CN115526671A - New energy power station site selection method based on improved analytic hierarchy process - Google Patents
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Abstract
The invention relates to the technical field of new energy power station site selection, and provides a new energy power station site selection method based on an improved analytic hierarchy process, which comprises the following steps: selecting an evaluation index by adopting a general principle; establishing a comprehensive evaluation index system based on the first-level evaluation index and the second-level evaluation index; and based on a comprehensive evaluation index system, an improved analytic hierarchy process and a grey correlation degree analytical method are combined to make a comprehensive decision, and the site of the target new energy power station is selected. The method provided by the invention can well process uncertain factors; the situation that inconsistency often occurs in the judgment matrix is obviously improved; removing interference of subjective factors to obtain an objective site of the target new energy power station; the convergence speed and consistency are improved.
Description
Technical Field
The invention relates to the technical field of new energy power station site selection, in particular to a new energy power station site selection method based on an improved analytic hierarchy process.
Background
In order to realize energy conservation, emission reduction and energy pressure relief, various renewable new energy forms including wind energy, solar energy, nuclear energy and the like are explored and utilized by all countries in the world. Wherein, the wind energy is abundant in reserve, wide in distribution and low in greenhouse gas emission. In the development of various renewable and new resources, wind energy has the advantages of short investment period, high safety, huge scale production potential and the like. However, at present, wind power generation still has a plurality of problems, which causes a gap between global expectation and the reality of wind power development. In addition to being influenced by meteorological conditions and high initial investment costs, wind energy also has some negative effects on social and ecological environments, including noise, landscape and visual effects, and ecological damage. These negative effects will lead to criticism and objection by local governments and residents, severely affecting their large-scale development. It is clear that the above negative effects mainly result from the inappropriate location of the wind energy. Therefore, various influence factors are considered, a reasonable and scientific comprehensive site selection evaluation index system is established, and a method for evaluating the indexes and deciding that the site selection of the new energy power station is a problem to be solved urgently in the new energy site selection process is adopted.
The evaluation index system for selecting the new energy site at home and abroad mostly comprises an evaluation target layer, a criterion layer and an index layer. The criterion layer only has a plurality of evaluation indexes which are respectively economic factors, environmental factors, geographic factors and the like, and the index layer evaluation indexes are respectively wind energy resource conditions, regional stability, stratum lithology conditions, hydrogeological conditions, engineering land acquisition, transportation conditions, static unit construction cost, construction period loan interest and the like. The classification of these indicators is not clear and definite. And in some cases, the criterion layer is split, in other cases, the secondary indexes are combined into the criterion layer, and no clear specified index system exists. And the indexes are only some indexes which influence the site selection of the new energy wind power plant, and a plurality of key indexes are not completely established. Therefore, these indicators do not fully reflect how well the wind farm is addressed. In the prior art [1], see the 'Jiangxi province high mountain wind power station site selection method' published in No. 4 of volume 35 of electric power construction, a criterion layer in an evaluation index system provided by the paper is divided into a specificity evaluation index and a general evaluation index, and geographic environment factor indexes such as wind energy resource conditions, topographic and geological conditions and the like are considered in secondary indexes, so that indexes such as economic and technical factors are not considered; in the prior art [2], as seen in the 'fuzzy chromatography analysis based distributed wind power plant site selection method' published in volume 40, no. 1 of the 'Chinese Power', a criterion layer in an evaluation index system provided by the paper includes six indexes such as wind energy resource condition, social environment influence, power grid access and the like. And some of the indicators may be incorporated into the secondary indicators, resulting in too many indicators given by the criteria layer. The traditional analytic hierarchy process can not be good for managing uncertain factors, different experts have different approvals for the constructed judgment matrix, the judgment matrix often has the condition of inconsistency and the like.
