Method and system for resource evaluation and site selection evaluation of large-scale wind and light base

Abstract

The invention discloses a method and a system for resource evaluation and site selection evaluation of a large-scale wind and light base, and belongs to the technical field of new energy power generation. The method comprises the following steps: acquiring meteorological and topographic data of a target large-scale wind and light base, integrating the meteorological and topographic data to obtain integrated data, and constructing a grading index and index scoring system based on the integrated data; weighting the grading indexes on the basis of an analytic hierarchy process and an entropy weight method, fusing the weights on the basis of a linear combination method to obtain a fusion weight, and dynamically adjusting the fusion weight of a preset index in the grading indexes to obtain a final weight; grading the grading indexes according to an index grading system, performing decentralized summing on scores according to the final weight, and performing resource evaluation and site selection grading on the target large-scale wind and light base through a decentralized summing result. The application of the method makes the evaluation result more fit the actual engineering constraint.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and more specifically, to a method and system for resource assessment and site selection evaluation of large-scale wind and solar power bases. Background Technology

[0002] (1) Overview of technological development:

[0003] The uneven spatial and temporal distribution and complex development conditions of wind and solar resources pose significant challenges to resource assessment and site selection evaluation. Traditional resource assessment methods often rely on single indicators (such as wind power density and solar irradiance) or simple weighted scoring, failing to comprehensively reflect the impact of multi-dimensional factors such as resource endowment, development conditions, and environmental constraints, resulting in insufficient scientific rigor and economic efficiency in site selection decisions. In recent years, some studies have attempted to introduce multi-criteria decision-making methods (such as the analytic hierarchy process and fuzzy comprehensive evaluation), but existing methods suffer from strong subjectivity in weight allocation and fail to consider regional development priority differences, thus hindering the large-scale and efficient development of large-scale sites.

[0004] (2) Related key technologies:

[0005] Wind and solar resource assessment technology: Currently, it mainly relies on meteorological station data, numerical simulation (such as WRF model) or remote sensing data to assess the potential of wind / solar energy resources, but lacks systematic integration of development constraints such as topography, land use, and ecologically sensitive areas.

[0006] Site selection evaluation methods: Commonly used methods include the Analytic Hierarchy Process (AHP), entropy weight method, and TOPSIS, the core of which lies in the allocation of indicator weights. In existing technologies, weights are mostly determined statically through expert experience or data statistics, which cannot adapt to the dynamic needs of different regions such as policy orientation and grid absorption capacity, resulting in a disconnect between evaluation results and actual development needs.

[0007] Hierarchical weighting technology: A few studies have attempted to stratify indicators (such as resource layer, environmental layer, economic layer), but have not solved the problem of weight transfer and dynamic adjustment between layers. In particular, there is a lack of hierarchical weighting mechanism for the "concentrated and contiguous" development characteristics of wind and solar bases, making it difficult to balance the relationship between global optimization and local priority.

[0008] (3) Deficiencies in existing technology:

[0009] Current methods have shortcomings in dynamic weight allocation of multi-dimensional indicators and hierarchical decision coupling, specifically:

[0010] ① The differentiated requirements of the "region-site" two-level weighting in the base development were not considered;

[0011] ② The lack of integration with dynamic constraints such as policy guidance and power grid planning results in low applicability of the evaluation results. These shortcomings urgently need to be addressed through hierarchical weighting and adaptive weight optimization methods for large-scale bases.

[0012] In recent years, research has attempted to improve assessment accuracy through methods such as GIS spatial analysis and life-cycle cost modeling, but bottlenecks remain. While Geographic Information Systems (GIS) can visualize and overlay topographic and ecological data, indicator weights still rely on manual pre-setting and cannot be dynamically adjusted based on slope exceeding limits or ecological protection requirements. Economic models are often independent of resource assessment, making it difficult to reflect the cascading impact of resource fluctuations on the entire life-cycle cost. Furthermore, existing technologies are inefficient in standardizing multi-source data, and the fusion analysis of meteorological station data, remote sensing imagery, and land planning data is time-consuming, hindering rapid site selection for large-scale projects. How to construct an assessment system that dynamically responds to environmental changes, balances multiple conflicting objectives, and possesses engineering practicality has become a core challenge restricting the efficient development of wind and solar resources. Summary of the Invention

