CN115829144B - Method for establishing power grid business optimization model and electronic equipment - Google Patents
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Abstract
The invention provides a method for establishing a power grid business optimization model and electronic equipment. The method comprises the following steps: constructing power grid business coupling indexes, and weighting each power grid business coupling index to obtain the weight of each power grid business coupling index; according to the weight of each power grid service coupling index, calculating the service coupling degree between every two power grid services respectively; acquiring 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 carrying out optimization combination on each power grid service so as to obtain an optimal service combination. The invention can effectively realize the power grid business combination optimization.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a method for establishing a power grid business optimization model and electronic equipment.
Background
The existing power grid enterprises are not limited to the traditional power transmission and distribution business any more, and the development direction needs to be transferred to the emerging business such as intelligent energy comprehensive systems, multi-station fusion, virtual power plants and the like.
When developing the emerging business, because the emerging business is various and complex in relation, various coupling relations are necessarily present between the emerging business and the traditional business. The traditional power grid service optimization method only carries out quantitative superposition on the contribution degree of each service through multidimensional indexes to realize the combination optimization among the services, does not consider the coupling relation and influence among the services, and is not suitable for the existing emerging services.
Therefore, a method for optimizing the power grid business considering the coupling relationship between businesses is needed to be explored so as to guide the business development of the 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, which are used for solving the problem that the traditional power grid service optimization method cannot effectively realize power grid service combination optimization because the coupling relation among services is not considered.
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 business coupling indexes, and weighting each power grid business coupling index to obtain the weight of each power grid business coupling index;
according to the weight of each power grid service coupling index, calculating the service coupling degree between every two power grid services respectively;
Acquiring 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 business optimization model is used for carrying out optimization combination on all power grid businesses so as to obtain optimal business combination.
In one possible implementation manner, the calculating the service coupling degree between each two grid services according to the weight of each grid service coupling index includes:
calculating a first fuzzy measure according to the weight of each power grid service coupling index;
for every two power grid services, respectively acquiring preset evaluation values of each power grid service coupling index between the two power grid services, and respectively calculating single evaluation values of the service coupling degree between the two power grid services according to the preset evaluation values and the first fuzzy measure;
and calculating the comprehensive evaluation value of the service coupling degree between two power grid services according to all the single evaluation values obtained by calculation.
In one possible implementation manner, before the calculating 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, 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;
and calculating the comprehensive evaluation value of the service coupling degree between two power grid services according to all the single evaluation values obtained by calculation, wherein the comprehensive evaluation value comprises the following steps:
and calculating the comprehensive evaluation value of the service coupling degree between the two power grid services according to all the calculated single evaluation values and the second fuzzy measure.
In one possible implementation manner, the calculating the first fuzzy measure according to the weight of each grid service coupling index includes:
according to
Calculating a first fuzzy measure;
wherein lambda is 1 Represents a first measure of ambiguity, g (I u ) Representing the u-th power grid business coupling index I u N represents the number of grid service coupling indicators.
In one possible implementation manner, according to the preset evaluation value and the first fuzzy measure, calculating a single evaluation value of the service coupling degree between two power grid services respectively includes:
according to
Calculating single evaluation values of service coupling degrees between two power grid services respectively;
wherein,,
a single evaluation value representing the degree of service coupling between the ith and jth grid services calculated from the kth preset evaluation value, +. >
A kth preset evaluation value, g (I) u ) A u-th power grid service coupling index I representing the space between two power grid services u Weights, lambda 1 Represents a first measure of ambiguity, g (I u+1 ) The (u+1) th power grid service coupling index I representing between two power grid services u+1 N represents the number of grid service coupling indicators.
In one possible implementation manner, the calculating the comprehensive evaluation value of the service coupling degree between two power grid services according to all the calculated single evaluation values and the second fuzzy measure includes:
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 degree of service coupling between the ith and jth grid services,
representing according to the kth pre-runSetting a single evaluation value, g (q) k ) Represents the kth preset evaluation value q k Weights, lambda 2 Represents a second measure of ambiguity, g (q k+1 ) Represents the (k+1) th preset evaluation value q k+1 M represents the number of preset evaluation values.
