An evaluation method for the entry-into-service strategy of main power supply points around cities

Abstract

The present invention relates to an evaluation method for the entry-into-service strategy of the main power supply points around the city, comprising the following steps: establishing a hierarchical system structure according to a number of entry-into-service strategies; the hierarchical system structure includes a target layer, a criterion layer, a sub-criterion layer and a scheme layer from top to bottom; constructing a judgment matrix according to the hierarchical system structure; the judgment matrix is ​​obtained according to the elements of two adjacent layers; obtaining the weight vector of each judgment matrix by the fuzzy hierarchical analysis method; obtaining the weight of each constraint condition of the last sub-criterion layer, and correcting the weight vector of the last sub-criterion layer according to the weight; obtaining the final weight vector of the target layer according to the weight vectors of each layer, and obtaining the evaluation result of each entry-in-service strategy according to the final weight vector. The invention evaluates the entry-into-service strategy by extracting the constraint conditions involved, based on the entropy weight method and the hierarchical method, and takes into account the influence of all constraint conditions on the entry-into-service strategy.

Description

Technical Field

[0001] The invention relates to an evaluation method for a main power supply point in the periphery of a city, and belongs to the technical field of electric power. Background Art

[0002] my country's power structure is dominated by coal-fired power generation. From the perspective of future development, thermal power generation will still dominate my country's power structure for a considerable period of time. In the face of the current and long-term development situation of coal-fired thermal power units, it is necessary to study their installation composition and status, conduct a comprehensive analysis of the reasonable economic and safe life of the units, and obtain the most accurate and quantitative retirement expectations of the in-service thermal power units, so as to provide basic and guiding data for the planning and construction of my country's coal-fired thermal power units, which is of great significance to the adjustment and orderly development of the power industry structure.

[0003] In the existing technology, there are two main evaluation methods for power point retirement. One is to judge whether the indicators of the unit itself meet the requirements of national policies, and the other is to analyze whether the retirement of the unit can meet the requirements of grid operation based on the load growth and power point construction in the power grid system. No matter which technical route is used, the response strategy after the retirement of the power point is not considered. In particular, for a specific project, how to use the original site when the unit is retired and the alternative plan of the power point after retirement are not considered, which cannot reflect the management concept of the whole life cycle process. Summary of the invention

[0004] In order to overcome the above problems, the present invention provides an evaluation method for the entry-into-service strategy of the main power supply points around the city. The evaluation method extracts the constraints involved, evaluates the entry-into-service strategy based on the entropy weight method and the hierarchical method, and takes into account the impact of all constraints on the entry-into-service strategy.

[0005] The technical solution of the present invention is as follows:

[0006] An evaluation method for a strategy for the entry-into-service of a main power supply point in a city periphery comprises the following steps:

[0007] A hierarchical system structure is established according to a number of in-service strategies; the hierarchical system structure includes, from top to bottom, a target layer, a criterion layer, a sub-criterion layer and a scheme layer; the sub-criterion layer is at least one; the criterion layer includes a constraint condition B of each in-service strategy; each sub-criterion layer includes a constraint condition C of the in-service strategy; the constraint condition C is a lower-level constraint condition of a constraint condition of an upper layer of the sub-criterion layer to which it belongs;

[0008] Constructing a judgment matrix according to the hierarchical system structure; the judgment matrix is ​​obtained according to the elements of two adjacent layers;

[0009] Obtaining the weight vector of each judgment matrix by fuzzy analytic hierarchy process;

[0010] Obtain the weights of each constraint condition of the last sub-criteria layer, and modify the weight vector of the last sub-criteria layer according to the weights;

[0011] The final weight vector of the target layer is obtained according to the weight vectors of each layer, and the evaluation results of each of the in-service strategies are obtained according to the final weight vector.

[0012] Furthermore, the judgment matrix is ​​constructed according to the hierarchical system structure, specifically:

[0013] Taking an element of this layer as the benchmark, use the comparison scale r ij Indicates the importance of the i-th element of the next layer relative to the j-th element; when the judgment matrix is ​​established between the criterion layer and the sub-criterion layer or between the sub-criterion layer and the sub-criterion layer, if the constraint condition C is irrelevant to the reference element, the comparison scale is r ij =0;

[0014] According to the comparison scale r ij Get the judgment matrix R of this element = (r ij ) n×n , where n is the number of elements in the next layer corresponding to the reference element.

