CN106651225A - Comprehensive evaluation method and system for demonstration engineering of smart power grid - Google Patents
Comprehensive evaluation method and system for demonstration engineering of smart power grid Download PDFInfo
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Abstract
The invention relates to a comprehensive evaluation method and a comprehensive evaluation system for a demonstration project of an intelligent power grid. And calculating to obtain a macroscopic demand index value according to the microscopic evaluation index data set and the weight of the corresponding data. And calculating the weight of each macroscopic demand index by utilizing a DEMATEL-ANP-inverse entropy weight method, and calculating according to the macroscopic demand index value and the weight corresponding to the macroscopic demand index to obtain and output a comprehensive evaluation result. The weight of each data in the microscopic evaluation index data set is calculated by using a combined weighting method based on a moment estimation theory, so that the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that the weighting method is used for determining the weight by a single weighting method is solved. The macroscopic index weight is determined by a DEMATEL-ANP-inverse entropy weight method, the microscopic index weight is determined by a combined weighting method based on a moment estimation theory, the comprehensive benefit can be scientifically evaluated, the evaluation reliability is high, and the method has an instructive effect.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a comprehensive evaluation method and system for demonstration engineering of an intelligent power grid.
Background
In recent years, the energy safety and environmental problems are increasingly emphasized, renewable energy is vigorously developed, and under the conditions of power grid informatization, automation and interactive calling, the smart power grid is highly concerned by the industry again and becomes a hot spot field for competitive development of all countries in the world. However, the development of the smart grid is a complex systematic project, and is still in a starting stage all over the world, and the construction of the smart grid involves numerous links such as power generation, power transmission, power transformation, power distribution, power utilization and scheduling, and the development of economy, society and environment in China is influenced profoundly. A set of scientific, reasonable and comprehensive evaluation index system and method needs to be constructed urgently, analysis and evaluation of the intelligent power distribution network are achieved, and guidance and help are provided for planning, construction and transformation of the intelligent power distribution network.
The traditional comprehensive benefit evaluation method for the smart power grid mainly adopts a subjective weighting method to carry out weight calculation on each index, wherein the subjective weighting method comprises an analytic hierarchy process, an expert survey method (Delphi method), a feature vector method and the like. The subjective weighting method has the defects of strong subjectivity, easy influence of human factors and lack of theoretical support. The traditional comprehensive benefit evaluation method for the smart power grid has the defect of low evaluation reliability.
Disclosure of Invention
Based on the above, it is necessary to provide a comprehensive evaluation method and system for the demonstration project of the smart grid with high evaluation reliability.
A comprehensive evaluation method for demonstration engineering of a smart power grid comprises the following steps:
receiving a microscopic evaluation index data set of the smart grid;
calculating the weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory;
calculating to obtain a macroscopic demand index value according to the microscopic evaluation index data set and the weight of the corresponding data;
calculating the weight of each macroscopic demand index by using a DEMATEL-ANP-entropy weight inversion method;
and calculating according to the macro demand index value and the weight of the corresponding macro demand index to obtain and output a comprehensive evaluation result.
A comprehensive evaluation system for demonstration engineering of a smart grid comprises:
the index data receiving module is used for receiving a microscopic evaluation index data set of the smart grid;
the microscopic weight calculation module is used for calculating the weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory;
the index value calculation module is used for calculating to obtain a macro demand index value according to the micro evaluation index data set and the weight of the corresponding data;
the macroscopic weight calculation module is used for calculating the weight of each macroscopic demand index by utilizing a DEMATEL-ANP-entropy weight inversion method;
and the comprehensive evaluation output module is used for calculating according to the macro demand index value and the weight corresponding to the macro demand index to obtain and output a comprehensive evaluation result.
According to the comprehensive evaluation method and system for the demonstration engineering of the smart power grid, the microscopic evaluation index data set of the smart power grid is received, and the weight of each data in the microscopic evaluation index data set is calculated by using a combined weighting method based on a moment estimation theory. And calculating to obtain a macroscopic demand index value according to the microscopic evaluation index data set and the weight of the corresponding data. And calculating the weight of each macroscopic demand index by utilizing a DEMATEL-ANP-inverse entropy weight method, and calculating according to the macroscopic demand index value and the weight corresponding to the macroscopic demand index to obtain and output a comprehensive evaluation result. The weight of each data in the microscopic evaluation index data set is calculated by using a combined weighting method based on a moment estimation theory, so that the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that the weighting method is used for determining the weight by a single weighting method is solved. The macroscopic index weight is determined by a DEMATEL-ANP-inverse entropy weight method, the microscopic index weight is determined by a combined weighting method based on a moment estimation theory, the comprehensive benefit can be scientifically evaluated, the evaluation reliability is high, and the method has an instructive effect.
