Small-scale site selection decision method for wind-wave hybrid power station on island

Abstract

The present invention provides a small-scale site selection decision method for an island wind-wave complementary power station, comprising the following steps: constructing an island wind-wave complementary power station site selection decision model, wherein the model evaluation index includes a primary index, and each primary index further includes a number of secondary indexes; solving the island wind-wave complementary power station site selection decision model based on the normalized results of the evaluation index; in the solution process, introducing the collective decision theory into the eigenvector method to calculate the weight of the primary index, and using the entropy method to calculate the weight of the secondary index; using the combined weight method to solve the combined weight of the primary index weight and the secondary index weight; and using a method combining the approach to the ideal point method with the grey correlation analysis to calculate the closeness, and ranking the alternatives based on the closeness; giving priority to the alternatives with high closeness to build the island wind-wave complementary power station. The present invention establishes a scientific and complete island wind-wave complementary power station site selection decision evaluation index system and a model solving algorithm, which can provide a direct basis for the site selection of the island wind-wave complementary power station.

Description

Technical Field

[0001] The present invention relates to the technical field of wave energy power generation, and in particular to a small-scale site selection decision method for an island wind-wave complementary power station. Background Art

[0002] In order to promote the economic development of islands and give full play to the strategic position of islands, it is necessary to solve the power supply problem of islands through ocean energy generation. The site selection issue needs to be considered for wind and wave complementary power generation on islands, which determines the efficiency of wind and wave complementary power generation.

[0003] At present, the research on site selection decision of wind-wave complementary power stations on islands has the following problems:

[0004] (1) The development of ocean energy on islands has problems such as low energy density, low power generation efficiency of single ocean energy power generation devices, and poor reliability. However, the complementarity of wind energy and wave energy has excellent reliability, safety, economy and environmental benefits. Therefore, wind and wave complementarity has become an important development direction for the development and utilization of ocean energy on islands. At present, there is still a lack of relevant research on the complementarity of wind and wave on islands, and there is a lack of a comprehensive and scientific decision-making model for the site selection of wind and wave complementary power stations on islands.

[0005] (2) The existing site selection of wind-wave complementary power stations or wind and wave single energy power stations mainly focuses on large-scale site selection, that is, suitability zoning based on geographic information systems or geographic information systems combined with multi-criteria decision-making. Suitability zoning divides the developable area into several levels and cannot directly serve the determination of the optimal site. It is not suitable for the site selection decision of wind-wave complementary power stations on islands.

[0006] (3) In terms of site selection decision: At present, the weight calculation of evaluation indexes for power station site selection mostly adopts the eigenvector method, which mainly relies on the subjective judgment of experts to determine the judgment matrix. It is difficult to avoid subjective bias, which affects the objectivity and robustness of the weight and the final calculation results; the scheme ranking algorithm mostly adopts a simple weighted method, because its calculation process is simple and can be directly solved by using geographic information system tools, but its mathematical model is not clear enough. Some studies have introduced the method of approaching the ideal point with clear mathematical meaning, but the classic method of approaching the ideal point only considers the distance between the best and worst ideal points, and ignores other dimensions. It still has certain limitations and needs to be further improved; the site selection decision is affected by many factors. In order to test the robustness of the calculation results and verify the feasibility and scientificity of the decision-making method, it is necessary to further conduct sensitivity analysis on the calculation results. Summary of the invention

[0007] The purpose of the present invention is to solve one of the above technical problems and provide a small-scale site selection decision method for an island wind-wave complementary power station.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] A small-scale site selection decision method for an island wind-wave complementary power station comprises the following steps:

[0010] Modeling steps: constructing a decision-making model for the site selection of an island wind and wave energy complementary power station, the model including a set of evaluation indicators, the evaluation indicators including primary indicators: resource condition indicators, natural condition indicators, economic factor indicators, and social factor indicators; each primary indicator further includes a number of secondary indicators;

[0011] Site selection solution steps: Based on the standardized results of the evaluation indicators, the site selection decision model of the island wind and wave energy complementary power station is solved; in the solution process, the collective decision-making theory is introduced into the characteristic vector method to calculate the weight of the first-level indicator, and the entropy method is used to calculate the weight of the second-level indicator; the combined weight method is used to solve the combined weight of the first-level indicator weight and the second-level indicator weight; and the closeness is calculated by combining the method of approaching the ideal point method with the gray correlation analysis, and the site selection of the alternative options is sorted based on the closeness;

[0012] Priority will be given to alternative options with high proximity to build wind and wave complementary power stations on islands.