Disclosure of Invention
In view of this, the invention provides a new energy power station site selection method based on an improved analytic hierarchy process, so as to solve the technical problems that uncertain factors cannot be handled well, different experts have different approvals for constructed judgment matrixes, and inconsistency often occurs in the judgment matrixes in the prior art.
The invention provides a new energy power station site selection method based on an improved analytic hierarchy process, which comprises the following steps:
s1, selecting evaluation indexes by adopting a general principle, wherein the evaluation indexes comprise a primary evaluation index and a secondary evaluation index;
s2, establishing a comprehensive evaluation index system based on the primary evaluation index and the secondary evaluation index;
s3, based on the comprehensive evaluation index system, an improved analytic hierarchy process and a grey correlation degree analytical method are combined to make a comprehensive decision, and a site of the target new energy power station is selected;
wherein the improved analytic hierarchy process comprises:
determining a new energy source address hierarchical structure;
solving the criterion layer to set sub-criterion weights and determining each evaluation index weight;
and the grey correlation degree analysis method comprises the following steps:
carrying out averaging processing on the evaluation index values of the evaluation schemes;
calculating the correlation coefficient of each evaluation index;
the grey correlation degree of each evaluation scheme was determined.
Further, in S1, the general principles include:
scientific principle, comprehensive principle, dominance principle and operability principle.
Further, in the step S2,
the first-level evaluation index comprises: geological conditions, meteorological conditions, social conditions, traffic conditions, grid access conditions and economic conditions;
the secondary evaluation indexes include: the method comprises the following steps of terrain gradient, terrain relief degree, hydrogeological conditions, annual average wind speed, annual average wind direction, annual effective utilization hours, distances between a power plant and surrounding farmlands, residential area distances, noise value influences, energy-saving and emission-reducing benefits, policy conditions, traffic transportation time, traffic transportation cost, main road distances, new road difficulty, new road conversion length, average voltage deviation, network loss rate, average load rate, access power grid line length, access power grid capacity, unit power generation cost, investment recovery period, total project investment, static unit manufacturing cost and construction period loan interest. Further, the solving the criterion layer to set sub-criterion weights comprises:
establishing a comparison matrix A by adopting a three-scale method to compare the relative importance of indexes pairwise;
constructing a judgment matrix C;
solving the weight vector and checking consistency;
and determining each evaluation index weight.
Further, the criterion layer setting the sub-criterion weight includes the following expression:
wherein, W (k) ＝(w 1 k w 2 k …w n k ) K =1,2, \8230;, m; m represents the number of participating experts; w (k) Representing a weight vector formed by the kth judgment matrix;
representing an expectation of a weight vector; w is a i Indicating the ith index weight.
Further, the grey correlation degree includes the following expression:
wherein, gamma is i And (3) representing the relevance of the ith evaluation scheme and the ideal scheme.
Further, the gray-level correlation is obtained by obtaining a correlation coefficient for each evaluation index.
Further, the evaluation index correlation coefficients include the following expression:
wherein ξ i (j) The j index correlation coefficient is obtained; delta j (j)＝∣x′ 0 (j)-x′ i (j)∣，x′ 0 (j) As the jth index value in the ideal case,
rho is a resolution coefficient, and the value range is [0, 1]]Removing subjective factors to optimize to obtain ρ =0.69.
Compared with the prior art, the invention has the following beneficial effects:
1. the improved analytic hierarchy process provided by the invention can well process uncertain factors.
2. The invention adopts the combination of a grey correlation analysis method and an improved analytic hierarchy process to remove subjective factor interference and obtain the objective site of the target new energy power station.
3. The invention adopts a three-scale method to construct the judgment matrix, thereby improving the convergence speed and consistency.
