CN115829144A - Method for establishing power grid service optimization model and electronic equipment - Google Patents
Method for establishing power grid service optimization model and electronic equipment Download PDFInfo
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Abstract
The invention provides a method for establishing a power grid service optimization model and electronic equipment. The method comprises the following steps: constructing power grid service coupling indexes, and weighting each power grid service coupling index to obtain the weight of each power grid service coupling index; respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index; obtaining a quantized value of the service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantized value of the service contribution index; constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; and the power grid service optimization model is used for carrying out optimization combination on each power grid service so as to obtain the optimal service combination. The invention can effectively realize the combination optimization of the power grid service.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a method for establishing a power grid service optimization model and electronic equipment.
Background
The existing power grid enterprises are not limited to traditional power transmission and distribution services any more, and the development direction needs to be transferred to emerging services such as an intelligent energy integrated system, multi-station fusion and a virtual power plant.
When developing emerging services, due to the various types and complex relationships of the emerging services, various coupling relationships necessarily exist among the emerging services and between the emerging services and the traditional services. The traditional power grid service optimization method only quantificationally overlaps the contribution degrees of all services through multi-dimensional indexes to realize the combination optimization among all services, does not consider the coupling relation and the influence among all services, and is not suitable for the existing emerging services.
Therefore, a power grid service optimization method considering the coupling relationship between services is urgently needed to be explored to guide the service development of a power grid enterprise.
Disclosure of Invention
The embodiment of the invention provides a method for establishing a power grid service optimization model and electronic equipment, and aims to solve the problem that the traditional power grid service optimization method does not consider the coupling relation among services, so that the power grid service combination optimization cannot be effectively realized.
In a first aspect, an embodiment of the present invention provides a method for establishing a power grid service optimization model, including:
constructing power grid service coupling indexes, and weighting each power grid service coupling index to obtain the weight of each power grid service coupling index;
respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index;
obtaining a quantized value of a service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantized value of the service contribution index;
constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; and the power grid service optimization model is used for optimizing and combining each power grid service to obtain an optimal service combination.
In a possible implementation manner, the calculating, according to the weight of each grid service coupling indicator, a service coupling degree between every two grid services respectively includes:
calculating a first fuzzy measure according to the weight of each power grid service coupling index;
aiming at every two power grid services, respectively acquiring a preset evaluation value of each power grid service coupling index between the two power grid services, and respectively calculating a single evaluation value of the service coupling degree between the two power grid services according to the preset evaluation value and the first fuzzy measure;
and calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation.
In a possible implementation manner, before the calculating, according to all the calculated single evaluation values, a comprehensive evaluation value of a service coupling degree between two grid services, the method further includes:
acquiring the weight of the preset evaluation value, and calculating a second fuzzy measure according to the weight of the preset evaluation value;
the calculating of the comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation comprises the following steps:
and calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation and the second fuzzy measure.
In a possible implementation manner, the calculating a first fuzzy measure according to the weight of each grid service coupling indicator includes:
according to
Calculating a first blur measure;
wherein λ is 1 Representing a first measure of blur, g (I) u ) Represents the u-th power grid service coupling index I u N represents the number of grid service coupling indicators.
In a possible implementation manner, calculating a single evaluation value of service coupling degrees between two power grid services according to the preset evaluation value and the first fuzzy measure respectively includes:
according to
Respectively calculating a single evaluation value of service coupling degree between two power grid services;
wherein the content of the first and second substances,
a single evaluation value of the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value,
a kth predetermined evaluation value, g (I), representing the u-th grid service coupling indicator between two grid services u ) Representing the u-th grid service coupling index I between two grid services u Weight of (a), λ 1 Representing a first measure of blur, g (I) u+1 ) Represents the u +1 th grid service coupling index I between two grid services u+1 N represents the number of grid service coupling indicators.
In a possible implementation manner, the calculating, according to all the single evaluation values obtained by the calculation and the second fuzzy measure, a comprehensive evaluation value of a service coupling degree between two power grid services includes:
according to
Comprehensive evaluation for calculating service coupling degree between two power grid servicesA value;
wherein r is ij A comprehensive evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service,
a single evaluation value g (q) representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value k ) Indicates the k-th preset evaluation value q k Weight of (a), λ 2 Representing a second measure of blur, g (q) k+1 ) Represents the k +1 th preset evaluation value q k+1 M represents the number of preset evaluation values.
In a possible implementation manner, the constructing a power grid service optimization model according to the service coupling degree and the service contribution degree includes:
constructing a coupling contribution function according to the service coupling degree;
and constructing an objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree.