In one possible implementation manner, the building a 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 business optimization model according to the coupling contribution function and the business contribution degree.
In one possible implementation manner, the constructing a coupling contribution function according to the service coupling degree includes:
according to t= Σ (X i ·X j ·(v i +v j )·r ij ) Constructing a coupling contribution function;
wherein T represents the coupling contribution degree, X i Representing the investment state, X, of the ith grid service j Representing the investment status of the j-th grid service, v i Representing the service contribution degree, v, of the ith power grid service j Representing the service contribution degree of the j-th power grid service, r ij A comprehensive evaluation value representing a degree of service coupling between an ith power grid service and a jth power grid service;
the constructing an objective function of the grid business optimization model according to the coupling contribution function and the business contribution degree comprises the following steps:
according to f= Σ (v i ·X i ) +T constructing an objective function of the grid business optimization model;
where f represents the service combination contribution.
In one possible implementation manner, according to the quantized value of the service contribution index, calculating the service contribution degree of each power grid service respectively includes:
According to
Calculating the service contribution degree of each power grid service respectively;
wherein v is i Representing the service contribution degree, y of the ith power grid service ih Quantized value representing h service contribution index in i-th power grid service, B h The combination weight of the h-th service contribution index is represented, and z represents the number of service contribution indexes.
In a second aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.
The embodiment of the invention provides a method for establishing a power grid business optimization model and electronic equipment, wherein the power grid business coupling indexes are constructed and weighted to obtain the weights of the power grid business coupling indexes; according to the weight of each power grid service coupling index, calculating the service coupling degree between every two power grid services respectively; acquiring 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; the power grid service optimization model is used for carrying out optimization combination on all power grid services to obtain optimal service combination, so that service coupling degree among different power grid services and service contribution degree of each power grid service can be fully considered in the process of optimizing the power grid service combination, and the power grid service combination is optimized by comprehensively considering the two aspects so as to truly realize the power grid service combination optimization.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a method for establishing a power grid business optimization model provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an index system of a power grid business coupling index according to an embodiment of the present invention;
FIG. 3 is a flow chart of an implementation of weighting each grid business coupling index by using an analytic hierarchy process provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of scoring criteria for the 1-9 ratio scale provided by the examples of the present invention;
FIG. 5 is a table of scoring criteria for the 1-9 ratio scale provided by the examples 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 an embodiment of the present invention;
FIG. 7 is a schematic diagram of an index system of a service contribution index according to an embodiment of the present invention;
FIG. 8 is a flow chart for implementing weighting of each service contribution index by using an AHP-entropy weighting method according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a device for establishing a power grid business optimization model according to an embodiment of the present invention;
fig. 10 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present 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.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be 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 business optimization model according to an embodiment of the present invention, which is described in detail below:
Coupling refers to the phenomenon that two or more systems or motion modes are influenced by each other through various interactions so as to be combined, and is a dynamic association relationship of interdependence, mutual coordination and mutual promotion under benign interaction among all subsystems. There are also multiple links and multiple coupling actions between 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 a service development process and a service development result. Correspondingly, the power grid business coupling indexes are mainly divided into two types of process coupling and result coupling. In the process of service development, various invoked resources are divided, so that the coupling effect of various services can be decoupled from different types of resource angles, and the coupling degree between different power grid services can be effectively quantized. Based on the above, in the process of service development, 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 totally constructed.
After the service is developed, the influence of the development effect of one service on the other service needs to be quantified. The influence here mainly includes three kinds of acceptances, complementarity and substitution. The service receiving is the inheritance and supplement of the original service in the scope and the field, such as the receiving of the power data value-added service and the traditional data acquisition service; the complementation between the services means that the two services complement each other and cooperatively develop in the service field and the content, and the effect of 1+1>2 is realized through the mutual coupling action between the services; the replacement between services means that contradiction and competition relation exists between services, and one service is gradually replaced by another service gradually along with the continuous development of society. Based on the above, after the service development is completed, a service acceptance index, a service complementation index and a service substitution index are constructed, and three power grid service coupling indexes are totally constructed.