4. The invention adopts the expectation of forming the weight vector by normalizing each expert judgment matrix as the weight of each index, and obviously improves the conditions that different experts have different approvals for the constructed judgment matrix and the judgment matrix is often inconsistent.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed for the embodiment or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flowchart of a new energy power station site selection method based on an improved analytic hierarchy process according to an embodiment of the present invention;
FIG. 2 is a flow chart of a comprehensive decision provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a comprehensive evaluation index system for site selection of a new energy power station according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The new energy power station site selection method based on the improved analytic hierarchy process of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a new energy power station site selection method based on an improved analytic hierarchy process according to an embodiment of the present invention.
Fig. 2 is a flowchart of a comprehensive decision provided by an embodiment of the present invention.
As shown in fig. 1, the method for establishing and improving the analytic hierarchy process includes:
fig. 3 is a schematic diagram of a new energy power station site selection comprehensive evaluation index system provided by an embodiment of the invention.
S1, selecting evaluation indexes by adopting a general principle, wherein the evaluation indexes comprise a primary evaluation index and a secondary evaluation index;
in S1, the general principles include:
scientific principle, comprehensive principle, dominance principle and operability principle.
1.1 scientific principles
Whether the evaluation index system is scientific or not directly concerns the result of site selection of the new energy power station. Therefore, the new energy power station evaluation index system is required to be in accordance with the basic characteristics of the new energy power station site, follow the national strategy of sustainable development, and can not only pay attention to economic factors but ignore factors such as environment and safety.
1.2 general principles
Factors influencing the site construction of the new energy power station are many, the characteristics of the region can be accurately described, the factors influencing the site selection of the new energy power station are considered thoroughly, any factors which are difficult to find are not ignored, and otherwise, a scientific and reasonable conclusion cannot be drawn even if an advanced method and an advanced technology are used.
1.3 principle of dominance
The factors influencing the construction of the new energy power station site are a leading factor and have obvious influence on the site selection, and a secondary factor and have small influence on the site, so that the factors are scientifically measured on the basis of the comprehensive principle, the representativeness of the factors is considered, the factor playing the leading role is selected, and the secondary factor is eliminated. If all factors are considered for evaluation, the index system is necessarily bloated and large, and wrong conclusions are more likely to appear during evaluation.
1.4 principle of operability
The evaluation index system is mainly established for being applied to new energy power station site selection, and therefore the established index system must have feasibility and operability. Therefore, the index system is not applicable but complex as much as possible, the index data is easy to obtain, the index system is simple and applicable, and the evaluation process is simple and is beneficial to operation.
S2, establishing a comprehensive evaluation index system based on the primary evaluation index and the secondary evaluation index;
in the above-mentioned step S2, the step (B) is carried out,
the first-level evaluation index comprises: geological conditions, meteorological conditions, social conditions, traffic conditions, grid access conditions and economic conditions;
the secondary evaluation indexes include: the method comprises the following steps of terrain gradient, terrain relief degree, hydrogeological conditions, annual average wind speed, annual average wind direction, annual effective utilization hours, distances between a power plant and surrounding farmlands, residential area distances, noise value influences, energy-saving and emission-reducing benefits, policy conditions, traffic transportation time, traffic transportation cost, main road distances, new road difficulty, new road conversion length, average voltage deviation, network loss rate, average load rate, access power grid line length, access power grid capacity, unit power generation cost, investment recovery period, total project investment, static unit manufacturing cost and construction period loan interest.
2.1.1 topographical gradient
The slope means steeper terrain and installation of the power facility is difficult. Furthermore, unfavorable terrain requires good wind turbine performance and high infrastructure costs. Therefore, it is recommended to select a field with a flat topography. The appropriate position is limited to a slope of less than or equal to 15. The gradient of most (90%) wind power plants in China is less than 10 degrees, and the gradient of most (74.4%) wind power plants is within 5 degrees. In order to score terrain conditions during comprehensive site selection, dimensionless processing is performed on the slope R. The gradient 10 ° is set as the intermediate value, and the gradient 20 ° is set as the upper limit of the gradient. A dimensionless slope S' definition is introduced.
Wherein: s represents an actual measured value of the terrain gradient.