[0013] To address the above problems, this invention proposes a method for resource assessment and site selection evaluation of large-scale wind and solar power bases, comprising:

[0014] Acquire meteorological and topographic data of the target large-scale scenic base, integrate the meteorological and topographic data, obtain integrated data, and construct a hierarchical index and index scoring system based on the integrated data;

[0015] Based on the analytic hierarchy process and the entropy weight method, the hierarchical indicators are weighted respectively, and the weights are merged based on the linear combination method to obtain the merged weights. The merged weights of the preset indicators in the hierarchical indicators are dynamically adjusted to obtain the final weights.

[0016] The graded indicators are scored according to the scoring system. Based on the final weights, the scores are weighted and summed. The results of the weighted sum are used to evaluate the resources and score the site selection of the target large-scale wind and light base.

[0017] Optional, tiered indicators include:

[0018] Primary indicators: resource potential, geographical constraints, and economic benefits;

[0019] Secondary indicators:

[0020] Secondary indicators under resource potential include: wind / solar resource density, availability, and stability;

[0021] Secondary indicators under geographical constraints include: altitude, slope, and land use type;

[0022] Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

[0023] Optionally, a weighted sum of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost can be used to assess the resources of a target large-scale wind and solar power base.

[0024] Optionally, the formulas for calculating the indicators of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows:

[0025] The formula for calculating wind resource density is as follows:

[0026]

[0027] The formula for calculating availability is as follows:

[0028]

[0029] The formula for calculating stability is as follows:

[0030]

[0031] The formula for calculating the levelized cost of electricity (LCOE) is as follows:

[0032]

[0033] The formula for calculating power transmission costs is as follows:

[0034]

[0035] Where ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I t O&M's initial investment (in yuan) in year t t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate. line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

[0036] Optionally, weights can be assigned to the hierarchical indicators based on the analytic hierarchy process, including:

[0037] Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

[0038] Optionally, based on the entropy weight method, weights are assigned to the hierarchical indicators, including:

[0039] Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

[0040] Optional, preset parameters include: slope and stability;

[0041] The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula:

[0042] w slope (S)=w slope ·[1-α·(1-f(S))]

[0043] The stability weights are dynamically adjusted according to the following formula:

[0044] w stability′ =min(w stability +0.2·(C v -0.25), 0.5)

[0045]

[0046] Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability ′ represents the adjusted weight, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

[0047] Furthermore, this invention also proposes a system for resource assessment and site selection evaluation of large-scale wind and solar power bases, comprising:

[0048] The indicator construction unit is used to acquire meteorological and topographic data of the target large-scale wind and light base, integrate the meteorological and topographic data, acquire integrated data, and construct hierarchical indicators and indicator scoring system based on the integrated data.

[0049] The weighting unit is used to assign weights to the hierarchical indicators based on the analytic hierarchy process and the entropy weight method, respectively, and to fuse the weights based on the linear combination method to obtain the fused weights. The fused weights of the preset indicators in the hierarchical indicators are dynamically adjusted to obtain the final weights.

[0050] The scoring unit is used to score the graded indicators according to the indicator scoring system, and to perform weighted summation of the scores according to the final weight. Based on the weighted summation result, the resources of the target large-scale scenic base are evaluated and the site selection is scored.

[0051] Optional, tiered indicators include:

[0052] Primary indicators: resource potential, geographical constraints, and economic benefits;

[0053] Secondary indicators:

[0054] Secondary indicators under resource potential include: wind / solar resource density, availability, and stability;

[0055] Secondary indicators under geographical constraints include: altitude, slope, and land use type;

[0056] Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

[0057] Optionally, a weighted sum of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost can be used to assess the resources of a target large-scale wind and solar power base.