[0013] In some embodiments of the present invention, the method for calculating the weight of the primary index includes:

[0014] S211: Construct a judgment matrix:

[0015] B=(b ij ) n×n

[0016] Where: b ij b is the first-level evaluation index i For the first-level evaluation index b j the relative importance of

[0017] S212: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector:

[0018] Bw=λ max

[0019] Among them, λ max is the maximum eigenvalue of the judgment matrix B, w is the eigenvector corresponding to the maximum eigenvalue, and after normalization, it corresponds to the weight of each indicator;

[0020] S213: Assume that there are L experts, forming an expert set E = {E1, E2, ..., E L}, the weight of each expert is {e1,e2,…,e l}; For each expert E L , the evaluation index weights solved are {w1,w2,...,w l}; Calculate the weight of evaluation indicators after collective decision-making:

[0021]

[0022] Where: w il represents the weight of the i-th indicator obtained by the l-th expert, e l represents the weight of the lth expert, Indicates w il E l Power.

[0023] In some embodiments of the present invention, in the process of calculating the weight of the primary index, after calculating the maximum eigenvalue of the judgment matrix, the judgment matrix is ​​further subjected to a consistency check;

[0024]

[0025]

[0026] Among them, CR is the consistency ratio, CI is the consistency index, and RI is the random consistency index, which can be given; judge whether CR is less than 0.1. If so, the judgment matrix meets the consistency requirements. If not, the judgment matrix does not meet the consistency requirements and needs to be reconstructed.

[0027] In some embodiments of the present invention, the method for calculating the weight of the secondary index includes:

[0028] S221: Calculate the weight of the jth indicator in the i-th solution;

[0029]

[0030] Where: p ij is the weight of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the i-th scheme, n is the number of indicators, and m is the number of schemes;

[0031] S222: Calculate the entropy value of the jth evaluation index:

[0032]

[0033]

[0034] Among them, E j is the entropy value of the jth evaluation index, K is the coefficient;

[0035] S223: Calculate the weight of the jth evaluation index:

[0036]

[0037] Among them, w j is the weight of the jth evaluation index.

[0038] In some embodiments of the present invention, the method for calculating the combination weight includes:

[0039] w′ cij =w ci × cij

[0040] Among them, w′ cij is the combined weight of the indicators, w ci is the first-level indicator weight, w cij It is the weight of the secondary indicator.

[0041] In some embodiments of the present invention, the method for calculating the closeness and ranking the solutions includes:

[0042] S31: Calculate the weighted normalized decision matrix:

[0043]

[0044]

[0045] Where: v ij is the weighted normalized index value of the jth index in the i-th scheme, is the normalized attribute value of the jth indicator in the i-th scheme, n is the number of indicators, and m is the number of schemes;

[0046] S32: Determine the positive ideal solution (A + ) and the negative ideal solution (A - )

[0047]

[0048]

[0049] Among them: J is the benefit indicator set, J′ is the cost indicator set, is the optimal value of all weighted normalized indicators of the nth solution, is the worst value of all weighted normalized indicators of the nth solution;

[0050] S33: Calculate the Euclidean distance of each solution to the positive ideal solution and the negative ideal solution and

[0051]

[0052]

[0053] in: is the optimal value of the jth indicator after weighted normalization, is the worst value of the jth indicator after weighted normalization, m is the number of solutions,

[0054] S34: Calculate the grey correlation coefficient between the i-th solution and the positive ideal solution relative to the j-th index:

[0055]

[0056]

[0057] Among them, ρ is the resolution coefficient, R + is the grey correlation coefficient matrix of the positive ideal solution;

[0058] Calculate the grey correlation coefficient between the i-th solution and the negative ideal solution relative to the j-th index:

[0059]

[0060] S35: Calculate the grey correlation degree of the positive ideal solution:

[0061]

[0062] Calculate the grey relational degree of the negative ideal solution:

[0063]

[0064] S36: Calculate the closeness of the solution:

[0065] Normalize the Euclidean distance and grey correlation:

[0066]

[0067]

[0068] in, is the normalized value of the Euclidean distance to the positive ideal solution; is the normalized value of the Euclidean distance to the negative ideal solution; is the normalized value of the grey relational degree of the positive ideal solution; is the normalized value of the grey relational degree of the negative ideal solution; the normalized Euclidean distance and grey relational degree are combined:

[0069] Combine the normalized post-processed Euclidean distance and grey relational degree:

[0070]

[0071]

[0072] Among them: α is the position weight in the similarity calculation between the alternative solution and the ideal solution and β is the shape weight in the similarity calculation between the alternative solution and the ideal solution; is the value of the positive ideal solution after combining Euclidean distance and grey relational degree; is the value of the negative ideal solution after combining Euclidean distance and grey relational degree;

[0073] S37: Calculate the closeness:

[0074]

[0075] Z i Sort by, preferably Z i The larger solution is preferred.