When the grid business coupling indexes are weighted, a hierarchical analysis method (AnalyticHierarchyProcess, AHP) can be adopted. Referring specifically to fig. 3, an analytic hierarchy model is constructed for each grid business coupling index. When the 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 acceptance degree index, the service complementation degree index and the service substitution degree index are used as evaluation index layers.
And judging indexes of each layer in the chromatographic analysis model by using a 1-9 ratio scale method, so as to determine a judgment matrix corresponding to the chromatographic analysis model. Referring to fig. 4 and 5, when indices of each layer in a chromatographic analysis model are evaluated using a 1-9 ratio scale, each index is quantized according to the evaluation criteria shown in fig. 4 and 5 according to the importance level thereof, thereby obtaining a judgment matrix. And judging that the feature vector corresponding to the maximum feature value of the matrix is a weight vector, normalizing the weight vector to obtain the weight of each layer of index, calculating the final weight of the index of the layer according to the weight of the index 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 paradoxical evaluation result of the judgment matrix, consistency test is required. The consistency test can be performed according to
To verify consistency.
Wherein CR represents random consistency ratio, RI represents average random consistency index, and the value of RI is only equal to the judgment momentThe order of the array is related, and can be obtained by looking up a table, wherein CI represents a consistency index, and
wherein lambda is max Represents the maximum feature root of the judgment matrix, and A represents the order of the judgment matrix.
In general, the smaller CR, the better the judgment matrix consistency. When CR is less than the first preset value, it is indicated that the judgment matrix has acceptable satisfactory consistency, otherwise, adjustment and correction should be performed on the judgment matrix. The first preset value here may be, for example, 0.1.
Referring to table 1, the correspondence between the numerical value size of RI and the order a of the judgment matrix only is shown in table 1.
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 order of the judgment matrix, the RI value can be determined, and then consistency test is carried out. And finally obtaining the weight of each power grid service coupling index.
Alternatively, referring to fig. 6, step 102 may include:
optionally, step 201 may include:
according to
Calculating a first fuzzy measure;
wherein lambda is 1 Represents a first measure of ambiguity, g (I u ) Representing the u-th power grid business 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, so that the accuracy of a single evaluation value of the subsequent calculated service coupling degree is improved.
Service coupling exists between every two grid services. Thus, the service coupling degree between every two grid services needs to be calculated in turn. That is, there is a degree of service coupling between every two grid services.
When calculating the service coupling degree, for a group of services (including two power grid services), a preset evaluation value of each power grid service coupling index in the group of services needs to be acquired first. It can be appreciated that, to improve the evaluation accuracy, each grid service coupling index corresponds to at least one preset evaluation value. According to a preset evaluation value and the first fuzzy measure, a single evaluation value of the service coupling degree of the group of services can be correspondingly calculated.
Optionally, according to the preset evaluation value and the first fuzzy measure, calculating a single evaluation value of the service coupling degree between two power grid services respectively, including:
according to
Calculating single evaluation values of service coupling degrees between two power grid services respectively;
Wherein,,
a single evaluation value representing the degree of service coupling between the ith and jth grid services calculated from the kth preset evaluation value, +.>
A kth preset evaluation value, g (I) u ) A u-th power grid service coupling index I representing the space between two power grid services u Weights, lambda 1 Represents a first measure of ambiguity, g (I u+1 ) The (u+1) th power grid service coupling index I representing between two power grid services u+1 N represents the number of grid service coupling indicators.
According to the above 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 the comprehensive evaluation value of the service coupling degree between the two power grid services according to all the calculated single evaluation values.