[0058] Optionally, the formulas for calculating the indicators of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows:

[0059] The formula for calculating wind resource density is as follows:

[0060]

[0061] The formula for calculating availability is as follows:

[0062]

[0063] The formula for calculating stability is as follows:

[0064]

[0065] The formula for calculating the levelized cost of electricity (LCOE) is as follows:

[0066]

[0067] The formula for calculating power transmission costs is as follows:

[0068]

[0069] Where ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I t O&M's initial investment (in yuan) in year t t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate.line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

[0070] Optionally, weights can be assigned to the hierarchical indicators based on the analytic hierarchy process, including:

[0071] Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

[0072] Optionally, based on the entropy weight method, weights are assigned to the hierarchical indicators, including:

[0073] Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

[0074] Optional, preset parameters include: slope and stability;

[0075] The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula:

[0076] w slope (S)=w slope ·[1-α·(1-f(S))]

[0077] The stability weights are dynamically adjusted according to the following formula:

[0078]

[0079]

[0080] Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

[0081] In another aspect, the present invention also provides a computing device, comprising: one or more processors;

[0082] A processor is used to execute one or more programs;

[0083] When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0084] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0085] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0086] This invention provides a method for resource assessment and site selection evaluation of large-scale wind and solar power bases, comprising: acquiring meteorological and topographic data of the target large-scale wind and solar power base, integrating the meteorological and topographic data to obtain integrated data, constructing a hierarchical index and an index scoring system based on the integrated data; assigning weights to the hierarchical indexes based on the analytic hierarchy process (AHP) and the entropy weight method, and fusing the weights based on the linear combination method to obtain fused weights, and dynamically adjusting the fused weights of preset indicators in the hierarchical indexes to obtain the final weights; scoring the hierarchical indexes according to the index scoring system, and weighting and summing the scores according to the final weights; and using the weighted summation results to assess the resources and score the site selection of the target large-scale wind and solar power base. The application of this invention makes the assessment results more closely aligned with actual engineering constraints. Attached Figure Description

[0087] Figure 1 This is a flowchart of the method of the present invention;

[0088] Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0090] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0091] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0092] Example 1:

[0093] This invention proposes a method for resource assessment and site selection evaluation of large-scale wind and solar power bases, such as... Figure 1 As shown, it includes:

[0094] Step 1: Obtain meteorological and topographic data of the target large-scale scenic base, integrate the meteorological and topographic data, obtain integrated data, and construct a hierarchical index and index scoring system based on the integrated data;

[0095] Step 2: Based on the analytic hierarchy process (AHP) and the entropy weight method, weights are assigned to the hierarchical indicators respectively. Based on the linear combination method, the weights are merged to obtain the merged weights. The merged weights of the preset indicators in the hierarchical indicators are dynamically adjusted to obtain the final weights.

[0096] Step 3: Score the graded indicators according to the indicator scoring system, and sum the scores according to the final weights. Use the sum of the weights to evaluate the resources and site selection of the target large-scale wind and light base.

[0097] The grading indicators include:

[0098] Primary indicators: resource potential, geographical constraints, and economic benefits;

[0099] Secondary indicators:

[0100] Secondary indicators under resource potential include: wind / solar resource density, availability, and stability;

[0101] Secondary indicators under geographical constraints include: altitude, slope, and land use type;

[0102] Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

[0103] The weighted sum of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost is used to assess the resources of the target large-scale wind and solar power base.

[0104] The formulas for calculating the indicators of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows:

[0105] The formula for calculating wind resource density is as follows:

[0106]

[0107] The formula for calculating availability is as follows:

[0108]

[0109] The formula for calculating stability is as follows:

[0110]

[0111] The formula for calculating the levelized cost of electricity (LCOE) is as follows:

[0112]

[0113] The formula for calculating power transmission costs is as follows:

[0114]

[0115] Where ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I t O&M's initial investment (in yuan) in year t t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate. line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

[0116] Among them, the weighting of hierarchical indicators based on the analytic hierarchy process includes:

[0117] Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

[0118] Among them, the weighting of the hierarchical indicators is based on the entropy weighting method, including:

[0119] Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

[0120] Among them, the preset indicators include: slope and stability;

[0121] The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula:

[0122] w slope (S)=w slope ·[1-α·(1-f(S))]

[0123] The stability weights are dynamically adjusted according to the following formula:

[0124] w stability′ =min(w stability +0.2·(C v -0.25), 0.5)

[0125]

[0126] Among them, wslope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

[0127] The invention will be further illustrated below with specific examples:

[0128] This invention integrates the characteristics of the Analytic Hierarchy Process (AHP) and the Entropy Weight Method, and incorporates dynamic adjustments for slope and wind resource stability. It can be effectively applied to wind and solar energy resource assessment and site selection. The specific process is as follows: Figure 2 As shown, it includes:

[0129] First, meteorological data, topographic data, and other data are integrated to construct hierarchical indicators and scoring standards. Then, the weights of the first and second-level indicators are assigned sequentially using the Analytic Hierarchy Process (AHP) and the entropy weight method. Finally, the weights from the AHP and entropy weight methods are combined using a linear combination method.