[0076] In some embodiments of the present invention, the normalization processing method includes the following steps:

[0077] The evaluation indicators are divided into benefit indicators and cost indicators, and the indicators are vector-normalized;

[0078] For benefit indicators:

[0079]

[0080] For cost-based indicators:

[0081]

[0082] In the formula, x ij is the attribute value of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the i-th scheme;

[0083] The standardized indicators are used to solve the site selection problem.

[0084] In some embodiments of the present invention, the secondary indicators of the resource condition indicators include:

[0085] Annual average wave power density: used to measure the potential development capacity of wave energy;

[0086] Annual average significant wave height: used to measure the potential development capacity of wave energy;

[0087] Annual effective wave hours: used to measure the operational time of island wind and wave energy generation devices;

[0088] Annual average wind speed: used to measure the potential development capacity of island wind energy;

[0089] Annual average wind power density: used to measure the potential development capacity of island wind energy;

[0090] Annual effective wind hours: used to measure the operational time of island wind and wave energy generation devices;

[0091] The annual average wave power density is:

[0092]

[0093] Among them, P wave is the wave power density, ρ is the seawater density, H S is the effective wave height, g is the gravitational acceleration, T E For the energy cycle;

[0094] The annual average wind power density is:

[0095]

[0096] Among them, P wind is the wind power density; ρ is the air density; n is the number of wind speed records within the set period; v i is the wind speed recorded for the ith time.

[0097] In some embodiments of the present invention, a model verification step is further included:

[0098] Set all first-level indicator weights to be equal, and retain the second-level weights obtained by the original calculation;

[0099] or,

[0100] Keep the first-level weight index obtained by the original calculation, and set all the second-level weight indexes to be equal;

[0101] or,

[0102] Set all first-level indicator weights and second-level indicator weights to be equal;

[0103] The site development sequence of island wind and wave energy complementary power stations is recalculated and compared with the original calculated site development sequence of island wind and wave energy complementary power stations.

[0104] In some embodiments of the present invention, the secondary indicators of the natural condition indicators include water depth, seabed geology, coastal erosion and geological disasters;

[0105] Secondary indicators of economic factors include total cost and number of people served;

[0106] The secondary indicators of social factors include traffic conditions, policy incentives and the degree of matching between electricity demand and supply.

[0107] The small-scale site selection decision method for an island wind-wave complementary power station provided by the present invention has the following beneficial effects:

[0108] (1) A small-scale site selection decision model for wind-wave complementary power stations suitable for islands was constructed, and an evaluation index set for the site selection decision model for wind-wave complementary power stations on islands was established. Factors such as resources, nature, economy, and society were considered, and a scientific and complete site selection decision evaluation index system for wind-wave complementary power stations on islands was established. This can solve the problem that the research on site selection evaluation indicators for wind-wave complementary power stations on islands is just beginning and is not yet perfect, and provide a direct basis for the site selection of wind-wave complementary power stations on islands.

[0109] (2) An algorithm system for solving the small-scale site selection decision model for island wind-wave complementary power stations was established, including a numerical index weighting algorithm and a scheme evaluation algorithm. This method system avoids the subjective bias of expert judgment and makes the calculation results more objective. It combines the ideal point method with the grey correlation analysis method and introduces it into the site selection field of island wind-wave complementary power stations, overcoming the shortcoming that the ideal point method cannot reflect the dynamic changes of the data series, and providing theoretical support for the site selection decision of island wind-wave complementary power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0111] Figure 1 This is a flow chart of the small-scale site selection decision-making method for island wind-wave complementary power stations. DETAILED DESCRIPTION

[0112] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0113] A small-scale site selection decision method for an island wind-wave complementary power station comprises the following steps.

[0114] S1: Modeling step: construct an island wind and wave energy complementary power station site selection decision model for each alternative plan, the model includes an evaluation index set, the evaluation index includes first-level indicators: resource condition index, natural condition index, economic factor index, social factor index; each first-level indicator further includes a number of second-level indicators.

[0115] (1) Resource condition indicators

[0116] Resource condition index is an important indicator reflecting the richness and exploitability of resources.