Optionally, before step 203, the method further includes:
and 204, acquiring the weight of the preset evaluation value, and calculating a second fuzzy measure according to the weight of the preset evaluation value.
Alternatively, according to
Calculating a second fuzzy measure;
wherein lambda is 2 Represents a second measure of ambiguity, g (q k ) Represents the kth preset evaluation value q k M represents the number of preset evaluation values.
Accordingly, step 203 may include:
and calculating the 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.
Optionally, step 203 may include:
according to
And 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 degree of service coupling between the ith and jth grid services,
a single evaluation value, g (q k ) Represents the kth preset evaluation value q k Weights, lambda 2 Represents a second measure of ambiguity, 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 weights 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 accuracy of the comprehensive evaluation value of the service coupling degree.
And 103, obtaining quantized values of the service contribution indexes, and respectively calculating the service contribution degree of each power grid service according to the quantized values of the service contribution indexes.
Optionally, calculating the service contribution degree of each grid service according to the quantized value of the service contribution index, including:
according to
Calculating the service contribution degree of each power grid service respectively;
wherein v is i Representing the service contribution degree, y of the ith power grid service ih Quantized value representing h service contribution index in i-th power grid service, B h The combination weight of the h-th service contribution index is represented, and z represents the number of service contribution indexes.
Alternatively, referring to fig. 7, the service contribution index of the grid service may start from social development, benefit incentive and electric energy green dimensions, including: the method comprises the steps of driving employment capability indexes, unit GDP energy consumption indexes, new technology utilization rate indexes, net profit rate indexes, net present value indexes, total asset improvement rate indexes, energy saving and emission reduction benefit indexes, carbon emission intensity indexes and fossil energy variation indexes, and adding 9 service contribution indexes in total.
Aiming at the driving employment capability index, the method can be based on
A quantized value of the index is calculated.
Wherein,,
represents the total number of employment increased in the j th year, eta j Newly increasing the employment number for the j-th unit investment, I j For the j-th project total investment, +.>
For employment benefit in the j th year->
Revenue is available for everyone in the j th year.
For the unit GDP energy consumption index, the method can be based on
A quantized value of the index is calculated.
Wherein E is g Is the unit GDP energy consumption level, Q E GDP is the total annual and domestic production value for annual energy consumption.
The utilization rate index of the new technology can be based on
A quantized value of the index is calculated.
Wherein E is CY For the new technical application benefit, C is the influence coefficient of the power production and supply industry,
is the engineering amount of the d new material +.>
Price per unit for the d-th new material, +.>
For the engineering quantity of the first new installation, < >>
The unit price of the first new device is D the new material quantity, and L the new device quantity.
For the net profit margin index, can be according to
A quantized value of the index is calculated.
Wherein η is the net profit margin, B p B is the total profit x For total income it is the tax rate obtained
For the net present value index, can be according to
A quantized value of the index is calculated.
Wherein C is t,c Indicating net present value, B p Indicating total profit, IRR indicating internal rate of return, T indicating current age, T indicating total age.
For the total asset enhancement rate index, it can be based on 
A quantized value of the index is calculated.
Wherein I is n To increase the rate of total assets, I y To increase the amount of the total assets in the year, I a Is the total amount of assets in the beginning of the year.
Aiming at the energy saving and emission reduction benefit index, the method can be according to
A quantized value of the index is calculated.
Wherein,,
is CO 2 Emission reduction, CO generated by burning per ton of standard coal 2 The amount was 2.769 tons, and Δg was the amount of change in energy consumption.
For the carbon emission intensity index, can be according to
A quantized value of the index is calculated.
Wherein C is i As unit GDP carbon emissions, E p The GDP is the total value of domestic production.
Aiming at the index of the change amount of fossil energy consumption, the method can be based on delta E hs ＝CA hs -CB hs A quantized value of the index is calculated.
Wherein ΔE is hs For fossil energy consumption variation, CA hs For fossil energy consumption, CB before business development hs And the energy consumption of fossil energy after business development is realized.