[0130] Subsequently, a dynamic adjustment method for slope and wind resource density stability was introduced, and the final score was obtained by weighting and summing the scores according to the weights of the primary and secondary indicators.

[0131] The construction of a wind and solar resource-environment coupled hierarchical assessment model based on AHP-entropy weighting method includes:

[0132] First, in constructing the resource assessment and site selection evaluation system for wind and solar power bases, we established a scientific and systematic hierarchical indicator system by deeply analyzing the key elements of the entire energy development process. The core of this system lies in decomposing the complex decision-making process into strategic-level primary indicators and operational-level secondary indicators, forming a set of interconnected evaluation logics. The construction of the indicator system is shown in Table 1.

[0133] The primary indicators are: resource potential, geographical constraints, and economic benefits. The secondary indicators under resource potential are: wind / solar resource density, availability (quantified by setting thresholds), and stability (quantified by the coefficient of variation to measure resource fluctuations). The secondary indicators under geographical constraints are: altitude, slope, and land use type. The secondary indicators under economic benefits are: LCOE (Levelized Cost of Electricity) and transmission cost. A scoring system for these indicators, based on literature review and expert opinions, is constructed as shown in Table 2.

[0134] The indicators that need to be calculated for resource assessment are as follows:

[0135] Wind resource density is:

[0136]

[0137] Where ρ is air density and v is wind speed.

[0138] Availability:

[0139]

[0140] Stability: measured by the coefficient of variation C v To calculate:

[0141]

[0142] Where σ is the standard deviation of resource density and μ is the mean.

[0143] LCOE is calculated using the following formula:

[0144]

[0145] Among them, I t O&M's initial investment (in yuan) in year t t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss r is the annual decay rate, and r is the discount rate.

[0146] Transmission costs:

[0147]

[0148] Among them, C line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

[0149] This invention first utilizes the Analytic Hierarchy Process (AHP) to assign weights to primary and secondary indicators from a subjective perspective. AHP quantifies the relative importance of indicators by constructing a hierarchical judgment matrix, ultimately determining the weight of each indicator. Its core principles include three key steps: judgment matrix construction, weight calculation, and consistency check.

[0150] The first step is to perform pairwise comparisons of the indicators and establish an indicator judgment matrix. Let there be n indicators C1, C2, ..., Cn at a certain level. n Then the judgment matrix A is:

[0151]

[0152] Where a ij Indicator C i Compared to Cj The degree of importance.

[0153] The second step is weight calculation. AHP determines the weight of each indicator by calculating the eigenvectors of the judgment matrix. The specific steps are as follows:

[0154] (1) Calculate the weight vector:

[0155] Normalize each column of the judgment matrix A:

[0156]

[0157] The weight vector W is obtained by taking the row mean of the normalized matrix:

[0158]

[0159] w i Let be the weight of the i-th indicator.

[0160] (2) Calculate the largest eigenvalue λ max :

[0161]

[0162] Among them (AW) i This represents the i-th element after multiplying matrix A by the weight vector W.

[0163] The third step is a consistency check, which verifies the logical consistency of the judgment matrix. This is done using the consistency ratio (CR).

[0164] (1) Calculate the Consistency Index (CI):

[0165]

[0166] After querying the random consistency index RI in the relevant table of the query matrix, the consistency ratio CR was calculated:

[0167]

[0168] If CR < 0.1, the judgment matrix passes the consistency test and the weights are valid; if CR ≥ 0.1, the judgment matrix needs to be adjusted (e.g., expert scoring is re-performed).