Drawings
FIG. 1 is a flow diagram of an exemplary comprehensive project assessment method for a smart grid in one embodiment;
FIG. 2 is a schematic diagram of a technical route for engineering characteristic analysis of a smart grid in an embodiment;
FIG. 3 is a schematic diagram illustrating a process of constructing a macro demand index set of the smart grid in an embodiment;
FIG. 4 is a diagram illustrating an intelligent index set of a smart grid according to an embodiment;
FIG. 5 is a diagram of a set of smart grid efficiency indicators in an embodiment;
FIG. 6 is a diagram of an example smart grid reliability index set;
FIG. 7 is a diagram of an example embodiment of a greenery indicator set for a smart grid;
FIG. 8 is a schematic diagram of an overall benefit evaluation index system of the smart grid in an embodiment;
FIG. 9 is a schematic diagram of a combined weight-based smart grid evaluation hierarchical optimization model in an embodiment;
fig. 10 is a block diagram of an exemplary comprehensive engineering evaluation system for a smart grid in one embodiment.
Detailed Description
In one embodiment, a comprehensive evaluation method for an exemplary project of a smart grid, as shown in fig. 1, includes the following steps:
step S110: and receiving a microscopic evaluation index data set of the smart grid.
The microscopic evaluation index data set comprises various data, and each data is a microscopic evaluation index of the smart grid. The type of the microscopic evaluation index is not unique, and specifically includes a benefit type index, a cost type index and a specific type index. Wherein, the benefit index means that the score of the index increases along with the increase of the value of the index; the cost-type index means that the fraction of the index increases as the value of the index decreases; and the specific index means that the score is the highest when the value or the subinterval in the middle is taken.
Step S120: and calculating the weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory.
The weight of each data in the microscopic evaluation index data set is calculated by using a combined weighting method based on a moment estimation theory, so that the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that the weighting method is used for determining the weight by a single weighting method is solved.
Step S130: calculating to obtain a macroscopic demand index value according to the microscopic evaluation index data set and the weight of the corresponding data;
the types of data included in the microscopic evaluation index data set are not unique, and the types of macroscopic demand index values calculated according to the microscopic evaluation index data set are correspondingly different. In this embodiment, the microscopic evaluation index data set includes microscopic evaluation index data including an intelligent index data set, a high efficiency index data set, a reliability index data set, and a greening index data set; the macro demand index value comprises an intellectualization index value, an efficiency index value, a reliability index value and a greening index value.
And respectively calculating the weight of each data in the intelligent index data set, the high-efficiency index data set, the reliability index data set and the greening index data set by using a combined weighting method based on a moment estimation theory, and then calculating according to each data set and the corresponding weight to obtain the corresponding intelligent index value, high-efficiency index value, reliability index value and greening index value.
Step S140: and calculating the weight of each macroscopic demand index by utilizing a DEMATEL-ANP-entropy weight inversion method.
And determining the weight of the macro demand index by adopting an optimal combined weighting method combining DEMATEL-ANP and an entropy weight resisting method, thereby integrating the expert experience information of the index and the objective attribute of the index. Correspondingly, in this embodiment, the macro demand index includes an intelligent index, a high efficiency index, a reliability index, and a greening index. And respectively calculating the weights of the intelligent index, the high efficiency index, the reliability index and the greening index by utilizing a DEMATEL-ANP-entropy weight resisting method.
Step S150: and calculating according to the macro demand index value and the weight corresponding to the macro demand index to obtain and output a comprehensive evaluation result.
And carrying out weighted summation on the corresponding index evaluation value and the corresponding weight to obtain a comprehensive evaluation result of the smart grid demonstration project. The output mode of the comprehensive evaluation result is not exclusive, and may be output to a memory for storage or output to a display for display.
The weight of a microscopic evaluation index and the weight of a macroscopic demand index are respectively determined by a combined weighting method and a DEMATEL-ANP-entropy-resisting weight method based on a moment estimation theory, and a comprehensive benefit evaluation index system of the intelligent power grid is established so as to carry out comprehensive evaluation on demonstration engineering of the intelligent power grid. Specifically, the establishment of the comprehensive benefit evaluation index system of the smart power grid follows the future key development field and key technology of the smart power grid, and the evaluation index system of the smart power grid is divided into two levels, namely a macroscopic index set and a microscopic index set. Wherein, the macro index set mainly analyzes the actual requirements of the stakeholders; the microscopic index set is used for decomposing and converting the macroscopic indexes, and further decomposing and converting the macroscopic indexes through a two-dimensional benefit analysis mapping method to form specific, complete and mutually independent indexes.