[0117] The resources required for island wind and wave power generation are mainly wind energy and wave energy. Therefore, the present invention establishes a secondary resource condition indicator set based on the wind energy and wave energy resource assessment method, which specifically includes:

[0118] Annual average wave power density: used to measure the potential development capacity of wave energy;

[0119] Annual average significant wave height: used to measure the potential development capacity of wave energy;

[0120] Annual effective wave hours: used to measure the operational time of island wind and wave energy generation devices;

[0121] Annual average wind speed: used to measure the potential development capacity of island wind energy;

[0122] Annual average wind power density: used to measure the potential development capacity of island wind energy;

[0123] Annual effective wind hours: used to measure the operational time of island wind and wave energy generation devices;

[0124] The annual average wave power density is:

[0125]

[0126] Where P wave is the wave power density (kW / m); ρ is the seawater density, which is 1025 kg / m 3 ;H S is the effective wave height (m); g is the acceleration due to gravity, which is 9.798m / s 2 ; T E is the energy cycle (s).

[0127] The annual average wind power density is:

[0128]

[0129] Where P wind is the wind power density (W / m 2 );ρ is air density (kg / m 3 ); n is the number of records in the set time period; v i is the wind speed recorded for the ith time.

[0130] The data used in the above calculation process: the original data of power density and wave height are obtained through the SWAN calculation model, and the input of the model is the actual water depth, wind field and other conditions; the wind field data required for the calculation of annual average wind power density and annual average wind speed are derived from the European Centre for Medium-Range Weather Forecasts (ECMWF).

[0131] (2) Natural conditions indicators

[0132] The quality of natural conditions will have a direct impact on the construction difficulty, investment benefits, and operational risks of island wind and wave complementary power stations.

[0133] The secondary indicators of natural condition indicators include water depth, seabed geology, coastal erosion and geological disasters. Water depth will limit the type and scale of wind and wave power generation devices to a certain extent, and will also affect the cost and installation process of submarine cable layout. Seabed geology affects the way of installing power generation devices and submarine cables. Installing wind and wave power generation equipment in areas with severe coastal erosion will increase the difficulty of construction and reduce the stability of power generation. Geological disasters refer to the frequency of geological disasters around islands. It is not advisable to build wind and wave complementary power stations in areas where geological disasters frequently occur.

[0134] (3) Economic factor indicators

[0135] Economic factor indicators affect the construction and operation costs of wind-wave complementary power stations on islands. The secondary indicators established include total cost and the number of people served. The total cost is intended to measure the capital investment in the construction of independent wind-wave complementary power stations on islands, including the construction, operation and maintenance costs of power stations. The number of people served refers to the number of island populations served by wind-wave complementary power plants. It reflects the number of potential energy consumers. The more island populations are served, the greater the demand for power stations on the island.

[0136] (4) Social Factors Indicators

[0137] The secondary indicators of social factors include traffic conditions, policy incentives, and the degree of matching between power demand and supply. Traffic conditions refer to shipping conditions and traffic conditions on the island, which affect the installation and operation and maintenance of power stations. Policy incentives reflect the degree of support for islands by local relevant policies. The degree of matching between power demand and supply can be determined based on the local population, current power conditions, and infrastructure construction, reflecting the urgency of building power stations.

[0138] Furthermore, considering that the evaluation indicators of the small-scale site selection decision model for island wind-wave complementary power stations have problems such as mixed qualitative and quantitative, inconsistent dimensions, and inconsistent indicator value orientation, directly using indicators for calculations is likely to cause inaccurate results.

[0139] To solve the above problems, in some embodiments of the present invention, the index system needs to be standardized before the schemes are sorted. According to the different orientations of the evaluation indicators, they can be divided into benefit indicators and cost indicators. For example, the benefit indicators selected by the present invention include: annual average wave power density, annual average effective wave height, annual effective wave hours, annual average wind speed, annual average wind power density, annual effective wind hours, seabed geology, service population, traffic conditions, and policy incentives; cost indicators include: water depth, coastal erosion, geological disasters, total cost, and power demand and supply matching. The 1-9 scoring method is used to quantify and dimensionlessly convert qualitative indicators. The vector normalization method has been proven to be the most suitable normalization method close to the ideal point method. Therefore, the vector normalization method is used to achieve the normalization of the index system, as shown in formulas (3) and (4).

[0140] For benefit indicators:

[0141]

[0142] For cost indicators:

[0143]

[0144] In the above two formulas, x ij is the attribute value of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the ith scheme, and n is the total number of indicators.

[0145] S2: Site selection solution steps: Based on the normalized results of the evaluation indicators, the site selection decision model of the island wind-wave complementary power station is solved; in the solution process, the collective decision-making theory is introduced into the eigenvector method to calculate the weight of the first-level indicator, and the entropy method is used to calculate the weight of the second-level indicator; the combined weight method is used to solve the combined weight of the first-level indicator weight and the second-level indicator weight; and based on the combined weight, the closeness is calculated by combining the method of approaching the ideal point method with the grey correlation analysis, and the alternative options are ranked based on the closeness.

[0146] Priority will be given to alternative options with high proximity to build wind and wave complementary power stations on islands.