[0169] Furthermore, this study utilizes the entropy weighting method to calculate the weights of the primary indicators and their subordinate secondary indicators. The entropy weighting method is an objective weighting method based on the degree of data dispersion, determining the weight of each indicator by calculating its information entropy. The smaller the entropy value, the greater the data variability of that indicator, and the more information it contains; therefore, it should be assigned a higher weight. The calculation steps are as follows:

[0170] 1. Data standardization:

[0171] Given m samples and n indicators, the original data matrix is ​​as follows:

[0172]

[0173] Standardize the indicators by dividing them into positive indicators (the larger the better) and negative indicators (the smaller the better).

[0174] (1) Positive indicators:

[0175]

[0176] (2) Negative indicators:

[0177]

[0178] The standardized matrix is ​​obtained as follows:

[0179]

[0180] 2. Calculate the weight of the indicators:

[0181] For the standardized data, calculate the weight of each indicator in each sample:

[0182]

[0183] Where p ij This represents the proportion of the i-th sample on the j-th indicator.

[0184] 3. Calculate information entropy:

[0185] Calculate the information entropy of each indicator:

[0186]

[0187] E j The smaller the value, the greater the data variability and the richer the information contained in the indicator; E j The larger the value, the more uniform the data for that indicator, and the less information it contains.

[0188] 4. Calculate the coefficient of difference:

[0189] d j =1-E j (18)

[0190] 5. Calculate the entropy weight:

[0191] The final entropy weight w of each objective j The calculation is as follows:

[0192]

[0193] Entropy weight w j The larger the value, the more important the indicator is in the overall evaluation.

[0194] Subsequently, this invention combines the values ​​of AHP and entropy weight method in a linear manner, resulting in a final weight W. A-E Represented as:

[0195] W A-E =αW AHP +(1-α)W EWM (20)

[0196] Among them W AHP For the subjective weights calculated by AHP, W EWM The objective weights are calculated using the entropy weight method, and α is the preference coefficient (usually taken as 0.5).

[0197] This combination of weights utilizes both the subjective judgment of AHP and the objective judgment of entropy weighting based on the data itself, thus ensuring a balance between subjective and objective factors.

[0198] Finally, this study obtains the final score based on the weights of each secondary indicator and the scoring (out of 100 points). The final score is obtained by weighting the indicators according to their respective weights.

[0199]

[0200] The location can be graded based on the total score: 0-20 points is extremely poor, 21-40 points is poor, 41-60 points is average, 61-80 points is good, and 81-100 points is excellent.

[0201] An improved AHP-entropy weighting method incorporating a dynamic adjustment mechanism includes:

[0202] Several influencing factors to consider during resource assessment: (1) If the land use of a certain area meets the qualification of an ecological protection zone, it is almost impossible to carry out wind power construction. (2) The power generation efficiency of wind and solar power stations is also seriously affected by the slope. Wind turbine stability: When the slope is >15°, the tower tilt angle exceeds 3°, requiring additional reinforcement (cost increases by 20-30%). Photovoltaic array: When the slope is >10°, the complexity of the support structure increases exponentially, and the installation efficiency decreases by 40%. (3) The spatiotemporal volatility of wind resources (when the wind speed variation coefficient is >25%) will cause the power generation prediction error to increase to more than 30%.

[0203] The ecological red line triggering mechanism is a technical implementation in the evaluation of wind and solar projects. Its core logic is: through GIS spatial analysis, the geometric relationship between the land parcel to be evaluated and the national / provincial ecological protection red line area is compared in real time. If the land parcel involves a protected area, the weight of its ecological land use type should be directly triggered as unavailable.

[0204] The impact of slope on scenic projects exhibits a non-linear relationship, and its effect function can be expressed as:

[0205]

[0206] Where S is the measured slope, S crit For the critical slope (15° for wind power, 10° for photovoltaic), β = 0.08 is the attenuation coefficient in the gentle slope area, and γ = 2.5 is the penalty coefficient in the steep slope area.

[0207] Adjust the slope based on the effect function:

[0208] w slope (S)=w slope ·[1-α·(1-f(S))] (23)

[0209] Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weight is α = 0.6, which is the maximum adjustment range.

[0210] Finally, ensure the sum of the weights is 1, and then compensate for the weights of other indicators in the geographical constraints synchronously:

[0211]

[0212] Where w j′ The adjusted weights for other indicators w j The weights of other indicators before adjustment.