In a possible implementation manner, the constructing a coupling contribution function according to the service coupling degree includes:
according to T = ∑ (X) i ·X j ·(v i +v j )·r ij ) Constructing a coupling contribution function;
wherein T represents the degree of coupling contribution, X i Indicating the investment status, X, of the ith grid service j Indicating the investment status, v, of the jth grid service i Represents the service contribution degree v of the ith power grid service j Represents the service contribution degree r of the jth power grid service ij The comprehensive evaluation value represents the service coupling degree between the ith power grid service and the jth power grid service;
the constructing of the objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree includes:
according to f = ∑ (v) i ·X i ) + T, constructing an objective function of the power grid service optimization model;
wherein f represents the contribution degree of the service combination.
In a possible implementation manner, calculating the service contribution degree of each grid service according to the quantized value of the service contribution index includes:
according to
Respectively calculating the service contribution degree of each power grid service;
wherein v is i Represents the service contribution degree, y, of the ith power grid service ih Representing the quantitative value of the h-th service contribution index in the i-th grid service, B h A combining weight indicating the h-th traffic contribution index, and z indicating the number of traffic contribution indexes.
In a second aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the steps of the method according to the first aspect or any possible implementation manner of the first aspect.
The embodiment of the invention provides a method for establishing a power grid service optimization model and electronic equipment, wherein the weight of each power grid service coupling index is obtained by constructing the power grid service coupling index and weighting each power grid service coupling index; respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index; obtaining a quantized value of the service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantized value of the service contribution index; constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; the power grid service optimization model is used for optimizing and combining all power grid services to obtain an optimal service combination, so that in the process of optimizing the power grid service combination, the service coupling degree among different power grid services and the service contribution degree of each power grid service can be fully considered, and the power grid service combination is optimized from the two aspects so as to really realize the optimization of the power grid service combination.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described 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 illustrating an implementation of a method for establishing a power grid service optimization model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an index system of a grid service coupling index provided in an embodiment of the present invention;
fig. 3 is a flowchart of implementing the weighting of each power grid service coupling indicator by using an analytic hierarchy process according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the scoring criteria of the 1-9 ratio scale provided by the embodiments of the present invention;
FIG. 5 is a table of scoring criteria for the 1-9 ratio scale provided by an embodiment of the present invention;
fig. 6 is a flowchart of an implementation of calculating a service coupling degree between every two grid services according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of an index system of a service contribution index provided by an embodiment of the present invention;
FIG. 8 is a flowchart illustrating an implementation of weighting contribution indicators of various services according to the AHP-entropy weight method according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an apparatus for building a power grid service optimization model according to an embodiment of the present invention;
fig. 10 is a schematic diagram of an electronic device provided in 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.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 is a flowchart of an implementation of a method for establishing a power grid service optimization model according to an embodiment of the present invention, which is detailed as follows:
Coupling is a phenomenon in which two or more systems or motion modes are mutually influenced or united through various interactions, and is a dynamic association relationship among subsystems that are dependent on, coordinated with, and promoted by each other. Multiple links and multiple aspects of coupling action also exist among different power grid services. Based on the above, the embodiment of the invention provides a power grid service coupling index for quantitatively evaluating the coupling effect between different power grid services.
Referring to fig. 2, the coupling effect between the grid services is mainly embodied in two aspects of the service development process and the service development result. Correspondingly, the grid service coupling indexes are mainly divided into process coupling and result coupling. In the service development process, various resources called by the service development process are divided, so that the coupling effect of various services can be decoupled from different resource angles, and the coupling degree among different power grid services can be effectively quantized. Based on the method, in the service development process, a production resource coupling degree index, a technical resource coupling degree index, a marketing resource coupling degree index and a management resource coupling degree index are constructed, and four power grid service coupling indexes are calculated.
After the development of a service is completed, the influence of the development effect of one service on another service needs to be quantified. The influence effect mainly comprises three types of adoptive property, complementary property and alternative property. The connection among the services is the succession and supplement of one service to the original service in the range and the field of the original service, such as the connection of the electric power data value-added service to the traditional data acquisition service; the complementation between services means that the two services complement and develop in cooperation in the service field and content, and the effect of 1+1 >; the substitution among the services means that contradiction and competition relationship exist among the services, and as the society develops continuously, one service is gradually substituted by another service. Based on the method, after the service development is completed, a service continuity index, a service complementarity index and a service substitution index are constructed, and the total three power grid service coupling indexes are calculated.