[0015] Furthermore, the weight vectors of the judgment matrices are obtained by the fuzzy analytic hierarchy process, specifically:

[0016] According to the judgment matrix, the fuzzy consistency judgment matrix F=(f ij ) n×n ; Among them, f i and f j are the sum of the i-th and j-th rows of the matrix F respectively;

[0017] Get the sorting vector w of the fuzzy consistency judgment matrix F (0) =(w1,w2,…,w n ) T ;

[0018] Using the conversion formula The fuzzy consistency judgment matrix F is converted into a reciprocal matrix E = (e ij ) n×n ;

[0019] by is the initial value of the iteration of the eigenvalue method, through the iteration formula V k+1 =EV k Get the feature vector V k+1 , and obtain the feature vector V k+1 The infinite norm of ||V k+1|| ∞ ;

[0020] If ||V k+1 || ∞ -||V k || ∞ <, then the feature vector V k+1 Normalize and get the weight vector Complete the iteration; otherwise, is the new iteration initial value of the eigenvalue and iterates again.

[0021] Furthermore, the ordering vector w of the fuzzy consistency judgment matrix F is obtained. (0) =(w1,w2,…,w n ) T , specifically:

[0022] The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the row unification method: (0) , the formula is as follows:

[0023]

[0024] Furthermore, the ordering vector w of the fuzzy consistency judgment matrix F is obtained. (0) =(w1,w2,…,w n ) T , specifically:

[0025] The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the square root method: (0) , the formula is as follows:

[0026]

[0027] Furthermore, the weights of the constraints of the last sub-criteria layer are obtained, and the weight vector of the last sub-criteria layer is corrected according to the weights, specifically:

[0028] Obtain the evaluation values ​​of several experts on the i-th in-service strategy under the constraints C of the last sub-criteria layer to form an evaluation matrix U i =(u ijk′ ) m′×n′ ; where m′ is the number of constraints in the last sub-criteria layer, n′ is the number of experts, and u ijk′ is the evaluation value of the k′th expert under the jth constraint C under the i-th in-service strategy;

[0029] The evaluation matrix is ​​normalized to obtain a standardized evaluation matrix P i =(p ijk′ )m′×n′ ,in,

[0030] Get the standardized evaluation matrix P i The information entropy vector Q i =(q ik′ ),in,

[0031] The weight vector is obtained according to the information entropy vector in,

[0032] A weight vector is obtained according to the evaluation matrix and the weight vector

[0033] Let the weight vector w of the i-th constraint C of the last sub-criteria layer be (k) For WC i =(wc ik′ ) T ;

[0034] According to the weight vector WH i and weight vector WC i Get the weight vector S i =(s ik′ ),in, Where I is the number of active strategies; the weight vector S i As the weight vector of the last sub-criteria layer.

[0035] Furthermore, the final weight vector of the target layer is obtained according to the weight vectors of each layer, and the evaluation results of each of the in-service strategies are obtained according to the final weight vector, specifically:

[0036] Let the weight vector w of the target layer be (k) For WA=(wa l ) T L ; Where L is the number of constraints in the criterion layer; Let the weight vector w of the lt′th constraint in the criterion layer and the sub-criterion layer except the last sub-criterion layer be (k) For WBt lt′ =(wbt ltv ) T Lt′ ; Where t is the serial number of the layer to which the constraint condition belongs, and Lt′ is the number of constraints in the next layer to which the constraint condition belongs.

[0037] According to the weight vector WA of the solution layer, the weight vector WB of the criterion layer and the sub-criterion layer except the last sub-criterion layer tl′and the weight vector S of the last sub-criteria layer i Get the final weight vector:

[0038]

[0039] Where T is the sum of the number of criterion layers and sub-criterion layers except the last sub-criterion layer.

[0040] Among them, the active strategy corresponding to the maximum value in the final weight vector WF is the recommended strategy.

[0041] Furthermore, the constraints of the criterion layer include urban planning, environmental protection, power grid security, engineering construction plan, efficiency indicators and owner's wishes.

[0042] Furthermore, the constraints of the sub-criteria layer include electricity load level, heat load level, power source planning, policy orientation, emission indicators, reliability, technical solutions, construction cost, construction period, site utilization conditions, operating costs, sunk costs, fuel conditions, process level, electricity price expectations and power supply company preferences.

