi-criteria evaluation flowchart based on GIS is provided for the embodiment of the present application.

[0035] Figure 4 A dynamic weight optimization flowchart based on time correction is provided for the embodiment of the present application.

[0036] Figure 5 A multi-criteria consensus ranking and robustness verification flowchart is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the embodiment of the present application more clear, the technical scheme in the embodiment of the present application will be described clearly below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiments of the present application, not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0038] Embodiment 1

[0039] Referring to Figure 1 The application provides a multi-energy complementary system site selection method based on dynamic weight and GIS cooperation, comprising the following steps:

[0040] S1, multi-dimensional data acquisition and preprocessing: by systematically collecting, integrating and standardizing various geographic data sets, a foundation database is established for multi-energy complementary system site selection. The data sources include multiple data sources such as publicly available meteorological data, resource data, government databases and remote sensing platforms, to ensure full coverage of the evaluation standard. Data preprocessing ensures the consistency of different measurement scales, time resolution and spatial format, while maintaining the integrity and accuracy of the data. The standardized data set is the input basis for all subsequent evaluation processes;

[0041] S2, multi-criteria evaluation based on GIS: combining the weighted analytic hierarchy process with spatial analysis algorithms, a comprehensive suitability score is generated for each potential location. Expert consultation and the opinions of stakeholders provide auxiliary information for the weight determination process, ensuring that the evaluation standard reflects the technical requirements and actual development needs. Spatial interpolation and overlay analysis extend point evaluation to area, visualizing the development potential of the entire study area;

[0042] S3, dynamic weight optimization based on time correction: by constructing a dynamic weighting mechanism, time adaptability is introduced into the traditional static site evaluation process to respond to changing development backgrounds, policy orientations and technological evolution. The time correction coefficient is calibrated using historical data and expert knowledge;

[0043] S4, multi-criteria consensus ranking and robustness verification: spatial quality evaluation, time series development focus and collaborative consideration are included in a unified ranking framework to balance competitive objectives and stakeholder demands. Clustering algorithms identify high-quality sites through geographical proximity to determine the optimal development area, achieving coordinated planning at the single-site and regional scales. Robustness verification uses sensitivity analysis and Monte Carlo simulation methods to assess the stability of the ranking under parameter uncertainty, providing reliable decision-making basis for practical applications. The collaborative verification process integrates power grid access analysis and system integration considerations to ensure that the selected site has both technical feasibility and economic rationality.

[0044] Embodiment 2

[0045] Referring to Figure 2 Specifically, S1 includes the following specific steps:

[0046] S11, determine and systematically classify four categories of key spatial data sets, including: climate conditions, geographic location, environmental factors and topographic features;

[0047] S12, Establish a standardized data collection process to obtain meteorological data, infrastructure data, and administrative boundary data, respectively;

[0048] S13, Collect the following high-resolution data sets: digital elevation model, land use classification data, and transportation network data;

[0049] S14, Identify and handle missing values, outliers, and data inconsistencies, unify the coordinate systems of all data sets, adjust the spatial resolution to ensure consistency, and finally normalize the data to ensure comparability:

[0050]

[0051] wherein, represents the standardized evaluation value of site i with respect to criterion j; and are the minimum and maximum values of criterion j, respectively.

[0052] S15, Create a binary exclusion mask to identify environmentally restricted areas (such as ecologically sensitive areas) and protected areas (such as nature reserves):

[0053]

[0054] Example 3

[0055] Please refer to Figure 3 Specifically, the specific steps of S2 include:

[0056] S21, Construct a comprehensive evaluation matrix covering technical feasibility, economic rationality, environmental compatibility, and social acceptability, etc.

[0057] S22, Determine the initial weight through expert-stakeholder collaboration, then apply the analytic hierarchy process to calculate the quantitative weight values of each evaluation index, and finally perform weighted superposition operation on the multi-source spatial data layer based on the established weight system to calculate the basic suitability score of all candidate sites in the study area:

[0058]

[0059] wherein, ω j represents the weight coefficient of criterion j, and when site i is excluded, its comprehensive score S i will be assigned a value of zero.

[0060] S23, Based on environmental protection requirements and policy and regulatory restrictions, apply exclusion criteria to eliminate unsuitable areas;

[0061] S24, Convert the suitability evaluation of discrete points to a continuous spatial distribution surface through the Kriging spatial interpolation method:

[0062]

[0063] In the formula, Z(x0) represents the suitability prediction value of unknown spatial position x0; λ i is the Kriging interpolation weight coefficient, satisfying the normalization condition

[0064] Embodiment 4

[0065] Please refer to Figure 4 Specifically, the S3 specific steps include:

[0066] S31, the system analyzes multi-energy complementary project historical data, and identifies key time sequence factors affecting site selection decision;

[0067] S32, based on the technology evolution track and the policy evolution trend, the sensitivity coefficient of each criterion is defined, and a time sequence weight adjustment model is constructed:

[0068]

[0069] In the formula, is the dynamic weight of criterion j at time t; is the criterion reference weight determined based on the analytic hierarchy process; δ j is the criterion sensitivity coefficient; f(t) is a time sequence adjustment function, representing the influence of time-varying factors such as policy, technology and market.

[0070] The time sequence correction factor for a specific location adopts a double consideration mechanism including site quality and development time:

[0071]

[0072] In the formula, β is the quality correction coefficient; γ is the policy sensitivity coefficient; g(t, policy) captures the influence of policy change over time.

[0073] Embodiment 5

[0074] Please refer to Figure 4 Specifically, the S4 specific steps include:

[0075] S41, a multi-dimensional evaluation system coupling spatial distribution characteristics, time sequence development demand and synergistic benefits is constructed, realizing the standardized integration and priority ordering of heterogeneous evaluation dimensions:

[0076]

[0077] In the formula, represents the spatial quality ranking; Combined with time sequence priority adjustment; Reflects the advantages of power grid access and system integration.