When the power grid service coupling indexes are weighted, an Analytic Hierarchy Process (AHP) may be adopted. Specifically, referring to fig. 3, a hierarchical analysis model is constructed for each grid service coupling index. When a chromatographic analysis model is constructed, the service coupling degree can be used as a decision target layer, the process coupling and the result coupling are respectively used as decision criterion layers, and the production resource coupling degree index, the technical resource coupling degree index, the marketing resource coupling degree index, the management resource coupling degree index, the service support degree index, the service complementation degree index and the service substitution degree index are used as evaluation index layers.
And judging the indexes of each layer in the analytic analysis model by using a 1-9 ratio scaling method, thereby determining a judgment matrix corresponding to the hierarchical analytic model. Referring to fig. 4 and 5, when the indices of each layer in the analytical model are evaluated by the 1-9 ratio scaling method, the indices are quantified according to the degree of importance of each index according to the evaluation criteria shown in fig. 4 and 5, thereby obtaining a judgment matrix. And judging the eigenvector corresponding to the maximum eigenvalue of the matrix as the weight vector, normalizing the weight vector to obtain the weight of each layer of indexes, calculating the final weight of the indexes of the layer according to the weight of the indexes of the previous layer, and finally obtaining the final weight of each power grid service coupling index in the evaluation index layer.
In order to avoid the self-contradictory evaluation result of the judgment matrix, consistency check is required to be carried out on the judgment matrix. When the consistency check is carried out, the method can be based on
To check for consistency.
Wherein, CR represents the random consistency ratio, RI represents the average random consistency index, the value of RI is only related to the order of the judgment matrix and can be obtained by table look-up, CI represents the consistency index, and
wherein λ is max The maximum characteristic root of the judgment matrix is shown, and A represents the order of the judgment matrix.
In general, the smaller CR, the better the judgment matrix consistency. When CR is smaller than the first preset value, the matrix is judged to have acceptable satisfactory consistency, otherwise, the matrix should be adjusted and corrected. Illustratively, the first preset value may be 0.1.
Referring to table 1, table 1 shows the correspondence between the numerical value of RI and only the rank a of the decision matrix.
TABLE 1
| A | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 |
According to the table 1 and the rank of the judgment matrix, the value of RI can be determined, and then consistency check is performed. And finally, obtaining the weight of each power grid service coupling index.
And 102, respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index.
Optionally, referring to fig. 6, step 102 may include:
optionally, step 201 may include:
according to
Calculating a first fuzzy measure;
wherein λ is 1 Representing a first blur measure, g (I) u ) Represents the u-th grid service coupling index I u N represents the number of grid service coupling indicators.
The AHP method belongs to a subjective weighting method, and the weight of each power grid service coupling index obtained based on the AHP method has subjectivity and may have deviation. Therefore, the embodiment of the invention is used for correcting the weight of each power grid service coupling index by calculating the first fuzzy measure, and the accuracy of the single evaluation value of the subsequent calculation service coupling degree is improved.
There is a service coupling between every two grid services. Therefore, the service coupling degree between every two grid services needs to be calculated in sequence. That is, there is a service coupling between every two grid services.
When calculating the service coupling degree, for a group of services (including two grid services), a preset evaluation value of each grid service coupling index in the group of services needs to be obtained first. It can be understood that, in order to improve the evaluation accuracy, each grid service coupling index corresponds to at least one preset evaluation value. And correspondingly calculating to obtain a single evaluation value of the service coupling degrees of the group of services according to a preset evaluation value and the first fuzzy measure.
Optionally, respectively calculating a single evaluation value of service coupling degrees between two power grid services according to the preset evaluation value and the first fuzzy measure, where the single evaluation value includes:
according to
Respectively calculating a single evaluation value of service coupling degree between two power grid services;
wherein the content of the first and second substances,
a single evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value,
a kth predetermined evaluation value, g (I), representing the u-th grid service coupling indicator between two grid services u ) Represents the u-th grid service coupling index I between two grid services u Weight of (a), λ 1 Representing a first measure of blur, g (I) u+1 ) Represents the u +1 th power grid service coupling index I between two power grid services u+1 N represents the number of grid service coupling indicators.
According to the formula, it can be understood that the number of the single evaluation values of the service coupling degree is equal to the number of the preset evaluation values corresponding to each grid service coupling index.
And 203, calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation.
Optionally, before step 203, the method further includes:
Optionally, according to
Calculating a second blur measure;
wherein λ is 2 Representing a second measure of blur, g (q) k ) Indicates the k-th preset evaluation value q k M represents the number of preset evaluation values.
Accordingly, step 203 may comprise:
and calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values and the second fuzzy measure obtained by calculation.
Optionally, step 203 may include:
according to
And calculating a comprehensive evaluation value of the service coupling degree between the two power grid services.