[0043] The present invention has the following beneficial effects:

[0044] 1. This evaluation method analyzes and extracts the constraints involved in various possible technical solutions adopted after the power source is put into service, constructs a hierarchical model, and uses the Delphi method to investigate technical experts in related fields to evaluate the constraints of each criterion layer when constructing the solution, establishes the evaluation system indicators of the influencing factors of the criterion layer, and provides effective indicator input for the multi-level comprehensive evaluation model. The advantage of this method is that it can comprehensively consider the impact of all constraints when constructing the commissioning strategy.

[0045] 2. This evaluation method uses the entropy weight method to determine the weight of expert opinion according to the variability of the indicator based on the indicators of the influencing factor evaluation system, calculates the entropy value of each indicator, and obtains the weight vector based on the entropy weight evaluation. The advantage of this method is that it can improve the problem of excessive subjective factors in the evaluation link, that is, it takes into account the opinions of experts and uses the information entropy system to normalize and process the data to obtain a more reasonable score for the binding conditions.

[0046] 3. This evaluation method calculates the weight distribution of the criterion layer according to the improved fuzzy analytic hierarchy process, and obtains the final weight vector after normalization. The weights determined by the entropy weight method and the analytic hierarchy process are processed by comprehensive weights to obtain the comprehensive weight vector of the criterion layer. Finally, the final weight vector of each scheme is calculated by the calculation method of the analytic hierarchy process. The advantage of this method is that it combines the consideration of the importance of the constraint conditions in the analytic hierarchy process and the consideration of each constraint condition meeting the project requirements in the entropy weight method, which can better characterize the satisfaction of the indicators to the project.

[0047] 4. When considering different projects, this evaluation method can divide the criterion layer into the criterion layer and the multi-level sub-criterion layer according to the size and complexity. The entropy weight method only needs to consider the weight of the last level of sub-criterion layer when fully determining the expert opinions. The advantage of this method is that the same element can be associated with different criterion elements, reflecting the correlation of the elements. At the same time, because only the entropy value calculation of the last level of sub-criteria needs to be considered, the calculation process is simplified. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process of the present invention.

[0049] Figure 2 Schematic diagram of a hierarchical system structure according to an embodiment of the present invention.

[0050] Figure 3 This is a comparison scale value selection rule according to an embodiment of the present invention.

[0051] Figure 4 FIG. 1 is a schematic diagram of the correlation between elements of each layer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Embodiment 1

[0054] See also Figure 1-2 , an evaluation method for the strategy of the main power supply point in the periphery of a city, comprising the following steps:

[0055] A hierarchical system structure is established according to a number of in-service strategies; the hierarchical system structure includes, from top to bottom, a target layer, a criterion layer, a sub-criterion layer and a scheme layer; the sub-criterion layer is at least one; the criterion layer includes a constraint condition B of each of the in-service strategies; each of the sub-criterion layers includes a constraint condition C of the in-service strategy; the constraint condition C is a subordinate constraint condition of the constraint condition of the upper layer of the sub-criterion layer to which it belongs; as in one embodiment of the present invention, the constraint condition C of the first sub-criterion layer is more specific than the constraint condition B of the criterion layer, and is its subordinate constraint condition, and the correlation between the constraints of the two adjacent layers is many-to-many, that is, a constraint condition of the upper layer can be related to multiple constraints of the lower layer at the same time, and a constraint condition of the lower layer can also be related to multiple constraints of the upper layer; the scheme layer includes each of the in-service strategies;

[0056] Constructing a judgment matrix according to the hierarchical system structure; the judgment matrix is ​​obtained according to the elements of two adjacent layers;

[0057] Obtaining the weight vector of each judgment matrix by fuzzy analytic hierarchy process;

[0058] Obtain the weights of each constraint condition of the last sub-criteria layer, and modify the weight vector of the last sub-criteria layer according to the weights;

[0059] The final weight vector of the target layer is obtained according to the weight vectors of each layer, and the evaluation results of each of the in-service strategies are obtained according to the final weight vector.