[0078] S42, apply density clustering algorithm to identify optimal development areas, and group sites based on geographical proximity and quality similarity:

[0079] Cluster m = {i: d(i, centroid m ) ≤ τ and S i ≥ S threshold}

[0080] where d(i, centroid m ) denotes the distance to the centroid; τ is the clustering radius threshold; S threshold is the minimum admission threshold for the score.

[0081] S43, perform global sensitivity analysis to assess the degree of disturbance of ranking results caused by changes in index weights:

[0082] ΔR i = max |R i (ω k + ε) - R i (ω k )|

[0083] where ε represents the disturbance amplitude of the standard weight ω k , ensuring robust site selection under parameter uncertainty.

[0084] S44, track the uncertainty propagation path in the evaluation process through Monte Carlo random simulation;

[0085] S45, incorporate grid infrastructure constraints into the final ranking, while performing life cycle economic evaluation, and finally generate an uncertainty quantified site selection scheme with confidence intervals.

Claims

1. A multi-energy complementary system siting method based on dynamic weight and geographic information system (GIS) collaboration, characterized in that: Comprising the following steps: S1, multi-dimensional data collection and preprocessing: by systematically collecting, integrating and standardizing various types of geographic data sets, a foundation database is established for site selection of multi-capability complementary systems. Data sources include multiple data sources such as publicly available meteorological data, resource data, government databases and remote sensing platforms, to ensure full coverage of evaluation criteria. Data preprocessing ensures consistency of different measurement scales, time resolution and spatial format, while maintaining data integrity and accuracy. Standardized data sets are the input basis for all subsequent evaluation processes; S2, multi-criteria evaluation based on GIS: combining analytic hierarchy process weighting with spatial analysis algorithm, a comprehensive suitability score is generated for each potential location. Expert consultation and stakeholder opinions provide auxiliary information for the weight determination process, ensuring that the evaluation criteria reflect technical requirements and actual development needs. Spatial interpolation and overlay analysis extend point evaluation to area, visualizing the development potential of the entire study area; S3, dynamic weight optimization based on time correction: by constructing a dynamic weighting mechanism, time adaptability is introduced into the traditional static site evaluation process to respond to changing development backgrounds, policy orientations and technological evolution. The time correction coefficient is calibrated using historical data and expert knowledge; S4, multi-criteria consensus ranking and robustness verification: spatial quality evaluation, time series development focus and synergistic consideration are included in a unified ranking framework to balance competing objectives and stakeholder demands. Clustering algorithm identifies high-quality sites through geographical proximity to determine optimal development areas, achieving coordinated planning at single-site and regional scales. Robustness verification uses sensitivity analysis and Monte Carlo simulation methods to assess the stability of the ranking under parameter uncertainty, providing reliable decision-making basis for practical applications. The synergistic verification process integrates grid connection analysis and system integration considerations to ensure that the selected sites are both technically feasible and economically reasonable. 2.The multi-energy complementary system site selection method based on dynamic weight and GIS collaboration of claim 1, wherein: S1 specific steps include: S11, determine and systematically classify four major categories of key spatial data sets, including: climate conditions, geographic location, environmental factors and terrain characteristics; S12, establish a standardized data collection process to obtain meteorological data, infrastructure data and administrative boundary data; S13, collect the following high-resolution data sets: digital elevation model, land use classification data and transportation network data; S14, identify and process missing values, outliers and data inconsistencies, unify the coordinate systems of all data sets, adjust the spatial resolution to ensure consistency, and finally normalize the data to ensure comparability; S15, create a binary exclusion mask to identify environmentally restricted areas (such as ecologically sensitive areas) and protected areas (such as nature reserves). 3.The multi-energy complementary system site selection method based on dynamic weight and GIS collaboration of claim 1, wherein: S2 specific steps include: S21, build a comprehensive evaluation matrix covering technical feasibility, economic rationality, environmental compatibility and social acceptability, etc. S22, the preliminary weight is determined by expert-stakeholder collaboration, then the analytic hierarchy process is applied to calculate the quantitative weight value of each evaluation index, finally the multi-source spatial data layer is weighted and superimposed according to the established weight system, and the basic suitability score of all candidate sites in the study area is calculated; S23, based on environmental protection requirements and policy and regulation restrictions, the exclusion standard is applied to eliminate unsuitable areas; S24, a continuous suitability distribution surface is generated by using spatial interpolation technology.

4. The multi-energy complementary system site selection method based on dynamic weight and GIS collaboration of claim 1, characterized in that: S3 includes the following specific steps: S31, the system analyzes the historical data of multi-energy complementary projects to identify key time sequence factors affecting site selection decisions; S32, based on the technology evolution track and policy evolution trend, the sensitivity coefficient of each criterion is defined, and a time sequence weight adjustment model is constructed.

5. The multi-energy complementary system site selection method based on dynamic weight and GIS collaboration of claim 1, wherein: S4 includes the following specific steps: S41, a multi-dimensional evaluation system coupling spatial distribution characteristics, time sequence development needs and synergistic benefits is constructed to realize the standardized integration and priority ordering of heterogeneous evaluation dimensions; S42, the density clustering algorithm is applied to identify the optimal development area, and the site grouping is carried out based on geographical proximity and quality similarity; S43, global sensitivity analysis is carried out to evaluate the disturbance degree of index weight change on the ordering result; S44, the uncertainty propagation path in the evaluation process is tracked through Monte Carlo random simulation; S45, the power grid infrastructure constraint is included in the final ordering, and the full life cycle economic evaluation is carried out, and finally the uncertainty quantification site selection scheme with confidence interval is generated.

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