Wherein r is ij A comprehensive evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service,
a single evaluation value g (q) representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value k ) Indicates the k-th preset evaluation value q k Weight of (a), λ 2 Representing a second measure of blur, g (q) k+1 ) Represents the k +1 th preset evaluation value q k+1 M represents the number of preset evaluation values.
By setting a plurality of preset evaluation values for each power grid service coupling index and setting a weight for each preset evaluation value, the accuracy of the comprehensive evaluation value of the service coupling degree can be effectively improved. Correspondingly, the embodiment of the invention is used for correcting the weight of each preset evaluation value by calculating the second fuzzy measure so as to improve the precision of the comprehensive evaluation value of the service coupling degree.
And 103, acquiring the quantitative value of the service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantitative value of the service contribution index.
Optionally, the calculating the service contribution degree of each grid service according to the quantized value of the service contribution index includes:
according to
Respectively calculating the service contribution degree of each power grid service;
wherein v is i Represents the service contribution degree, y, of the ith power grid service ih Representing the quantitative value of the h-th service contribution index in the i-th grid service, B h A combination weight representing the h-th traffic contribution index, and z representing the number of traffic contribution indexes.
Optionally, referring to fig. 7, the service contribution index of the grid service may be based on social development, benefit incentive and green dimension of electric energy, and includes: the employment ability index, unit GDP energy consumption index, new technology utilization index, net profit rate index, net present value index, total asset improvement rate index, energy saving and emission reduction benefit index, carbon emission intensity index and fossil energy variation index are driven to total 9 service contribution indexes.
For the index of driving employment ability, can be based on
A quantized value of the index is calculated.
Wherein, the first and the second end of the pipe are connected with each other,
indicates the increased employment population eta of the j-th year j The number of newly increased employment people for the j-th unit investment, I j For the total investment of the project in the j-th year,
for the employment benefit of the j-th year,
the income can be controlled for the people in the j year.
For unit GDP energy consumption index, can be according to
A quantized value of the index is calculated.
Wherein E is g In units of GDP energy consumption level, Q E The GDP is the annual energy consumption and the annual domestic production total value.
The index of new technical utilization rate can be based on
A quantized value of the index is calculated.
Wherein, E CY For the benefit of new technology application, C is the influence coefficient of power generation and supply industry,
for the engineering dosage of the new material of the d-th type,
for the unit price of the new material of the d-th,
for the engineering usage of the first new plant,
the unit price of the new equipment in the first category, D the number of new materials, and L the number of new equipment.
For net profit margin index, can be based on
A quantized value of the index is calculated.
Wherein eta is net profit margin, B p To total profit, B x It is the tax rate obtained for the total income
For the net present value index, can be based on
A quantized value of the index is calculated.
Wherein, C t,c Denotes the net present value, B p Indicating total profit, IRR internal rate of return, T current age, T total age.
For the total asset improvement rate index, it can be based on
A quantized value of the index is calculated.
Wherein, I n For total asset improvement rate, I y Increase amount for total assets of the year I a Is the sum of the assets at the beginning of the year.
Aiming at the benefit indexes of energy conservation and emission reduction, the method can be based on
A quantized value of the index is calculated.
Wherein the content of the first and second substances,
is CO 2 Reduced emission of CO produced by burning standard coal per ton 2 The amount is 2.769 tons, and Δ G is the energy consumption variation.
For the carbon emission intensity index, can be according to
A quantized value of the index is calculated.
Wherein, C i Carbon emission in unit GDP, E p GDP is the total value of domestic production for carbon dioxide emission.
Aiming at the variable index of fossil energy consumption, the variable index can be determined according to delta E hs ＝CA hs -CB hs A quantized value of the index is calculated.
Wherein, delta E hs For the variable consumption of fossil energy, CA hs Fossil energy consumption before development of business, CB hs The fossil energy consumption after the business development is realized.
Optionally, the service contribution indexes may be subjected to combined weighting through an AHP-entropy weight method, so as to obtain a combined weight of the service contribution indexes.
The AHP-entropy weight method is a method for combining subjective weight determined by the AHP method with objective weight determined by the entropy weight method to obtain combined weight of subjective and objective integration. And the key degree of each service contribution index is judged more comprehensively through the combined weight. Specifically, referring to fig. 8, on one hand, subjective weighting is performed on each service contribution index by constructing a hierarchical analysis model and a judgment matrix by using an AHP method, and on the other hand, objective weighting is performed on each service contribution index by using an entropy weighting method. The subjective weighting part of each service contribution index by using the analytic hierarchy process is described in detail in step 101, and is not described herein again. The entropy weight method is described in detail below:
when the entropy weight method is used for objectively weighting each service contribution index, an evaluation matrix formed by z service contribution indexes and p power grid services is required to be constructed.