[0060] Embodiment 2

[0061] A method for evaluating the strategy of the main power supply points in the periphery of a city, based on the first embodiment, constructing a judgment matrix according to the hierarchical system structure, specifically:

[0062] Taking an element of this layer as the benchmark, use the comparison scale r ij Indicates the importance of the i-th element of the next layer relative to the j-th element; when the judgment matrix is ​​established between the criterion layer and the sub-criterion layer or between the sub-criterion layer and the sub-criterion layer, if the constraint condition C is irrelevant to the reference element, the comparison scale is r ij =0; In one embodiment of the present invention, using Figure 3 The value selection rule shown in the figure determines the comparison scale r ij In this embodiment, the number of sub-criteria layers is 1, and the correlation between the upper layer constraint condition and the lower layer constraint condition is as follows: Figure 4 , where the sub-criterion layer constraints that are independent of a certain criterion layer constraint are set to 0. For example, constraint C5 is independent of constraint B1, then the comparison scale r i5 and r 5j All are taken as 0.

[0063] According to the comparison scale r ij Get the judgment matrix R of this element = (r ij ) n×n , where n is the number of elements in the next layer corresponding to the reference element.

[0064] Furthermore, the weight vectors of the judgment matrices are obtained by the fuzzy analytic hierarchy process, specifically:

[0065] According to the judgment matrix, the fuzzy consistency judgment matrix F=(f ij ) n×n ; Among them, f i and f j are the sum of the i-th and j-th rows of the matrix F respectively;

[0066] Get the sorting vector w of the fuzzy consistency judgment matrix F (0) =(w1,w2,…,w n ) T ;

[0067] Using the conversion formula The fuzzy consistency judgment matrix F is converted into a reciprocal matrix E = (e ij ) n×n ;

[0068] by is the initial value of the iteration of the eigenvalue method, through the iteration formula V k+1 =EV k Get the feature vector V k+1 , and obtain the feature vector V k+1 The infinite norm of ||V k+1 || ∞ ;

[0069] If ||V k+1 || ∞ -||V k || ∞ <ε, then the eigenvector V k+1 Normalize and get the weight vector Complete the iteration; otherwise, is the new iteration initial value of the eigenvalue and iterates again.

[0070] In one embodiment of the present invention, the ordering vector w of the fuzzy consistency judgment matrix F is obtained. (0) =(w1,w2,…,w n ) T , specifically:

[0071] The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the row unification method: (0) , the formula is as follows:

[0072]

[0073] In another embodiment of the present invention, the ordering vector w of the fuzzy consistency judgment matrix F is obtained. (0) =(w1,w2,…,w n ) T , specifically:

[0074] The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the square root method: (0) , the formula is as follows:

[0075]

[0076] Embodiment 3

[0077] A method for evaluating the strategy of the main power supply points in the periphery of a city, based on the second embodiment, the weights of the constraints of the last layer of sub-criteria layers are obtained, and the weight vector of the last layer of sub-criteria layers is corrected according to the weights, specifically:

[0078] Obtain the evaluation values ​​of several experts on the i-th in-service strategy under the constraints C of the last sub-criteria layer to form an evaluation matrix U i =(u ijk′ ) m′×n′ ; where m′ is the number of constraints in the last sub-criteria layer, n′ is the number of experts, and u ijk′ is the evaluation value of the k′th expert under the jth constraint C under the i-th in-service strategy;

[0079] The evaluation matrix is ​​normalized to obtain a standardized evaluation matrix P i =(p ijk′ ) m′×n′ ,in, min(u ij ) and max(u ij ) represent the minimum and maximum values ​​of the evaluation values ​​of the experts on the same constraint condition of the same entry strategy;

[0080] Get the standardized evaluation matrix P i The information entropy vector Q i =(q ik′ ),in, At this time, the information entropy vector Q i is a row vector of 1×k′;

[0081] The weight vector is obtained according to the information entropy vector in, At this time, the weight vector H i is a k′×1 column vector;

[0082] A weight vector is obtained according to the evaluation matrix and the weight vector At this time, the weight vector WH i is a row vector of 1×m′;

[0083] Let the weight vector w of the i-th constraint C of the last sub-criteria layer be (k) For WC i =(wc ik′ ) T According to the second embodiment, At this time, w (k)is an n×1 column vector, n is the number of elements in the next layer to which the constraint condition belongs. Since the constraint condition C is the last sub-criteria layer, the next layer is the solution layer, and n is the number of active strategies. In this embodiment, let the weight vector w (k) For WC i =(wc ik′ ) T , is a different way of expressing the weight vector w (k) , which is convenient for the following calculation, namely

[0084] According to the weight vector WH i and weight vector WC i Get the weight vector S i =(s ik′ ),in, Where I is the number of active strategies; the weight vector S i As the weight vector of the last sub-criteria layer; at this time, S i is a 1×I row vector.