2.1.2 relief
The terrain relief degree of most (88.6%) wind power plants in China is less than 300m, and the terrain relief degree of most (78.9%) wind power plants is within 200 m. In order to score the terrain conditions during comprehensive site selection, dimensionless processing is performed on the gradient S. The mean value of the topographic relief degree was 300m, and the upper limit of the topographic relief degree was 600 m. And introducing the definition of the degree of undulation R' of the dimensionless terrain.
Wherein, S represents the measured value of the relief degree, and is represented by the difference between the altitude of the highest point and the altitude of the lowest point in the field area.
2.1.3 hydrogeological conditions
The hydrogeological conditions refer to the type of ground water in the site, the influence of the type of the ground water on the basic design and construction, and whether the ground water in the site has corrosiveness on reinforcing steel bars in concrete.
2.2 weather conditions
The meteorological conditions considered in the macro site selection of the new energy power station mainly refer to wind speed, wind direction, annual effective hours and the like.
Average wind speed of 2.2.1 years
Wind speed is a key evaluation standard and a ubiquitous technical index for wind power development. High wind speed means rich wind energy resources, which is beneficial to increase the yield. The wind speed is closely related to the local climate, geography, weather, etc. Because wind has statistical properties, the wind speed laws in various regions must be calculated according to the frequency of occurrence of wind speeds of various levels [1,2,3, \8230; (30, \8230; (m/s) ]. The Weibull distribution describes the statistical distribution rule of wind speed and is representative. The formula is as follows:
where k and c represent the shape parameter and the scale parameter, respectively.
Average wind direction of 2.2.2 years
The wind direction distribution directly influences the arrangement mode of the wind generating set in the wind power plant, if the wind turbine is mainly caught
The wind catching direction is the same as the main prevailing wind direction, so that the output of the fan is large, the generating efficiency of the fan is high, and otherwise, the generating efficiency is low. Therefore, the prevailing wind direction and its range of variation is to be precise. We usually describe and study the statistical laws of wind direction by using a wind direction rose diagram. The wind direction rose diagram is a statistical diagram of the frequency of each wind direction or the average wind speed of each wind direction in a certain time period in a certain area plotted on a polar coordinate base diagram. The pattern is similar to a rose flower, so a wind rose diagram is obtained.
Number of effective utilization hours of 2.2.3 years
Wind generators typically have a cut-in and cut-out speed, where the cut-in wind speed is about 3m/s or 5m/s and the cut-out wind speed is typically 20m/s. We refer to the speed between sending two wind speeds as the effective wind speed. In practice, the effective wind speed will vary depending on the type and model of the wind turbine. Number of years of validity: and (4) counting the annual accumulated wind speed value, and averaging the hours in the effective wind speed range according to the year.
2.3 social Condition
The social conditions considered in the macro site selection of the new energy power station mainly refer to distances from surrounding farmlands and residential areas, noise value influence, energy-saving and emission-reducing benefits, political conditions and the like.
2.3.1 distance between power plant and surrounding farmland and residential area
Considering the influence of the products in the industrial production, transportation and other processes of the power plant on the residential area, the power plant is ensured to be at a considerable distance from the surrounding farmlands and residential areas.
2.3.2 noise value influence
According to the noise emission standard of the industrial enterprise boundary, the distance between the boundary of the wind power plant and the surrounding villages is implemented according to the I-type standard in the noise emission standard of the industrial enterprise boundary, namely, the distance does not exceed 55dB (A) in the daytime and 45dB (A) in the nighttime. If the sound power level of A of a point sound source is known and the sound source is in a free sound field, then the noise of neighboring residential points is predicted using the following formula:
L A (r)＝L AW -20lg(r)-11 (2-4)
where LA (r) represents the a sound level (dB) at a distance r from the sound source; LAW represents the A sound power level (dB) of the sound source.