The evaluation matrix may be expressed as: y = (Y) ih ) p×z ,i＝1,2,...,p；h＝1,2,...,z。
Wherein Y represents a scoreEstimate matrix, y ih And the quantized value of the h-th service contribution index in the ith power grid service is represented, p represents the number of the power grid services, and z represents the number of the service contribution indexes.
According to
And carrying out standardization processing on each service contribution index in the evaluation matrix to obtain a standard quantized value of the service contribution index.
Wherein, E ih And the standard quantized value of the h-th service contribution index in the i-th power grid service is represented.
Further according to
And respectively calculating the entropy value of each service contribution index. In the formula, when E ih When =0, let E ih lnE ih ＝0。
Wherein H (H) represents the entropy value of the H-th service contribution index.
According to
And respectively calculating the entropy weight of each service contribution index.
Wherein, w h "indicates the entropy weight of the h-th service contribution index.
Further, combining the weight of each service contribution index obtained by the AHP method, and finally obtaining the combined weight of each service contribution index.
According to
And calculating to obtain the combined weight of each service contribution index.
Wherein, B h Combined weight, w 'representing the h-th traffic contribution indicator' h A subjective weight representing the h-th service contribution index, the subjective weight being determined by an AHP method, w' h ' denotes the entropy weight of the h-th service contribution index.
And 104, constructing a power grid service optimization model according to the service coupling degree and the service contribution degree. And the power grid service optimization model is used for carrying out optimization combination on each power grid service so as to obtain the optimal service combination.
Optionally, a power grid service optimization model is constructed according to the service coupling degree and the service contribution degree, and the method includes:
and constructing a coupling contribution function according to the service coupling degree.
And constructing an objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree.
Optionally, constructing a coupling contribution function according to the service coupling degree includes:
according to T = ∑ (X) i ·X j ·(v i +v j )·r ij ) A coupling contribution function is constructed.
Wherein T represents the degree of coupling contribution, X i Indicating the investment status, X, of the ith grid service j Indicating the investment status, v, of the jth grid service i Representing the service contribution degree, v, of the ith grid service j Represents the service contribution degree r of the jth power grid service ij And the comprehensive evaluation value represents the service coupling degree between the ith power grid service and the jth power grid service.
Optionally, constructing an objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree, where the objective function includes:
according to f = ∑ (v) i ·X i ) And + T, constructing an objective function of the power grid service optimization model. Wherein f represents the service combination contribution degree.
Optionally, the power grid service optimization model further includes: a constraint condition.
The total investment amount after the investment of the business combination should not exceed the investment capacity of the power grid enterprise. Based on the method, business investment amount constraint is established:
wherein N is i Representing the amount of investment, X, of the ith grid service i The investment state of the ith power grid service is represented, N represents the investment capacity of a power grid enterprise, and p represents the number of the power grid services.
Power grid serviceAnd (4) during investment, the economic benefit of the investment of the service needs to be considered, and if the income input-output ratio of the power grid service is particularly low, the investment is not needed. Based on the method, establishing service marginal benefit constraint:
wherein, MR i Marginal benefit for ith grid service, v i Service contribution, M, for the ith grid service i For the service investment of the ith grid service, MR e With minimal marginal benefit for the service.
With X i Indicating the investment status of the ith grid service, and using X when investing the ith grid service i =1 represents, whereas X i And =0. When the dependency relationship exists between the two power grid services, establishing power grid service relationship constraint:
if the two grid services are interdependent, that is, only if the ith grid service is selected, the jth grid service is possibly selected; on the contrary, if the ith grid service is not selected, the jth grid service is also not possible to be selected, and then
If a complementary relationship exists between the two grid services, namely: if the two grid services have to be selected simultaneously or are not selected simultaneously, then
And according to the objective function and the constraint condition, a complete power grid service optimization model can be obtained. And performing optimization solution on the power grid business optimization model to obtain an optimal power grid business combination for guiding a power grid enterprise to develop business.
The specific model solving mode in the embodiment of the invention is not particularly limited, and the user can select the model solving mode by himself. Illustratively, the power grid service optimization model may be solved in a linear programming manner to obtain an optimal power grid service combination.