Optionally, the combination weighting of each service contribution index can be obtained by carrying out combination weighting on each service contribution index through an AHP-entropy weighting method.
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 subjective and objective integrated combination weight. And the key degree of each service contribution index is judged more comprehensively through the combination weight. Specifically, referring to fig. 8, subjective weighting is performed on each service contribution index by constructing an analytic hierarchy process and a judgment matrix by using an AHP method on one hand, and objective weighting is performed on each service contribution index by using an entropy weighting method on the other hand. The subjective weighting portion for each service contribution index by using the analytic hierarchy process is described in detail in step 101, and will not be described here again. The entropy weight method is specifically described as follows:
When objective weighting is carried out on each service contribution index by utilizing an entropy weighting method, an evaluation matrix formed by z service contribution indexes and p power grid services is firstly required to be constructed.
The evaluation matrix can be expressed as: y= (Y) ih ) p×z ,i＝1,2,...,p；h＝1,2,...,z。
Wherein Y represents an evaluation matrix, Y ih And the quantized value of the h service contribution index in the ith power grid service is represented, p represents the number of power grid services, and z represents the number of 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 is ih And the standard quantized value of the h service contribution index in the ith power grid service is represented.
Further according to
And respectively calculating entropy values of the service contribution indexes. Wherein, when E ih When=0, let E ih lnE ih ＝0。
Wherein H (H) represents the entropy value of the H-th traffic contribution index.
According to
And respectively calculating the entropy weight of each service contribution index.
Wherein w is h "represents the entropy weight of the h-th service contribution index.
Further, the weights of the service contribution indexes obtained by combining the AHP method are combined, and finally the combination weights of the service contribution indexes are obtained.
According to
And calculating to obtain the combination weight of each service contribution index.
Wherein B is h Combining weight, w 'representing h business contribution index' h Subjective weight representing h business contribution index, the subjective weightThe weight is determined by AHP method, w' h ' represents the entropy weight of the h-th traffic 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 an optimal service combination.
Optionally, constructing the grid service optimization model according to the service coupling degree and the service contribution degree includes:
and constructing a coupling contribution function according to the service coupling degree.
And constructing an objective function of the power grid business optimization model according to the coupling contribution function and the business contribution degree.
Optionally, constructing the coupling contribution function according to the service coupling degree includes:
according to t= Σ (X i ·X j ·(v i +v j )·r ij ) And constructing a coupling contribution function.
Wherein T represents the coupling contribution degree, X i Representing the investment state, X, of the ith grid service j Representing the investment status of the j-th grid service, v i Representing the service contribution degree, v, of the ith power grid service j Representing the service contribution degree of the j-th power grid service, r ij And the comprehensive evaluation value is used for representing the service coupling degree between the ith power grid service and the jth power grid service.
Optionally, constructing an objective function of the grid business optimization model according to the coupling contribution function and the business contribution degree, including:
according to f= Σ (v i ·X i ) And constructing an objective function of the power grid business optimization model by +T. Where f represents the service combination contribution.
Optionally, the power grid business optimization model further includes: constraint conditions.
The total investment amount after the business combination investment should not exceed the investment capacity of the power grid enterprises. Based on this, a business investment amount constraint is established:
wherein N is i Representing the investment amount, X, of the ith grid service i The investment state of the ith power grid business is represented, N represents the investment capacity of the power grid enterprise, and p represents the number of the power grid businesses.
The economic benefit of the business needs to be considered when the power grid business invests, and if the income input-output ratio of the power grid business is particularly low, the investment is not needed. Based on this, a business marginal benefit constraint is established:
wherein MR is i V is the marginal benefit of the ith grid service i Service contribution degree M for ith power grid service i For the i-th grid business investment, MR e Is the minimum marginal benefit of the service.