2.3.3 benefits of energy conservation and emission reduction
Wind energy is a renewable, clean energy source. Wind power generation is a process of converting local natural wind energy into mechanical energy and then converting the mechanical energy into electric energy. No harmful gas is discharged in the production process, and the environment is not polluted.
The annual grid electricity quantity of the wind power plant is assumed to be a MW.h Tun, compared with thermal power of coal, the annual grid electricity quantity of the wind power plant can save 350a kg of standard coal for the country each year in the unit-degree electricity standard coal consumption of 350 g/kW.h. According to the design water consumption of a coal-fired thermal power plant, about 28.8 million tons/hundred million kW.h, the wind power plant can save 28800 million tons of water resources for the country every year.
2.3.4 political conditions
Political conditions mainly refer to the policy requirements of countries and provincial and municipal regions on new energy power station site selection.
2.4 traffic conditions
The traffic conditions considered in the macro site selection of the new energy power station mainly refer to traffic transportation time, transportation cost, distance from a main road, difficulty of a newly-built road, reduced length of the newly-built road and the like.
2.4.1 time of transportation
The transportation time is the time for transporting the fan equipment from a manufacturer to a wind power plant site by adopting an optimal transportation mode.
2.4.2 cost of transportation
The transportation cost is the cost for transporting the fan equipment from a manufacturer to a wind power plant site by adopting an optimal transportation mode, and comprises the following steps: transportation fees, road tolls, and the like. The lower the transportation cost, the better.
2.4.3 Main road distance
The expressway is divided into 5 types of roads, namely national roads, provinces roads, counties roads, villages roads and villages roads, which are regulated by the government. The national road, the provincial road and the county road are spacious in space, smooth in road surface and convenient to transport. Therefore, the suitable position of the new energy power station should be as short as possible for the above three road types to avoid excessive economic cost.
2.4.4 difficulty of building new road
Due to geological and topographic reasons, road construction has different difficulty degrees, and { simple, medium and complex } evaluation is carried out on the construction conditions of the newly-built road according to geological and topographic conditions.
2.4.5 reduced length of newly-built road
Whether the road is an off-site road or an on-site road, building (rebuilding) roads with different specifications and grades correspondingly pays different economic costs. In order to estimate the cost, a standard road needs to be defined as a reference for uniform calculation. The road which can meet the transportation requirement of a wind power unit of 600kW level under the condition of flat terrain and good engineering geology is taken as a benchmark, namely, the length of a newly built (rebuilt) road under other conditions is converted into the length of the newly built (rebuilt) benchmark road. Generally, the cost of creating (rebuilding) a unit length reference road and a non-reference road can be estimated from local statistics.
2.5 grid Access Condition
The power grid access conditions considered in the macro site selection of the new energy power station mainly refer to average voltage deviation, grid loss rate, average load rate, access power grid line length, access power grid capacity and the like.
2.5.1 mean voltage deviation
The influence of the distributed power supply connected to the power distribution network on the system voltage is mainly reflected in the change of the original grid structure of the system, the node voltage is greatly influenced by the system tide distribution, particularly for some intermittent distributed power supplies such as wind power generation and photovoltaic power generation, the uncertainty of the output of the distributed power supplies inevitably causes the increase of the uncertainty of the system operation, and the uncertainty has great influence on the node voltage of the system.
The average voltage deviation index a11 is calculated as follows:
wherein N represents the number of nodes of the grid structure；U i Represents the voltage of the ith node; u shape in Indicating the rated voltage of the ith node.
2.5.2 net loss Rate
Different schemes generate unequal network loss, and the ratio of the network loss to the power supply amount is used to express the network loss rate:
wherein M represents the number of lines from the transformer to the lowest point of the voltage; s i ，U i ，R i The maximum load power, the node voltage and the corresponding line resistance of the ith line are respectively; e G ,E O Respectively the generated energy and the station service power consumption.
2.5.3 average load Rate
The average load rate represents the change condition of the network transmission power after the wind power is accessed.