[0213] Regarding wind resource stability, C v If the value is greater than 0.25, the weight will be adjusted. The adjustment formula for the wind resource stability weight is as follows:

[0214] w stability′ =min(w stability +0.2·(C v -0.25),0.5) (25)

[0215]

[0216] Where w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ w represents the weights of wind speed and availability after adjustment. j These are the weights for wind speed and availability before adjustment. This invention sets the weight to increase by 0.2 for every 0.1 increase in the variation index beyond a threshold, with an upper limit of 0.5.

[0217] Through such dynamic adjustments, while ensuring a fixed-weight grading index system for wind and solar resources, the weights can be adjusted according to the specific circumstances of each grid point. The weighted results are shown in Tables 3 and 4, making the grading results more scientific.

[0218] Compared with traditional static weighting models, the dynamic hierarchical weighting method proposed in this invention demonstrates stronger environmental adaptability in the assessment of complex terrain and ecologically sensitive areas. Through a slope threshold triggering mechanism, when the slope of a wind power area exceeds 15° or the slope of a photovoltaic area exceeds 10°, the model automatically increases the slope weight and compresses the weight of secondary indicators. Furthermore, when wind resources are highly volatile, it also triggers dynamic adjustments to the stability weight, making the assessment results more consistent with actual engineering constraints.

[0219] This invention, based on multi-source data fusion and dynamic weighting to generate a high-resolution wind and solar resource-environment coupled assessment map, significantly improves the spatial accuracy and decision-making reliability of resource assessment. By integrating meteorological station observation data, a 30-meter resolution digital elevation model (DEM), and ecological redline vector boundaries, the model, when generating development priority zones, not only reflects resource distribution trends but also simultaneously quantifies terrain development costs and ecological protection costs.

[0220] This invention quantifies the standards for large-scale wind and solar resource assessment and site selection by constructing a grading table and scoring criteria, which can improve the reference value for the implementation of actual projects.

[0221] Taking Guangdong Province as an example, this invention selects eight influencing factors for assessing the exploitable potential of onshore photovoltaic power in Guangdong Province. Through GIS spatial analysis, standardized hierarchical layers of each indicator are derived. Before conducting a comprehensive site selection suitability evaluation analysis, the previously constructed site selection suitability evaluation model is used to weight and sum the various hierarchical evaluation indicators. This involves multiplying the previously processed pixel data of multiple evaluation indicators by the weights obtained through game theory combination weighting, and then summing the weights to obtain a new pixel data set. This new pixel data set represents the comprehensive result of multiple evaluation indicators, as shown in Table 5. High values ​​are mainly concentrated in southern Guangdong Province, such as Zhanjiang, Jieyang, Shantou, and Shanwei. There are no particularly obvious low-value areas, and the distribution is relatively even across the rest of Guangdong Province. Furthermore, considering the results of the previous analysis, although solar radiation has the largest weight, its spatial distribution is not entirely consistent with the spatial distribution of the overlay analysis results. This indicates that the site selection of photovoltaic power plants in Guangdong Province is not determined by a single factor, but rather by the combined effect of multiple factors. According to the assessment results, the northern part of Guangdong Province has a low photovoltaic development suitability rating due to factors such as construction conditions. The areas suitable for photovoltaic development are mainly concentrated in the coastal areas of Guangdong Province, with a development area of ​​91,900 km2 that is moderately or above suitable for photovoltaic development.

[0222] Table 6 shows the feasibility assessment of onshore wind power development in Guangdong Province. It can be seen that areas unsuitable for onshore wind power development are mainly located in the Pearl River Delta region, Meizhou City, and some mountainous areas in the north of Guangdong Province. The Pearl River Delta region has dense residential areas, requiring a 3km buffer zone for wind power station site selection, thus its suitability for wind power development is low. Meizhou City also has low suitability due to its low average annual wind speed, while the northern mountainous areas present significant challenges due to their steep slopes. Areas deemed moderately suitable or above are mainly concentrated in Zhanjiang City and some coastal cities. These areas can accommodate a 3km buffer zone from residential areas while meeting other comprehensive indicator constraints. Table 6 shows that the developable area for onshore wind power at the moderately suitable or above level is 30,100 km². 2 .