The embodiment of the invention obtains the weight of each power grid service coupling index by constructing the power grid service coupling index and weighting each power grid service coupling index; respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index; obtaining a quantitative value of the service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantitative value of the service contribution index; constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; the power grid service optimization model is used for optimizing and combining all power grid services to obtain an optimal service combination, so that in the process of optimizing the power grid service combination, the service coupling degree among different power grid services and the service contribution degree of each power grid service can be fully considered, and the power grid service combination is optimized from the two aspects in a comprehensive manner, so that the optimization of the power grid service combination is really realized.
And calculating the service coupling degree between every two power grid services quantitatively according to the power grid service coupling indexes. On the basis, the service contribution degree of each power grid service is combined, the service combination contribution degree of different power grid service combinations can be accurately calculated, so that the power grid service combination with the largest service contribution degree can be determined as the optimal service combination according to the service combination contribution degree, and a power grid enterprise is guided to develop services.
Further, when the service coupling degree is calculated in a quantification mode, a plurality of preset evaluation values are respectively set for each power grid service coupling index, and each power grid service coupling index can be evaluated in a quantification mode from a plurality of evaluation angles, so that a plurality of single evaluation values of the service coupling degree are obtained through corresponding calculation, and then a comprehensive evaluation value of the service coupling degree is finally determined according to the single evaluation values, and the accuracy of the comprehensive evaluation value of the service coupling degree is improved; in addition, the embodiment of the method also corrects the weight of each power grid service coupling index and the weight of each preset evaluation value by calculating the first fuzzy measure and the second fuzzy measure, so as to further improve the calculation accuracy of the service coupling.
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 following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 9 is a schematic structural diagram of a device for building a power grid service optimization model according to an embodiment of the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown, which are detailed as follows:
as shown in fig. 9, the device 9 for establishing a grid service optimization model includes: an empowerment module 91, a calculation module 92 and a modeling module 93.
And the weighting module 91 is configured to construct a power grid service coupling index, and perform weighting on each power grid service coupling index to obtain a weight of each power grid service coupling index.
And the calculating module 92 is configured to calculate a service coupling degree between every two power grid services according to the weight of each power grid service coupling index.
The calculating module 92 is further configured to obtain a quantized value of the service contribution index, and calculate the service contribution degree of each grid service according to the quantized value of the service contribution index.
And the modeling module 93 is used for constructing a power grid service optimization model according to the service coupling degree and the service contribution degree. And the power grid service optimization model is used for carrying out optimization combination on each power grid service so as to obtain the optimal service combination.
In a possible implementation manner, the calculating module 92 is configured to calculate the first fuzzy measure according to a weight of each grid service coupling indicator.
The calculating module 92 is further configured to, for each two power grid services, respectively obtain a preset evaluation value of each power grid service coupling index between the two power grid services, and respectively calculate a single evaluation value of the service coupling degree between the two power grid services according to the preset evaluation value and the first fuzzy measure.
The calculating module 92 is further configured to calculate a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the calculated single evaluation values.
In a possible implementation manner, the calculating module 92 is configured to obtain a weight of the preset evaluation value, and calculate a second fuzzy measure according to the weight of the preset evaluation value;
the calculating module 92 is further configured to calculate a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation and the second fuzzy measure.
In one possible implementation, the calculation module 92 is configured to calculate
Calculating a first fuzzy measure;
wherein λ is 1 Representing a first measure of blur, g (I) u ) Represents the u-th power grid service coupling index I u N represents the number of grid service coupling indicators.
In one possible implementation, the calculation module 92 is configured to calculate
Respectively calculating a single evaluation value of service coupling degree between two power grid services;
wherein, the first and the second end of the pipe are connected with each other,
a single evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value,
a kth predetermined evaluation value, g (I), representing the u-th grid service coupling indicator between two grid services u ) Representing the u-th grid service coupling index I between two grid services u Weight of (a), λ 1 Representing a first blur measure, g (I) u+1 ) Represents the u +1 th power grid service coupling index I between two power grid services u+1 N represents the number of grid service coupling indicators.
In one possible implementationA calculation module 92 for calculating a value based on
Calculating a comprehensive evaluation value of the service coupling degree between two power grid services;
wherein r is ij A comprehensive evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service,
a single evaluation value g (q) representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value k ) Represents the kth preset evaluation value q k Weight of (a), λ 2 Representing a second measure of blur, g (q) k+1 ) Represents the k +1 th preset evaluation value q k+1 M represents the number of preset evaluation values.
In a possible implementation manner, the modeling module 93 is configured to construct a coupling contribution function according to the service coupling degree.
The modeling module 93 is further configured to construct an objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree.