By X i Representing the investment status of the ith grid service, X being used when the ith grid service is invested i =1, otherwise X i =0. When the dependency relationship exists between two power grid services, a power grid service relationship constraint is established:
if the two power grid services are interdependent, that is, only the ith power grid service is selected, the jth power grid service is likely to be selected; otherwise, if the ith power grid service is not selected, the jth power grid service is also impossible to be selected, and then there are
If there is a complementary relationship between two grid services, namely: the two grid services must be selected or not selected at the same time, and then there are
And obtaining a complete power grid business optimization model according to the objective function and the constraint condition. And carrying out 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 is not particularly limited in the embodiment of the invention, and the user can select the mode by himself. The power grid business optimization model can be solved in a linear programming mode to obtain an optimal power grid business combination.
According to the embodiment of the invention, the power grid business coupling indexes are constructed, and each power grid business coupling index is weighted to obtain the weight of each power grid business coupling index; according to the weight of each power grid service coupling index, calculating the service coupling degree between every two power grid services respectively; acquiring 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; the power grid service optimization model is used for carrying out optimization combination on all power grid services to obtain optimal service combination, so that service coupling degree among different power grid services and service contribution degree of each power grid service can be fully considered in the process of optimizing the power grid service combination, and the power grid service combination is optimized by comprehensively considering the two aspects so as to truly realize the power grid service combination optimization.
And the service coupling degree between every two power grid services can be quantitatively calculated according to the service coupling indexes of each power grid. 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 to be the optimal service combination according to the service combination contribution degree, and the power grid enterprises are guided to develop the service.
Further, when the service coupling degree is calculated in a quantized mode, a plurality of preset evaluation values are respectively set for the service coupling indexes of each power grid, and the service coupling indexes of each power grid can be evaluated in a quantized 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 the 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 method further corrects the weight of the power grid service coupling index and the weight of each preset evaluation value respectively by calculating the first fuzzy measure and the second fuzzy measure so as to further improve the calculation accuracy of the service coupling degree.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 9 is a schematic structural diagram of a device for establishing a power grid business optimization model according to an embodiment of the present invention, and for convenience of explanation, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 9, the device 9 for establishing the grid business optimization model includes: a weighting module 91, a calculation module 92 and a modeling module 93.
The weighting module 91 is configured to construct power grid service coupling indexes, and weight each power grid service coupling index to obtain a weight of each power grid service coupling index.
The calculating module 92 is configured to calculate, according to the weight of each grid service coupling index, the service coupling degree between every two grid services.
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 power grid service according to the quantized value of the service contribution index.
The modeling module 93 is configured to construct a 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 an optimal service combination.
In one possible implementation, the calculating module 92 is configured to calculate the first ambiguity measure according to the weight of each grid service coupling indicator.
The calculation module 92 is further configured to obtain, for each two power grid services, a preset evaluation value of each power grid service coupling index between the two power grid services, and calculate, according to the preset evaluation value and the first fuzzy measure, a single evaluation value of the service coupling degree between the two power grid services.
The calculation 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.
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 computing module 92 is configured to, according to
Calculating a first fuzzy measure;
wherein lambda is 1 Represents a first measure of ambiguity, g (I u ) Representing the u-th power grid business coupling index I u N represents the number of grid service coupling indicators.
In one possible implementation, the computing module 92 is configured to, according to
Calculating single evaluation values of service coupling degrees between two power grid services respectively;
wherein,,
a single evaluation value representing the degree of service coupling between the ith and jth grid services calculated from the kth preset evaluation value, +.>
Kth preset evaluation of a kth grid service coupling index representing a relationship between two grid servicesValuation, g (I u ) A u-th power grid service coupling index I representing the space between two power grid services u Weights, lambda 1 Represents a first measure of ambiguity, g (I u+1 ) The (u+1) th power grid service coupling index I representing between two power grid services u+1 N represents the number of grid service coupling indicators.
In one possible implementation, the computing module 92 is configured to, 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 degree of service coupling between the ith and jth grid services, 
A single evaluation value, g (q k ) Represents the kth preset evaluation value q k Weights, lambda 2 Represents a second measure of ambiguity, 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, 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 grid service optimization model according to the coupling contribution function and the service contribution.