[0085] Furthermore, the final weight vector of the target layer is obtained according to the weight vectors of each layer, and the evaluation results of each of the in-service strategies are obtained according to the final weight vector, specifically:

[0086] Let the weight vector w of the target layer be (k) For WA=(wa l ) T L According to the second embodiment, At this time, w (k) is an n×1 column vector, n is the number of elements in the next layer to which the constraint belongs. Since this layer is the target layer and the next layer is the criterion layer, n is the number of constraints in the criterion layer; where L is the number of constraints in the criterion layer; this is a symbol replacement for the convenience of the following calculation. Let the weight vector w of the lt′th constraint condition of the criterion layer and the sub-criteria layer except the last sub-criteria layer be (k) For WBt lt′ =(wbt lt′ ) T Lt′ ; Wherein, t is the serial number of the layer to which the constraint condition belongs, that is, the criterion layer is 1, the first sub-criterion layer is 2, and so on. Lt′ is the number of constraints in the next layer of the layer to which the constraint condition belongs. The processing method here is the same as the processing method of the target layer weight vector in the above question.

[0087] According to the weight vector WA of the solution layer, the weight vector WB of the criterion layer and the sub-criterion layer except the last sub-criterion layer tl′ and the weight vector S of the last sub-criteria layer ik′ Get the final weight vector:

[0088]

[0089] Where T is the sum of the number of criterion layers and sub-criterion layers except the last sub-criterion layer. At this time, WF is an I×1 column vector, and each element in the column vector represents the evaluation value of the corresponding active strategy.

[0090] Among them, the active strategy corresponding to the maximum value in the final weight vector WF is the recommended strategy.

[0091] Embodiment 4

[0092] A method for evaluating the strategy for the entry-into-service of trunk power supply points around a city. Based on the first embodiment, the constraint conditions of the criterion layer include urban planning, environmental protection, power grid safety, engineering construction plan, efficiency index and owner's willingness.

[0093] Furthermore, the constraints of the sub-criteria layer include electricity load level, heat load level, power source planning, policy orientation, emission indicators, reliability, technical solutions, construction cost, construction period, site utilization conditions, operating costs, sunk costs, fuel conditions, process level, electricity price expectations and power supply company preferences.

[0094] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure made using the contents of the present invention's specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating the strategy of main power supply points in the periphery of a city, characterized in that: The following steps are involved: A hierarchical system structure is established according to a number of in-service strategies; the hierarchical system structure includes, from top to bottom, a target layer, a criterion layer, a sub-criterion layer and a scheme layer; the sub-criterion layer is at least one; the criterion layer includes a constraint condition B of each in-service strategy; each sub-criterion layer includes a constraint condition C of the in-service strategy; the constraint condition C is a lower-level constraint condition of a constraint condition of an upper layer of the sub-criterion layer to which it belongs; the scheme layer includes each in-service strategy; Constructing a judgment matrix according to the hierarchical system structure; the judgment matrix is ​​obtained according to the elements of two adjacent layers; Obtaining the weight vector of each judgment matrix by fuzzy analytic hierarchy process; Obtain the weights of each constraint condition of the last sub-criteria layer, and modify the weight vector of the last sub-criteria layer according to the weights; Obtaining a final weight vector of the target layer according to the weight vectors of each layer, and obtaining an evaluation result of each of the in-service strategies according to the final weight vector; The construction of the judgment matrix according to the hierarchical system structure is specifically as follows: Taking an element of this layer as the benchmark, use the comparison scale r ij Indicates the importance of the i-th element of the next layer relative to the j-th element; when the judgment matrix is ​​established between the criterion layer and the sub-criterion layer or between the sub-criterion layer and the sub-criterion layer, if the constraint condition C is irrelevant to the reference element, the comparison scale is r ij =0; According to the comparison scale r ij Get the judgment matrix R of this element = (r ij ) n×n , where n is the number of elements in the next layer corresponding to the reference element; The weight vectors of the judgment matrices are obtained by the fuzzy hierarchical analysis method, specifically: According to the judgment matrix, the fuzzy consistency judgment matrix F=(f ij ) n×n ; Among them, f i and f j are the sum of the i-th and j-th rows of the matrix F respectively; Get the sorting vector w of the fuzzy consistency judgment matrix F (0) =(w1, w2, ..., w n ) T ; Using the conversion formula The fuzzy consistency judgment matrix F is converted into a reciprocal matrix E = (e ij ) n×n ; by is the initial value of the iteration of the eigenvalue method, through the iteration formula V k+1 =EV k Get the feature vector V k+1 , and obtain the feature vector V k+1 The infinite norm of ||V k+1 || ∞ ; If ||V k+1 || ∞ -||V k || ∞ <ε, then the eigenvector V k+1 Normalize and get the weight vector Complete the iteration; otherwise, is the new iteration initial value of the eigenvalue, and iterates again; The step of obtaining the weights of the constraints of the last sub-criteria layer and correcting the weight vector of the last sub-criteria layer according to the weights is specifically as follows: Obtain the evaluation values ​​of several experts on the i-th in-service strategy under the constraints C of the last sub-criteria layer to form an evaluation matrix U i =(u ijk′ ) m′×n′ ; where m′ is the number of constraints in the last sub-criteria layer, n′ is the number of experts, is the evaluation value of the k′th expert under the jth constraint C under the i-th in-service strategy; The evaluation matrix is ​​normalized to obtain a standardized evaluation matrix P i =(p ijk′ ) m′×n′ ,in, Get the standardized evaluation matrix P i The information entropy vector Q i =(q ik′ ),in, The weight vector H is obtained according to the information entropy vector i =(h ik′ ) T ,in, The weight vector WH is obtained according to the evaluation matrix and the weight vector i =U i ×H i =(wh ik′ ); Let the weight vector w of the i-th constraint C of the last sub-criteria layer be (k) For WC i =(wc ik′ ) T ; According to the weight vector WH i and weight vector WC i Get the weight vector S i =(s ik′ ),in, Where I is the number of active strategies; the weight vector S i As the weight vector of the last sub-criteria layer; A constraint condition at the upper layer is simultaneously related to multiple constraints at the lower layer, and a constraint condition at the lower layer is also related to multiple constraints at the upper layer.