[0147] The decision-making process of the wind-wave hybrid power station includes weight calculation. In the prior art, weight solution methods usually include subjective weighting method and objective weighting method. The most commonly used weighting method is the eigenvector method. The eigenvector method forms a judgment matrix based on the subjective judgment of experts on the importance of the evaluation index. The weight of the evaluation index is obtained by solving the eigenvalue and eigenvector of the judgment matrix. The calculation result is dominated by the subjective judgment of the experts and often has subjective bias.

[0148] In order to reduce the subjective bias of the eigenvector method, a combined weighting algorithm is established based on the eigenvector method, entropy method and collective decision-making theory. The calculation rule is to introduce collective decision-making theory into the eigenvector method to calculate the weight of the first-level indicators, and use the entropy method to calculate the weight of the second-level indicators. Compared with the single eigenvector method, this method can gather the preferences of different experts to form a collective preference, avoid the subjective bias of a single expert's judgment, and make the calculation results more objective. In addition, while considering the subjective information of experts, it can make full use of the objective data of the indicator attribute value, and correct the subjective judgment according to the entropy of the data, thereby further improving the accuracy and robustness of the calculation results.

[0149] In some embodiments of the present invention, the method for calculating the weight of the first-level index is the eigenvector method, which was proposed by Saaty and is the most commonly used method for solving the weight of evaluation indexes in the field of renewable energy site selection. The process of calculating the weight of the first-level evaluation index by the eigenvector method is as follows:

[0150] S211: Construct a judgment matrix:

[0151] Assuming there are n evaluation indicators, experts use Saaty's 1-9 scale method to compare each evaluation indicator pairwise to determine its relative importance, thereby constructing a judgment matrix:

[0152] B=(b ij ) n×n

[0153] Where: b ij b is the first-level evaluation index i For the first-level evaluation index b j the relative importance of

[0154] S212: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector:

[0155] Bw=λ max

[0156] Among them, λ max is the maximum eigenvalue of the judgment matrix B, w is the eigenvector corresponding to the maximum eigenvalue, and after normalization, it corresponds to the weight of each indicator;

[0157] S213: In order to make the evaluation results more objective, the collective decision-making theory is introduced in the calculation of the evaluation index weights to reduce subjective bias. Suppose there are L experts, and the expert set E = {E1, E2, ..., E L}, the weight of each expert is {e1,e2,…,e l}; For each expert E L , the evaluation index weights solved are {w1,w2,...,w l}; Calculate the weight of evaluation indicators after collective decision-making:

[0158]

[0159] Where: w il represents the weight of the ith indicator obtained by the lth expert, e l represents the weight of the lth expert, Indicates w il E l Power.

[0160] In some embodiments of the present invention, in the process of calculating the weight of the primary index, after calculating the maximum eigenvalue of the judgment matrix, the judgment matrix is ​​further subjected to a consistency check;

[0161]

[0162]

[0163] Among them, CR is the consistency ratio, CI is the consistency index, and RI is the random consistency index, which can be given; judge whether CR is less than 0.1. If so, the judgment matrix meets the consistency requirements. If not, the judgment matrix does not meet the consistency requirements and needs to be reconstructed.

[0164] RI is determined from Table 1 according to the value of n.

[0165] Table 1 Random index RI of n-order matrix

[0166]

[0167] In some embodiments of the present invention, the entropy method is used to calculate the weight of the secondary indicator. The entropy method calculates the indicator weight based on the size of the sample data observation value and the degree of difference between them. The larger the entropy, the smaller the impact of the evaluation indicator on the decision, that is, the smaller the weight of the indicator. Conversely, the smaller the entropy, the greater the impact of the evaluation indicator on the decision, and the greater the weight of the indicator. The process of calculating the weight of the secondary evaluation indicator using the entropy method is as follows:

[0168] S221: Calculate the weight of the jth indicator in the i-th solution;

[0169]

[0170] Where: p ij is the weight of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the i-th scheme;

[0171] S222: Calculate the entropy value of the jth evaluation index:

[0172]

[0173]

[0174] Among them, E j is the entropy value of the jth evaluation index, K is the coefficient;

[0175] S223: Calculate the weight of the jth evaluation index:

[0176]

[0177] Among them, w j ——The weight of the jth evaluation indicator.

[0178] In some embodiments of the present invention, a combined weight method is proposed for the site selection decision model of the island wind-wave complementary power station. The calculation rule is to solve the weights of the primary index and the secondary index respectively, where the primary index weight is calculated by the characteristic vector method and the collective decision method, and the secondary index weight is calculated by the entropy method. Finally, the combined weight is solved by the following formula

[0179] w′ cij =w ci × cij

[0180] Among them, w′ cij is the combined weight of the indicators, w ci is the first-level indicator weight, w cij It is the weight of the secondary indicator.