In one possible implementation, the modeling module 93 is configured to calculate the value according to t= Σ (X i ·X j ·(v i +v j )·r ij ) Constructing a coupling contribution function;
wherein T represents the coupling contribution degree, X i Representing the investment state, X, of the ith grid service j Representing the investment status of the j-th grid service, v i Representing the service contribution degree, v, of the ith power grid service j Service contribution degree representing j-th power grid service，r ij A comprehensive evaluation value representing a degree of service coupling between an ith power grid service and a jth power grid service;
a modeling module 93 for modeling according to f= Σ (v i ·X i ) +T constructing an objective function of the grid business optimization model;
Where f represents the service combination contribution.
In one possible implementation, the computing module 92 is configured to, according to
Calculating the service contribution degree of each power grid service respectively;
wherein v is i Representing the service contribution degree, y of the ith power grid service ih Quantized value representing h service contribution index in i-th power grid service, B h The combination weight of the h-th service contribution index is represented, and z represents the number of service contribution indexes.
The embodiment of the invention is used for constructing power grid business coupling indexes through the weighting module 91, and weighting each power grid business coupling index to obtain the weight of each power grid business coupling index; the calculating module 92 is configured to calculate, according to the weight of each grid service coupling index, a service coupling degree between every two grid services; the calculation module 92 is further configured to obtain a quantized value of the service contribution index, and calculate a service contribution degree of each power grid service according to the quantized value of the service contribution index; the modeling module 93 is configured to construct 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 carrying out optimization combination on all power grid services to obtain optimal service combination, so that service coupling degree among different power grid services and service contribution degree of each power grid service can be fully considered in the process of optimizing the power grid service combination, and the power grid service combination is optimized by comprehensively considering the two aspects so as to truly realize the power grid service combination optimization.
And the service coupling degree between every two power grid services can be quantitatively calculated according to the service coupling indexes of each power grid. 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 to be the optimal service combination according to the service combination contribution degree, and the power grid enterprises are guided to develop the service.
Further, when the service coupling degree is calculated in a quantized mode, a plurality of preset evaluation values are respectively set for the service coupling indexes of each power grid, and the service coupling indexes of each power grid can be evaluated in a quantized 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 the 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 method further corrects the weight of the power grid service coupling index and the weight of each preset evaluation value respectively by calculating the first fuzzy measure and the second fuzzy measure so as to further improve the calculation accuracy of the service coupling degree.
Fig. 10 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 10, the electronic device 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 steps in the above embodiments of the method for establishing the grid business optimization model are implemented by the processor 1000 when executing the computer program 1002, for example, steps 101 to 104 shown in fig. 1. Alternatively, the processor 1000, when executing the computer program 1002, performs the functions of the modules/units in the above-described device embodiments, for example, the functions of 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 accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which are used to describe the execution of the computer program 1002 in the electronic device 100. For example, the computer program 1002 may be divided into 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. It will be appreciated by those skilled in the art that fig. 10 is merely an example of the electronic device 10 and is not intended to limit the electronic device 10, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 1000 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 1001 may be an internal storage unit of the electronic device 10, for example, 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, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are 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 for 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-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a 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 process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
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 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 manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (5)
1. The method for establishing the power grid business optimization model is characterized by comprising the following steps of:
constructing power grid business coupling indexes, and weighting each power grid business coupling index to obtain the weight of each power grid business coupling index;