2. The evaluation method for the strategy of the main power supply points in the periphery of a city according to claim 1 is characterized in that: The step of obtaining the sorting vector w of the fuzzy consistency judgment matrix F (0) =(w1, w2, ..., w n ) T , specifically: The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the row unification method: (0) , the formula is as follows:

3. The evaluation method for the strategy of the main power supply points in the periphery of a city according to claim 1 is characterized in that: The step of obtaining the sorting vector w of the fuzzy consistency judgment matrix F (0) =(w1, w2, ..., w n ) T , specifically: The sorting vector w of the fuzzy consistency judgment matrix F is obtained by the square root method: (0) , the formula is as follows:

4. According to claim 1, the evaluation method for the strategy of the main power supply points in the periphery of the city is characterized by: The final weight vector of the target layer is obtained according to the weight vectors of each layer, and the evaluation results of each of the in-service strategies are obtained according to the final weight vector, specifically: Let the weight vector w of the target layer be (k) For WA=(wa l ) T L ; Where L is the number of constraints in the criterion layer; Let the weight vector w of the lt′th constraint in the criterion layer and the sub-criterion layer except the last sub-criterion layer be (k) For WBt lt′ =(wbt lt′ ) T Lt′ ; Where t is the serial number of the layer to which the constraint condition belongs, and Lt′ is the number of constraints in the next layer to which the constraint condition belongs; According to the weight vector WA of the solution layer, the weight vector WB of the criterion layer and the sub-criterion layer except the last sub-criterion layer tl′ and the weight vector S of the last sub-criteria layer i Get the final weight vector: Where T is the sum of the number of criterion layers and sub-criterion layers except the last sub-criterion layer; Among them, the active strategy corresponding to the maximum value in the final weight vector WF is the recommended strategy.

5. According to claim 1, the evaluation method for the strategy of the main power supply points in the periphery of the city is characterized by: The constraints of the criterion layer include urban planning, environmental protection, power grid security, engineering construction plan, efficiency indicators and the owner's wishes.

6. The evaluation method for the strategy of the main power supply points in the periphery of a city according to claim 1 is characterized in that: The constraints of the sub-criteria layer include electricity load level, heat load level, power source planning, policy orientation, emission indicators, reliability, technical solutions, construction cost, construction period, site utilization conditions, operating costs, sunk costs, fuel conditions, process level, electricity price expectations and power company preferences.

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