[0181] The approach to the ideal point method was first proposed by Hwang and Yoon and is currently commonly used to solve ranking problems. The basic idea of ​​the approach to the ideal point method is that the decision closest to the ideal point and farthest from the negative ideal point is the best decision. The approach to the ideal point method introduces two reference points and ranks the alternatives by comparing the distances between the alternatives and these reference points. Although the approach to the ideal point method has been widely used in many fields, it also has some shortcomings. It can reflect the positional relationship between data curves, but cannot reflect the dynamic changes of data sequences. The grey correlation analysis method was first proposed by Deng and is suitable for decision-making in multi-attribute situations. The limitations of the approach to the ideal point method can be overcome by the grey correlation coefficient of the grey correlation analysis model. The approach to the ideal point method is combined with the grey correlation analysis method to measure the relationship between the options according to the degree of similarity or difference in the position and shape of the options.

[0182] Based on the above theory, in some embodiments of the present invention, the method for calculating the closeness and ranking the solutions includes:

[0183] S31: Calculate the weighted normalized decision matrix:

[0184]

[0185]

[0186] Where: v ij is the weighted normalized index value of the jth index in the i-th scheme, is the normalized attribute value of the jth indicator in the i-th scheme, n is the number of indicators, and m is the number of schemes;

[0187] S32: Determine the positive ideal solution (A + ) and the negative ideal solution (A - )

[0188]

[0189]

[0190] Among them: J is the benefit indicator set, J′ is the cost indicator set, is the optimal value of all weighted normalized indicators of the nth solution, is the worst value of all weighted normalized indicators of the nth solution;

[0191] S33: Calculate the Euclidean distance of each solution to the positive ideal solution and the negative ideal solution and

[0192]

[0193]

[0194] in: is the optimal value of the jth indicator after weighted normalization, is the worst value of the jth indicator after weighted normalization, and m is the number of solutions;

[0195] S34: Calculate the grey correlation coefficient between the i-th solution and the positive ideal solution relative to the j-th index:

[0196]

[0197]

[0198] Among them, ρ is the resolution coefficient, R + is the grey correlation coefficient matrix of the positive ideal solution;

[0199] Calculate the grey correlation coefficient between the i-th solution and the negative ideal solution relative to the j-th index:

[0200]

[0201] S35: Calculate the grey correlation degree of the positive ideal solution:

[0202]

[0203] Calculate the grey relational degree of the negative ideal solution:

[0204]

[0205] S36: Calculate the closeness of the solution:

[0206] Normalize the Euclidean distance and grey correlation:

[0207]

[0208]

[0209] in, is the normalized value of the Euclidean distance to the positive ideal solution; is the normalized value of the Euclidean distance to the negative ideal solution; is the normalized value of the grey relational degree of the positive ideal solution; is the normalized value of the grey relational degree of the negative ideal solution; the Euclidean distance and the grey relational degree after normalization are combined:

[0210] Combine the normalized post-processed Euclidean distance and grey relational degree:

[0211]

[0212]

[0213] Among them: α is the position weight in the similarity calculation between the alternative solution and the ideal solution and β is the shape weight in the similarity calculation between the alternative solution and the ideal solution; is the value of the positive ideal solution after combining Euclidean distance and grey relational degree; is the value of the negative ideal solution after combining Euclidean distance and grey relational degree;

[0214] S37: Calculate the closeness:

[0215]

[0216] Z i Sort by, preferably Z i The larger solution is preferred.

[0217] In order to test the robustness of the calculation results and verify the feasibility and scientificity of the decision-making method, the calculation results are further subjected to sensitivity analysis. Sensitivity analysis refers to an uncertainty analysis method that finds sensitive factors that have an important impact on the decision results from many uncertain factors, analyzes and measures their influence and sensitivity on the decision results, and then tests the robustness of the decision results.

[0218] In some embodiments of the present invention, after the site selection final calculation is completed, the model verification can be further performed. The verification method is as follows:

[0219] Set all first-level indicator weights to be equal, and retain the second-level weights obtained by the original calculation;

[0220] or,

[0221] Keep the first-level weight index obtained by the original calculation, and set all the second-level weight indexes to be equal;

[0222] or,

[0223] Set all first-level indicator weights and second-level indicator weights to be equal;

[0224] Recalculate the site development sequence of the island wind and wave energy complementary power station and compare it with the original calculated site development sequence of the island wind and wave energy complementary power station. Compare the recalculated scheme ranking results with the original calculated scheme ranking results to see the degree of change in the ranking of all schemes. If the degree of change in the scheme ranking is small and the optimal scheme remains basically unchanged, it can be considered that the site selection decision model and model solution method have strong stability. Otherwise, the weight factor has a certain influence on the ranking of the schemes, and the greater the weight change, the more obvious the influence.