Wherein: p i ，Q i Respectively representing active power and reactive power transmitted on the ith line; s. the imax Which represents the maximum transmission power value of the ith line.
2.5.4 Access grid line Length
The wind farm is required to be close to the grid, and generally less than 20km. Because the grid-connected power supply is close to the power grid, the grid-connected investment can be reduced, the line loss is reduced, and the requirement of voltage drop is easily met.
2.5.5 Access grid Capacity
Because the wind power generation output has larger randomness, the power grid has enough capacity so as to avoid the damage to the power grid caused by the random change of the grid-connected output of the wind power plant or the shutdown and disconnection. Generally speaking, the total capacity of the wind power plant should not be greater than 5% of the total capacity of the power grid, otherwise special measures should be taken to meet the grid stability requirements.
2.6 economic Condition
2.6.1 Unit cost of Power Generation
The unit power generation cost refers to a ratio of the total power generation cost to the total power generation amount. The total cost of electricity generation includes capital cost
Cost and operating costs. Wherein the investment cost mainly refers to project construction cost, and the operation cost comprises depreciation cost and transportation cost
Line maintenance cost, grid-connected additional cost, tax, and the like, which are formulated as
Wherein, C w Representing the average power generation cost over the economic life, n representing the economic life of the investment, E representing the annual energy production, i.e. the power generation, K 0 Representing the original investment, f w Denotes the depreciation rate, ci denotes the cost of the i-th year, and p denotes the depreciation rate.
2.6.2 recovery period on investment
The investment recovery period refers to how long the invested capital funds can be recovered after the wind power plant is put into production and operated. Generally, the recovery period is in units of years. It should be noted that the investment recovery period is calculated from the construction period of the wind power plant, and if the investment recovery period is calculated from the time of commissioning, the investment recovery period needs to be explained to avoid concept confusion. The static recovery period (static PP) is formulated as
Wherein T represents the number of years that the cumulative net cash flow begins to take on positive values, NCF represents the net cash flow t-1 Representing the net cash flow for the previous year. The calculation process of the dynamic recovery period (dynamic PP) needs to reflect the cash flow, and the formula is expressed as
Where NCF' represents the discounted value of net cash flow. Because dynamic recycle periods are more realistic than static recycle periods, only dynamic recycle periods are considered herein.
2.6.3 Total investment in project
The total investment of the project refers to the sum of all the investments of the wind power plant in the construction period.
2.6.4 static Unit cost
The static unit cost refers to the cost of generating capacity per kilowatt, and does not consider the capital and time value.
2.6.5 construction period loan interest
The construction period loan interest refers to the sum of the interest generated from the bank loan during the construction period. According to the reckoning, the reckoning calculation formula is expressed as follows:
W＝P(1+i) n -P(2-11)
wherein W represents interest in dividend, P represents principal of borrowing, i represents interest rate of borrowing, and n represents year of borrowing.
S3, based on the comprehensive evaluation index system, an improved analytic hierarchy process and a grey correlation degree analytical method are combined to make a comprehensive decision, and a site of the target new energy power station is selected;
wherein the improved analytical hierarchy process comprises:
determining a new energy source address hierarchical structure;
solving the criterion layer to set sub-criterion weights and determining each evaluation index weight;
and the grey correlation degree analysis method comprises the following steps:
carrying out averaging processing on the evaluation index values of the evaluation schemes;
calculating the correlation coefficient of each evaluation index;
the grey correlation of each evaluation protocol was determined.
The analytic hierarchy process is a systematic and hierarchical analysis method proposed by american research scientist t.l. saaty in the middle of the 70's 20 th century. But it is not good to deal with the uncertain factors, different experts have different approvals for the constructed judgment matrix, and the judgment matrix often has the inconsistency.