[0223] Table 1

[0224]

[0225] Table 2

[0226]

[0227]

[0228]

[0229] Table 3

[0230]

[0231] Table 4. Weighted Details of Grid Point Light Resource Scores

[0232]

[0233] Table 5 Assessment Results of Onshore Photovoltaic Resources

[0234]

[0235] Table 6. Assessment Results of Onshore Wind Power Resources

[0236]

[0237] Example 2:

[0238] This invention also proposes a system 200 for resource assessment and site selection evaluation of large-scale wind and solar power bases, such as... Figure 2 As shown, it includes:

[0239] The indicator construction unit 201 is used to acquire meteorological and topographic data of the target large-scale scenic base, integrate the meteorological and topographic data, acquire integrated data, and construct hierarchical indicators and indicator scoring system based on the integrated data.

[0240] The weighting unit 202 is used to assign weights to the hierarchical indicators based on the analytic hierarchy process and the entropy weight method, respectively, and to fuse the weights based on the linear combination method to obtain the fused weights. It also dynamically adjusts the fused weights of the preset indicators in the hierarchical indicators to obtain the final weights.

[0241] The scoring unit 203 is used to score the graded indicators according to the indicator scoring system, and to perform weighted summation of the scores according to the final weight. Based on the weighted summation result, the resources of the target large-scale scenic base are evaluated and the site selection is scored.

[0242] The grading indicators include:

[0243] Primary indicators: resource potential, geographical constraints, and economic benefits;

[0244] Secondary indicators:

[0245] Secondary indicators under resource potential include: wind / solar resource density, availability, and stability;

[0246] Secondary indicators under geographical constraints include: altitude, slope, and land use type;

[0247] Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

[0248] The weighted sum of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost is used to assess the resources of the target large-scale wind and solar power base.

[0249] The formulas for calculating the indicators of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows:

[0250] The formula for calculating wind resource density is as follows:

[0251]

[0252] The formula for calculating availability is as follows:

[0253]

[0254] The formula for calculating stability is as follows:

[0255]

[0256] The formula for calculating the levelized cost of electricity (LCOE) is as follows:

[0257]

[0258] The formula for calculating power transmission costs is as follows:

[0259]

[0260] Where ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I t O&M's initial investment (in yuan) in year t t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate. line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

[0261] Among them, the weighting of hierarchical indicators based on the analytic hierarchy process includes:

[0262] Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

[0263] Among them, the weighting of the hierarchical indicators is based on the entropy weighting method, including:

[0264] Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

[0265] Optional, preset parameters include: slope and stability;

[0266] The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula:

[0267] w slope (S)=w slope ·[1-α·(1-f(S))]

[0268] The stability weights are dynamically adjusted according to the following formula:

[0269] w stability′ =min(w stability +0.2·(C v -0.25), 0.5)

[0270]

[0271] Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

[0272] The application of this invention makes the evaluation results more closely aligned with actual engineering constraints.

[0273] Example 3:

[0274] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0275] Example 4:

[0276] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0277] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0278] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0279] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0280] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0281] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0282] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for resource assessment and site selection evaluation of large-scale wind and solar power bases, characterized in that, include: Acquire meteorological and topographic data of the target large-scale scenic base, integrate the meteorological and topographic data, obtain integrated data, and construct a hierarchical index and index scoring system based on the integrated data; Based on the analytic hierarchy process and the entropy weight method, the hierarchical indicators are weighted respectively, and the weights are merged based on the linear combination method to obtain the merged weights. The merged weights of the preset indicators in the hierarchical indicators are dynamically adjusted to obtain the final weights. The graded indicators are scored according to the scoring system. Based on the final weights, the scores are weighted and summed. The results of the weighted sum are used to evaluate the resources and score the site selection of the target large-scale wind and light base.

2. The method according to claim 1, characterized in that, The grading indicators include: Primary indicators: resource potential, geographical constraints, and economic benefits; Secondary indicators: Secondary indicators under resource potential include: wind / solar resource density, availability, and stability; Secondary indicators under geographical constraints include: altitude, slope, and land use type; Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

3. The method according to claim 1, characterized in that, The weighted summation of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost is used to assess the resources of a target large-scale wind and solar power base.