[0225] It can be seen that the verification method provided by the present invention is performed by changing the indicator weights, and one or more of the three verification methods can be selected for verification.

[0226] Through the model verification steps, the stability of the small-scale site selection decision model for island wind and wave complementary power stations was verified. The influence of indicator weight factors on the site selection decision of island wind and wave complementary power stations was fully considered. By changing the weights of the evaluation indicators, a sensitivity analysis was performed, and the site development sequence of island wind and wave complementary power stations under different situations was calculated. The robustness of the calculation results was tested and the feasibility and scientificity of the decision-making method were verified.

[0227] Relying on the research project of Shandong Province's marine energy industry development planning, the present invention has applied the established small-scale site selection decision model for island wind-wave complementary power stations to the site selection example of Qingdao's island wind-wave complementary power stations. According to the natural resource conditions of Qingdao, literature research and field investigation, four candidate islands were identified, namely, Nvdao, Xiaomai Island, Zhucha Island and Sanping Island, forming the alternative solution set A = {A1, A2, A3, A4}. The research results show that the site development sequence of Qingdao's island wind-wave complementary power stations is A3 (Zhucha Island) > A2 (Xiaomi Island) > A1 (Nvdao) > A4 (Sanping Island). Zhucha Island has the best wave energy resources and the highest gross national product, and its relative proximity is much greater than the other three islands. Therefore, Zhucha Island is the preferred island for Qingdao to build an island wind-wave complementary power station. With the change of the indicator weight, the results of the scheme ranking have not changed, which verifies the reliability of the model to a certain extent. The research results can provide a direct basis for the local construction of island wind-wave complementary power stations in Qingdao.

[0228] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A small-scale site selection decision method for an island wind-wave complementary power station, characterized in that: The following steps are involved: Modeling steps: constructing a decision-making model for the site selection of an island wind and wave energy complementary power station, the model including a set of evaluation indicators, the evaluation indicators including primary indicators: resource condition indicators, natural condition indicators, economic factor indicators, and social factor indicators; each primary indicator further includes a number of secondary indicators; Site selection solution steps: Based on the standardized results of the evaluation indicators, the site selection decision model of the island wind and wave energy complementary power station is solved; the solution process includes: introducing the collective decision-making theory into the characteristic vector method to calculate the weight of the first-level indicator, and using the entropy method to calculate the weight of the second-level indicator; using the combined weight method to solve the combined weight of the first-level indicator weight and the second-level indicator weight; and using the method of combining the ideal point method with the gray correlation analysis to calculate the closeness, and ranking the site selection of the alternatives based on the closeness; Prioritize alternative options with high proximity and build wind and wave complementary power stations on islands; The method for calculating the closeness and sorting the schemes includes: S31: Calculate the weighted normalized decision matrix: Where: v ij is the weighted normalized index value of the jth index in the i-th scheme, w c ' ij is the combined weight of the indicators, is the normalized attribute value of the jth indicator in the i-th scheme, n is the number of indicators, and m is the number of schemes; w′ cij =w ci × cij , w ci is the first-level indicator weight, w cij is the secondary indicator weight; S32: Determine the positive ideal solution (A + ) and the negative ideal solution (A - ) Among them: J is the benefit indicator set, J′ is the cost indicator set, is the optimal value of all weighted normalized indicators of the nth solution, is the worst value of all weighted normalized indicators of the nth solution; S33: Calculate the Euclidean distance of each solution to the positive ideal solution and the negative ideal solution and in: is the optimal value of the jth indicator after weighted normalization, is the worst value of the jth indicator after weighted normalization, and m is the number of solutions; S34: Calculate the grey correlation coefficient between the i-th solution and the positive ideal solution relative to the j-th index: Among them, ρ is the resolution coefficient, R + is the grey correlation coefficient matrix of the positive ideal solution; Calculate the grey correlation coefficient between the i-th solution and the negative ideal solution relative to the j-th index: S35: Calculate the grey correlation degree of the positive ideal solution: Calculate the grey relational degree of the negative ideal solution: S36: Calculate the closeness of the solution: Normalize the Euclidean distance and grey correlation: in, is the normalized value of the Euclidean distance to the positive ideal solution; is the normalized value of the Euclidean distance to the negative ideal solution; is the normalized value of the grey relational degree of the positive ideal solution; is the normalized value of the grey relational degree of the negative ideal solution; the normalized Euclidean distance and grey relational degree are combined: Among them: α is the position weight in the similarity calculation between the alternative solution and the ideal solution and β is the shape weight in the similarity calculation between the alternative solution and the ideal solution; is the value of the positive ideal solution after combining Euclidean distance and grey relational degree; is the value of the negative ideal solution after combining Euclidean distance and grey relational degree; S37: Calculate the closeness: Z i Sort by, preferably Z i The larger solution is preferred.