The invention adopts a three-scale method which is different from the traditional analytic hierarchy process with the scale of 1-9 to construct a judgment matrix so as to improve the convergence rate and consistency; normalizing each expert judgment matrix to form an expectation of a weight vector as the weight of each index.
The S3 comprises the following steps:
3.1 the improved analytical hierarchy process comprises:
3.1.1, determining a new energy resource address hierarchical structure;
comprehensively considering the Google earth, statistically analyzing site selection traffic, grid connection, construction technical conditions and the like, and considering factors such as wind energy, geology, economy, environmental influence and the like, and determining the hierarchical structure of the site selection of the new energy power station.
3.1.2 solving the rule layer to set sub-rule weights;
the solving the criterion layer to set sub-criterion weights comprises:
(1) adopting a three-scale method to compare the relative importance of the indexes pairwise and establishing a comparison matrix A;
the following comparison matrix a is obtained by comparing the relative importance of the indicators in pairs using a three-scale method.
Wherein, a 1 ～a n For each index;
wherein, a ij Represents the index a i And index a j The result of the comparison therebetween.
(2) Constructing a judgment matrix C
Wherein, the first and the second end of the pipe are connected with each other,
(3) solving weight vectors and checking consistency
Solving the maximum eigenvalue lambda of the judgment matrix C by adopting an eigenvector method max And the corresponding feature vector W is a weight vector.
Then will lambda max Introduction of compatibility index
And checking the consistency of the judgment matrix. When CI is present<0.1, judging that the consistency of the matrix meets the requirement; and (4) judging that the consistency of the matrix does not meet the requirement by CI being more than or equal to 0.1, modifying the comparison matrix, recalculating and checking the consistency until the consistency meets the requirement.
(4) Determining each evaluation index weight
Normalizing the weight vector formed by the m expert judgment matrices
The weight of each index is obtained and,
the criterion layer setting sub-criterion weight comprises the following expression:
wherein, W (k) ＝(w 1 k w 2 k …w n k ) K =1,2, \ 8230;, m; m represents the number of participating experts; w is a group of (k) Representing a weight vector formed by the kth judgment matrix;
representing an expectation of a weight vector; w is a i Indicating the ith index weight.
Wherein, W (k) ＝(w 1 k w 2 k …w n k ) K =1,2, \ 8230;, m; m represents the number of participating experts; w (k) Representing a weight vector formed by the kth judgment matrix;
representing an expectation of a weight vector; w is a i Indicating the ith index weight.
3.2 the grey correlation analysis method comprises the following steps:
3.2.1 equalization treatment;
the evaluation scheme number m and the index n (C) of the index layer are processed by the equalization processing of a formula (3-4) 11 -C 55 ) X (m × n) of the composition.
Wherein x represents an array, mxn represents a matrix, m is a scheme, n is a scalar number,
wherein x is i (j) Indicating the jth index value under the ith scheme;
representing a desired value; x' i (j) Representing the averaged value.
3.2.2, solving the correlation coefficient of each evaluation index;
the gray correlation degree is obtained by obtaining a correlation coefficient of each evaluation index.
The evaluation index correlation coefficient includes the following expression:
wherein ξ i (j) Representing the j index correlation coefficient; delta of j (j)＝∣x′ 0 (j)-x′ i (j)∣，x′ 0 (j) Representing the jth index value in the ideal scheme;
rho represents a resolution coefficient, and the value interval is [0, 1]]Removing subjective factors to optimize to obtain ρ =0.
3.2.3 determine the grey relevance of each evaluation protocol.
And determining the weight of each index and the correlation coefficient of each index by an AHP (attitude and heading) formula (3-5), and performing comprehensive decision to obtain the grey correlation degree of each evaluation scheme.
Wherein AHP, full-spelling Analytic Hierarchy Process, analytic Hierarchy Process;
the grey correlation degree comprises the following expression:
wherein, γ i And (3) representing the relevance of the ith evaluation scheme and the ideal scheme.