4. The method according to claim 3, characterized in that, The formulas for calculating wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows: The formula for calculating wind resource density is as follows: The formula for calculating availability is as follows: The formula for calculating stability is as follows: The formula for calculating the levelized cost of electricity (LCOE) is as follows: The formula for calculating power transmission costs is as follows: Where ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I t For the initial investment (in yuan) in year t, O&M t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate. line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

5. The method according to claim 1, characterized in that, Weighting of hierarchical indicators based on the analytic hierarchy process includes: Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

6. The method according to claim 1, characterized in that, Based on the entropy weight method, weights are assigned to the hierarchical indicators, including: Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

7. The method according to claim 1, characterized in that, The preset indicators include: slope and stability; The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula: w slope (S)=w slope ·[1-α·(1-f(S))] The stability weights are dynamically adjusted according to the following formula: In stability′ =min(in stability +0.2 (C v -0.25),0.5) Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

8. A system for resource assessment and site selection evaluation of large-scale wind and solar power bases, characterized in that, include: The indicator construction unit is used to acquire meteorological and topographic data of the target large-scale wind and light base, integrate the meteorological and topographic data, acquire integrated data, and construct hierarchical indicators and indicator scoring system based on the integrated data. The weighting unit is used to assign weights to the hierarchical indicators based on the analytic hierarchy process and the entropy weight method, respectively, and to fuse the weights based on the linear combination method to obtain the fused weights. The fused weights of the preset indicators in the hierarchical indicators are dynamically adjusted to obtain the final weights. The scoring unit is used to score the graded indicators according to the indicator scoring system, and to perform weighted summation of the scores according to the final weight. Based on the weighted summation result, the resources of the target large-scale scenic base are evaluated and the site selection is scored.

9. The system according to claim 8, characterized in that, The grading indicators include: Primary indicators: resource potential, geographical constraints, and economic benefits; Secondary indicators: Secondary indicators under resource potential include: wind / solar resource density, availability, and stability; Secondary indicators under geographical constraints include: altitude, slope, and land use type; Secondary indicators under economic benefits include: levelized cost of electricity (LCOE) and transmission cost.

10. The system according to claim 8, characterized in that, The weighted summation of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost is used to assess the resources of a target large-scale wind and solar power base.

11. The system according to claim 10, characterized in that the calculation formulas for the indicators of wind resource density, availability, stability, levelized cost of electricity (LCOE), and transmission cost are as follows: The formula for calculating wind resource density is as follows: The formula for calculating availability is as follows: The formula for calculating stability is as follows: The formula for calculating the levelized cost of electricity (LCOE) is as follows: The formula for calculating power transmission costs is as follows: in, ρ is air density, v is wind speed, σ is the standard deviation of resource density, μ is the mean, and I is the mean. t For the initial investment (in yuan) in year t, O&M t For the maintenance cost in year t, F t For the fuel cost in year t, E t Let η be the power generation rate in year t. loss Let C be the annual decay rate, r be the discount rate, and C be the annual depreciation rate. line Where D is the cost per unit distance of the line, and C is the distance to the substation. sub Let n be the cost of expanding the substation, and n be the lifespan.

12. The system according to claim 8, characterized in that, Weighting of hierarchical indicators based on the analytic hierarchy process includes: Establish an indicator judgment matrix, calculate the indicator weights based on the indicator judgment matrix, and verify the consistency of the indicator weight calculation results.

13. The system according to claim 8, characterized in that, Based on the entropy weight method, weights are assigned to the hierarchical indicators, including: Meteorological and topographic data are standardized. Using the standardized data, the weight of indicators is calculated. Based on the weight of indicators, the information entropy is calculated. Based on the information entropy, the difference coefficient is calculated. The entropy weight is calculated through the difference coefficient.

14. The system according to claim 8, characterized in that, The preset indicators include: slope and stability; The weight of the slope is dynamically adjusted based on the effect function, as shown in the following formula: w slope (S)=w slope ·[1-α·(1-f(S))] The stability weights are dynamically adjusted according to the following formula: In stability′ =min(in stability +0.2 (C v -0.25),0.5) Among them, w slope (S) represents the adjusted slope weight, w slope The original slope weights are given, α = 0.6 represents the maximum adjustment range, and w stability′ For the adjusted weights, w stability The weights before adjustment, w j′ These are the weights adjusted for wind speed and availability, w j These are the weights for wind speed and availability before adjustment.

15. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-7 is implemented.

16. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-7.

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