2. The small-scale site selection decision method for an island wind-wave complementary power station according to claim 1, characterized in that: The methods for calculating the weights of the first-level indicators include: S211: Construct a judgment matrix: B=(b ij ) n×n Where: b ij b is the first-level evaluation index i For the first-level evaluation index b j the relative importance of S212: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector: Bw=λ max Among them, λ max is the maximum eigenvalue of the judgment matrix B, w is the eigenvector corresponding to the maximum eigenvalue, and after normalization, it corresponds to the weight of each indicator; S213: Assume that there are L experts, forming an expert set E = {E1, E2, ..., E L }, the weight of each expert is {e1,e2,…,e l }; For each expert E L , the evaluation index weights solved are {w1,w2,...,w l }; Calculate the weight of evaluation indicators after collective decision-making: Where: w il represents the weight of the ith indicator obtained by the lth expert, e l represents the weight of the lth expert, Indicates w il E l Power.

3. The small-scale site selection decision method for an island wind-wave complementary power station as claimed in claim 2, characterized in that: In the process of calculating the weight of the first-level indicators, after calculating the maximum eigenvalue of the judgment matrix, the judgment matrix is ​​further tested for consistency; Among them, CR is the consistency ratio, CI is the consistency index, and RI is the random consistency index, which can be given; judge whether CR is less than 0.

1. If so, the judgment matrix meets the consistency requirements. If not, the judgment matrix does not meet the consistency requirements and needs to be reconstructed.

4. The small-scale site selection decision method for an island wind-wave complementary power station as claimed in claim 1, 2 or 3, characterized in that: The methods for calculating the weights of secondary indicators include: S221: Calculate the weight of the jth indicator in the i-th solution; Where: p ij is the weight of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the ith scheme, m is the number of schemes, and n is the number of indicators; S222: Calculate the entropy value of the jth evaluation index: Among them, E j is the entropy value of the jth evaluation index, K is the coefficient; S223: Calculate the weight of the j-th evaluation index: Among them, w j is the weight of the jth evaluation index.

5. The small-scale site selection decision method for an island wind-wave complementary power station according to claim 1, characterized in that: The normalization process includes the following steps: The evaluation indicators are divided into benefit indicators and cost indicators, and the indicators are vector-normalized; For benefit indicators: For cost-based indicators: In the formula, x ij is the attribute value of the jth indicator in the i-th solution, is the normalized attribute value of the jth indicator in the i-th scheme; The standardized indicators are used to solve the site selection problem.

6. The small-scale site selection decision method for an island wind-wave complementary power station according to claim 1, characterized in that: The secondary indicators of the self-resource condition indicators include: Annual average wave power density: used to measure the potential development capacity of wave energy; Annual average significant wave height: used to measure the potential development capacity of wave energy; Annual effective wave hours: used to measure the operational time of island wind and wave energy generation devices; Annual average wind speed: used to measure the potential development capacity of island wind energy; Annual average wind power density: used to measure the potential development capacity of island wind energy; Annual effective wind hours: used to measure the operational time of island wind and wave energy generation devices; The annual average wave power density is: Among them, P wave is the wave power density, ρ is the seawater density, H S is the effective wave height, g is the gravitational acceleration, T E For the energy cycle; The annual average wind power density is: Among them, P wind is the wind power density; ρ is the air density; n is the number of wind speed records within the set period; v i is the wind speed recorded for the ith time.

7. The small-scale site selection decision method for an island wind-wave complementary power station according to claim 1, characterized in that: Further model validation steps are included: Set all first-level indicator weights to be equal, and retain the second-level weights obtained by the original calculation; or, Keep the first-level weight index obtained by the original calculation, and set all the second-level weight indexes to be equal; or, Set all first-level indicator weights and second-level indicator weights to be equal; The site development sequence of island wind and wave energy complementary power stations is recalculated and compared with the original calculated site development sequence of island wind and wave energy complementary power stations.

8. The small-scale site selection decision method for an island wind-wave complementary power station as claimed in claim 1, characterized in that: Secondary indicators of natural conditions include water depth, seafloor geology, coastal erosion, and geological hazards; Secondary indicators of economic factors include total cost and number of people served; The secondary indicators of social factors include traffic conditions, policy incentives and the matching degree between electricity demand and supply.

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