The improved analytic hierarchy process provided by the invention can well process uncertain factors; combining a grey correlation analysis method with an improved analytic hierarchy process to remove interference of subjective factors and obtain an objective site of the target new energy power station; a judgment matrix is constructed by adopting a three-scale method, so that the convergence speed and consistency are improved; the expectation of the weight vector formed by normalizing each expert judgment matrix is used as the weight of each index, and the conditions that different experts have different approvals on the constructed judgment matrix and the judgment matrix is often inconsistent are remarkably improved.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described herein again.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. A new energy power station site selection method based on an improved analytic hierarchy process is characterized by comprising the following steps:
s1, selecting evaluation indexes by adopting a general principle, wherein the evaluation indexes comprise a primary evaluation index and a secondary evaluation index;
s2, establishing a comprehensive evaluation index system based on the primary evaluation index and the secondary evaluation index;
s3, based on the comprehensive evaluation index system, an improved analytic hierarchy process and a gray correlation degree analytical method are combined to make a comprehensive decision, and a field address of the target new energy power station is selected;
wherein the improved analytic hierarchy process comprises:
determining a new energy source address hierarchical structure;
solving the criterion layer to set sub-criterion weights and determining each evaluation index weight;
and the grey correlation degree analysis method comprises the following steps:
carrying out averaging processing on the evaluation index values of the evaluation schemes;
calculating the correlation coefficient of each evaluation index;
the grey correlation of each evaluation protocol was determined.
2. The new energy power station site selection method of claim 1, wherein in S1, the general principle comprises:
scientific principle, comprehensive principle, dominant principle and operability principle.
3. The new energy power station site selection method of claim 1, characterized in that in S2,
the first-level evaluation index comprises: geological conditions, meteorological conditions, social conditions, traffic conditions, grid access conditions and economic conditions;
the secondary evaluation indexes include: the method comprises the following steps of terrain gradient, terrain relief degree, hydrogeological conditions, annual average wind speed, annual average wind direction, annual effective utilization hours, distances between a power plant and surrounding farmlands, residential area distances, noise value influences, energy-saving and emission-reducing benefits, policy conditions, traffic transportation time, traffic transportation cost, main road distances, new road difficulty, new road conversion length, average voltage deviation, network loss rate, average load rate, access power grid line length, access power grid capacity, unit power generation cost, investment recovery period, total project investment, static unit manufacturing cost and construction period loan interest.
4. The new energy power station site selection method of claim 1, wherein the solving the criteria layer set sub-criteria weights comprises:
establishing a comparison matrix A by adopting a three-scale method to compare the relative importance of indexes pairwise;
constructing a judgment matrix C;
solving the weight vector and checking consistency;
and determining each evaluation index weight.
5. The new energy power station site selection method of claim 1, wherein the criterion layer setting sub-criterion weight comprises the following expression:
wherein, W (k) ＝(w 1 k w 2 k …w n k ) K =1,2, \ 8230;, m; m represents the number of participating experts; w is a group of (k) Representing a weight vector formed by the kth judgment matrix;
representing an expectation of a weight vector; w is a i Indicating the ith index weight.
6. The new energy power station site selection method of claim 1, wherein the grey correlation comprises the following expression:
wherein, γ i And (3) representing the relevance of the ith evaluation scheme and the ideal scheme.
7. The new energy power station site selection method according to claim 1, characterized in that the grey correlation degree is obtained by obtaining a correlation coefficient of each evaluation index.
8. The new energy power station site selection method according to claim 1, wherein the evaluation index correlation coefficient includes the following expression:
wherein ξ i (j) The j index correlation coefficient is obtained; delta j (j)＝∣x′ 0 (j)-x′ i (j)∣，x′ 0 (j) The j index value in the ideal scheme,
rho is a resolution coefficient and a value areaHas a spacing of [0, 1]]Removing subjective factors to optimize to obtain ρ =0.69.
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