according to the weight of each power grid service coupling index, respectively calculating the service coupling degree between every two power grid services, including: calculating a first fuzzy measure according to the weight of each power grid service coupling index; for every two power grid services, respectively acquiring preset evaluation values of each power grid service coupling index between the two power grid services, and respectively calculating single evaluation values of the service coupling degree between the two power grid services according to the preset evaluation values and the first fuzzy measure; according to all the single evaluation values obtained by calculation, calculating a comprehensive evaluation value of the service coupling degree between two power grid services;
According to the weight of each power grid service coupling index, calculating a first fuzzy measure, including: according to
Calculating a first fuzzy measure; wherein (1)>
Representing a first blur measure, +_>
Indicate->
Individual grid business coupling index->
Weight of->
Representing the number of grid business coupling indexes;
according to the preset evaluation value and the first fuzzy measure, respectively calculating a single evaluation value of the service coupling degree between two power grid services, wherein the single evaluation value comprises: according to
Calculating single evaluation values of service coupling degrees between two power grid services respectively; wherein (1)>
Representing according to->
The>
Personal grid business and->
Single evaluation value of service coupling degree between individual grid services,/->
Representing the +.o between two grid services>
The>
A preset evaluation value, ">
Representing the +.o between two grid services>
Individual grid business coupling index->
Weight of->
Representing a first blur measure, +_>
Representing the +.o between two grid services>
Individual grid business coupling index->
Weight of->
Representing the number of grid business coupling indexes
Acquiring 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;
According to the service coupling degree and the service contribution degree, constructing a power grid service optimization model, which comprises the following steps: constructing a coupling contribution function according to the service coupling degree; constructing an objective function of the power grid business optimization model according to the coupling contribution function and the business contribution degree;
the constructing a coupling contribution function according to the service coupling degree comprises the following steps: according to
Constructing a coupling contribution function; wherein (1)>
Representing the coupling contribution degree, +.>
Indicate->
Investment status of individual grid services>
Indicate->
Investment status of individual grid services>
Indicate->
Service contribution of individual grid services, +.>
Indicate->
Service contribution of individual grid services, +.>
Indicate->
Personal grid business and->
A comprehensive evaluation value of service coupling degree between individual grid services;
the constructing an objective function of the grid business optimization model according to the coupling contribution function and the business contribution degree comprises the following steps: according to
Constructing an objective function of the power grid business optimization model; wherein (1)>
Representing the contribution degree of service combination; and the power grid business optimization model is used for carrying out optimization combination on all power grid businesses so as to obtain optimal business combination.
2. The method for building a grid business optimization model according to claim 1, further comprising, before the calculating the integrated evaluation value of the business coupling degree between two grid businesses according to all the single evaluation values obtained by the calculating:
acquiring the weight of the preset evaluation value, and calculating a second fuzzy measure according to the weight of the preset evaluation value;
and calculating the comprehensive evaluation value of the service coupling degree between two power grid services according to all the single evaluation values obtained by calculation, wherein the comprehensive evaluation value comprises the following steps:
and calculating the comprehensive evaluation value of the service coupling degree between the two power grid services according to all the calculated single evaluation values and the second fuzzy measure.
3. The method for building a power grid business optimization model according to claim 2, wherein the calculating the comprehensive evaluation value of the business coupling degree between two power grid businesses according to all the calculated single evaluation values and the second fuzzy measure comprises:
according to
Calculating a comprehensive evaluation value of the service coupling degree between two power grid services;
wherein,,
indicate->
Personal grid business and->
Comprehensive evaluation value of service coupling degree between individual grid services,/- >
Representing according to->
The>
Personal grid business and->
Single evaluation value of service coupling degree between individual grid services,/->
Indicate->
Preset evaluation value->
Weight of->
Representing a second blur measure, +_>
Indicate->
Preset evaluation value->
Weight of->
Representing the number of preset evaluation values.
4. The method for building the grid business optimization model according to claim 1, wherein calculating the business contribution degree of each grid business according to the quantized value of the business contribution index comprises:
according to
Calculating the service contribution degree of each power grid service respectively;
wherein,,
indicate->
Service contribution of individual grid services, +.>
Indicate->
The>
Quantized value of individual traffic contribution index, +.>
Indicate->
Combining weights of individual service contribution indicators, +.>
Representing the number of traffic contribution indicators.
5. 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, characterized in that the processor, when executing the computer program, realizes the steps of the method for building a grid business optimization model according to any one of the preceding claims 1 to 4.
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