In one possible implementation, the modeling module 93 is configured to model a signal according to T = ∑ (X) i ·X j ·(v i +v j )·r ij ) Constructing a coupling contribution function;
wherein T represents a coupling contribution degree, X i Indicating the investment status, X, of the ith grid service j Indicating the investment status, v, of the jth grid service i Representing the service contribution degree, v, of the ith grid service j Represents the service contribution degree r of the jth power grid service ij The comprehensive evaluation value represents the service coupling degree between the ith power grid service and the jth power grid service;
a modeling module 93 for modeling according to f = ∑ (v) i ·X i ) + T, constructing an objective function of the power grid service optimization model;
wherein f represents the service combination contribution degree.
In one possible implementationIn one embodiment, the calculation module 92 is used for calculating
Respectively calculating the service contribution degree of each power grid service;
wherein v is i Represents the service contribution degree, y, of the ith power grid service ih Representing the quantitative value of the h-th service contribution index in the i-th grid service, B h A combination weight representing the h-th traffic contribution index, and z representing the number of traffic contribution indexes.
The embodiment of the invention is used for constructing the power grid service coupling indexes and weighting the power grid service coupling indexes through the weighting module 91 to obtain the weight of each power grid service coupling index; the calculating module 92 is configured to calculate a service coupling degree between each two power grid services according to the weight of each power grid service coupling index; the calculating module 92 is further configured to obtain a quantized value of the service contribution index, and calculate a service contribution degree of each grid service according to the quantized value of the service contribution index; the modeling module 93 is used for constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; the power grid service optimization model is used for optimizing and combining all power grid services to obtain an optimal service combination, so that in the process of optimizing the power grid service combination, the service coupling degree among different power grid services and the service contribution degree of each power grid service can be fully considered, and the power grid service combination is optimized from the two aspects in a comprehensive manner, so that the optimization of the power grid service combination is really realized.
And calculating the service coupling degree between every two power grid services quantitatively according to the power grid service coupling indexes. On the basis, the service contribution degree of each power grid service is combined, the service combination contribution degree of different power grid service combinations can be accurately calculated, so that the power grid service combination with the largest service contribution degree can be determined as the optimal service combination according to the service combination contribution degree, and a power grid enterprise is guided to develop services.
Furthermore, when the service coupling degree is calculated in a quantitative mode, a plurality of preset evaluation values are respectively set for each power grid service coupling index, and each power grid service coupling index can be evaluated in a quantitative mode from a plurality of evaluation angles, so that a plurality of single evaluation values of the service coupling degree are obtained through corresponding calculation, and then a comprehensive evaluation value of the service coupling degree is finally determined according to the plurality of single evaluation values, and the accuracy of the comprehensive evaluation value of the service coupling degree is improved; in addition, the embodiment of the method also corrects the weight of each power grid service coupling index and the weight of each preset evaluation value by calculating the first fuzzy measure and the second fuzzy measure, so as to further improve the calculation accuracy of the service coupling.
Fig. 10 is a schematic diagram of an electronic device provided in an embodiment of the present invention. As shown in fig. 10, the electronic apparatus 10 of this embodiment includes: a processor 1000, a memory 1001 and a computer program 1002 stored in said memory 1001 and executable on said processor 1000. The processor 1000, when executing the computer program 1002, implements the steps in the above-described method embodiments for establishing a grid service optimization model, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 1000, when executing the computer program 1002, implements the functions of the modules/units in the device embodiments, such as the modules 91 to 93 shown in fig. 9.
Illustratively, the computer program 1002 may be partitioned into one or more modules/units that are stored in the memory 1001 and executed by the processor 1000 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 1002 in the electronic device 100. For example, the computer program 1002 may be divided into the modules 91 to 93 shown in fig. 9.
Electronic device the electronic device 10 may include, but is not limited to, a processor 1000, a memory 1001. Those skilled in the art will appreciate that fig. 10 is merely an example of an electronic device 10 and does not constitute a limitation of the electronic device 10 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 1000 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 1001 may be an internal storage unit of the electronic device 10, such as a hard disk or a memory of the electronic device 10. The memory 1001 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 10. Further, the memory 1001 may also include both an internal storage unit and an external storage device of the electronic device 10. The memory 1001 is used for storing the computer program and other programs and data required by the electronic device. The memory 1001 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should 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 depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. A method for establishing a power grid service optimization model is characterized by comprising the following steps:
constructing power grid service coupling indexes, and weighting each power grid service coupling index to obtain the weight of each power grid service coupling index;
respectively calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index;
obtaining a quantitative value of a service contribution index, and respectively calculating the service contribution degree of each power grid service according to the quantitative value of the service contribution index;
constructing a power grid service optimization model according to the service coupling degree and the service contribution degree; and the power grid service optimization model is used for carrying out optimization combination on each power grid service so as to obtain the optimal service combination.
2. The method for establishing the power grid service optimization model according to claim 1, wherein the step of calculating the service coupling degree between every two power grid services according to the weight of each power grid service coupling index comprises:
calculating a first fuzzy measure according to the weight of each power grid service coupling index;
aiming at every two power grid services, respectively acquiring a preset evaluation value of each power grid service coupling index between the two power grid services, and respectively calculating a single evaluation value of the service coupling degree between the two power grid services according to the preset evaluation value and the first fuzzy measure;
and calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation.
3. The method for building a grid service optimization model according to claim 2, wherein before the calculating a comprehensive evaluation value of service coupling between two grid services according to all the calculated single evaluation values, the method further comprises:
acquiring the weight of the preset evaluation value, and calculating a second fuzzy measure according to the weight of the preset evaluation value;
the calculating of the comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation comprises the following steps:
and calculating a comprehensive evaluation value of the service coupling degree between the two power grid services according to all the single evaluation values obtained by calculation and the second fuzzy measure.
4. The method for establishing the power grid service optimization model according to claim 2, wherein the calculating the first fuzzy measure according to the weight of each power grid service coupling index comprises:
according to
Calculating a first fuzzy measure;
wherein λ is 1 Representing a first measure of blur, g (I) u ) Represents the u-th power grid service coupling index I u N represents the number of grid service coupling indicators.
5. The method for establishing the power grid service optimization model according to claim 2, wherein calculating a single evaluation value of a service coupling degree between two power grid services according to the preset evaluation value and the first fuzzy measure respectively comprises:
according to
Respectively calculating a single evaluation value of service coupling degree between two power grid services;
wherein, the first and the second end of the pipe are connected with each other,
a single evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value,
representing two power grid industriesKth preset evaluation value, g (I), of u-th grid service coupling index of service room u ) Representing the u-th grid service coupling index I between two grid services u Weight of (a), λ 1 Representing a first blur measure, g (I) u+1 ) Represents the u +1 th power grid service coupling index I between two power grid services u+1 N represents the number of grid service coupling indicators.
6. The method for establishing the power grid service optimization model according to claim 3, wherein the calculating a comprehensive evaluation value of the service coupling degree between two power grid services according to all the single evaluation values obtained by calculation and the second fuzzy measure comprises:
according to
Calculating a comprehensive evaluation value of the service coupling degree between two power grid services;
wherein r is ij A comprehensive evaluation value representing the service coupling degree between the ith power grid service and the jth power grid service,
a single evaluation value g (q) representing the service coupling degree between the ith power grid service and the jth power grid service calculated according to the kth preset evaluation value k ) Indicates the k-th preset evaluation value q k Weight of (a), λ 2 Representing a second measure of blur, g (q) k+1 ) Represents the k +1 th preset evaluation value q k+1 M represents the number of preset evaluation values.
7. The method for establishing the power grid service optimization model according to claim 1, wherein the constructing the power grid service optimization model according to the service coupling degree and the service contribution degree comprises:
constructing a coupling contribution function according to the service coupling degree;
and constructing an objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree.
8. The method for building the power grid service optimization model according to claim 7, wherein the building a coupling contribution function according to the service coupling degree comprises:
according to T = ∑ (X) i ·X j ·(v i +v j )·r ij ) Constructing a coupling contribution function;
wherein T represents the degree of coupling contribution, X i Indicating the investment status, X, of the ith grid service j Indicating the investment status, v, of the jth grid service i Representing the service contribution degree, v, of the ith grid service j Represents the service contribution degree r of the jth power grid service ij The comprehensive evaluation value represents the service coupling degree between the ith power grid service and the jth power grid service;
the constructing of the objective function of the power grid service optimization model according to the coupling contribution function and the service contribution degree includes:
according to f = ∑ (v) i ·X i ) + T, constructing an objective function of the power grid service optimization model;
wherein f represents the contribution degree of the service combination.
9. The method for establishing the power grid service optimization model according to claim 1, wherein the step of calculating the service contribution degree of each power grid service according to the quantized value of the service contribution index comprises:
according to
Respectively calculating the service contribution degree of each power grid service;
wherein v is i Represents the service contribution degree, y, of the ith power grid service ih Representing the quantitative value of the h-th service contribution index in the i-th grid service, B h A combination weight representing the h-th traffic contribution index, and z representing the number of traffic contribution indexes.
10. An electronic device comprising a memory for storing a computer program and a processor for calling and running the computer program stored in the memory, wherein the processor when executing the computer program implements the steps of the method for building a grid service optimization model as claimed in any one of claims 1 to 9.
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