The development and application of electric energy are epoch-making achievements obtained in the natural process of conquering by human beings. With the fact that electric energy enters various aspects of human production and life step by step, a power grid serving as an electric energy transmission platform also undergoes a development process of one-line-on-one-line, transmission distance is gradually increased from near to far, transmission capacity is gradually increased from small to large, and transmission quality, stability, coordination with the external environment and the like are gradually improved. According to the related definition of the intelligent development of the power grid, the intelligent development characteristics of the power grid are analyzed in combination with the natural attributes, the social attributes, the technical characteristics, the object characteristics and the functional characteristics of the power grid, and a power grid intelligent development characteristic analysis technology route map is shown in fig. 2.
The macro demand index is the evaluation of core values of safety, economy, social benefits and the like brought by the smart grid, the value is the embodiment of the demand, and the core value of the smart grid is the embodiment of the core demand of the power grid stakeholders. The macro demand index is essentially the measure of the degree of satisfaction of the demands of the power grid interest correlators, but the coupling and the opposition exist among the demands of the power grid interest correlators, so that the omission of the index is easily caused in the analysis, and the comprehensiveness of the index is influenced. Therefore, when an index system is constructed, the requirements of all interest correlators are comprehensively considered, and from the viewpoint of power grid operation, a required balance point is found, so that the harmonious development of the power grid is realized. The four basic attributes of intelligence, high efficiency, reliability and green of the intelligent power grid are combined with the operation characteristics of the power grid. The method is characterized in that the focus of interest of power grid interest correlators is divided according to the requirements of intellectualization, high efficiency, reliability and greening, and specifically comprises the following steps:
1) intelligentized demand: safe, economical, flexible and reliable intelligent equipment is the basis of an intelligent power grid; the high-quality electric energy quality can effectively improve the user power consumption satisfaction degree, and is one of main tasks of intelligent power grid construction; therefore, the intelligent level and the power quality of the facility are focused on, and the quantitative indexes are the intelligent degree and the power supply quality of the facility;
2) the high efficiency requirement is as follows: by means of efficient operation management, the functions of optimizing the utilization rate of power grid assets, reducing loss and saving construction cost can be achieved, so that the power grid operation efficiency is focused on, and the quantized index is operation efficiency;
3) the reliability requirement is as follows: the power supply capacity supporting self-healing is a fundamental guarantee for stable operation of a power grid, so that the power supply capacity is focused on, and the quantized index is the power supply reliability;
4) green requirements: the intelligent power grid supporting bidirectional communication and coordinated sustainable development is a necessary trend of future social development, so that the interaction capacity between the power grid and users and the coordination capacity between the power grid and new energy and environment are focused, and the quantized indexes are the interaction capacity and development coordination capacity of the power grid.
By combining the macro index set and the micro index set, a complete comprehensive evaluation index system of the smart grid can be constructed, wherein the comprehensive evaluation index system comprises 4 macro indexes, as shown in fig. 3.
Each macroscopic index can be further decomposed and converted through a two-dimensional benefit analysis mapping method, and a corresponding microscopic index set is obtained. Index decomposition: firstly, the time, space and object aimed at by the index are determined, a proper dimension is selected according to different investigation points, the index is decomposed, and meanwhile, the index boundary is also decomposed into a boundary for decomposing the object in the specific dimension. Index conversion: firstly, analyzing each influence factor of the index by methods such as business process analysis or data mining, and the like, selecting a proper process index for each influence factor to carry out quantization and evaluation, completing the conversion of the index and a corresponding index boundary, and further forming a series of indexes with causal relationship. Through decomposition of the indexes, the characteristic indexes of the smart grid are refined, and all basic components of the indexes are determined. Through index conversion, influence factors of the original indexes are converted into a series of subordinate indexes which can be analyzed quantitatively, and characteristics of the smart power grid can be evaluated conveniently. The decomposition and transformation results of the microscopic indexes under the respective macroscopic indexes are shown in FIGS. 4 to 7.
Intelligentization: (1) in order to adapt to the intelligent development of the power grid, corresponding requirements are also put forward for the construction of matched infrastructure of the power grid, namely the infrastructure can support the realization of multiple functions such as power grid two-way communication, real-time information acquisition, automatic management and the like, thereby playing a supporting role in the intelligent operation of the power grid. The method analyzes the current mature power grid intelligent facilities, namely a power grid communication foundation, a power grid automatic application and an intelligent system. (2) The intelligent final foothold of smart power grids is for carrying out intelligent control through to the electric wire netting and realizing the promotion of power supply capacity, and power supply capacity indicates the quality of supplying to user's power receiving end electric energy, and is key to the electric energy quality except that voltage deviation, along with deepening of electric power system reform, the electric energy also walks into the market as the commodity, and the goodness of its quality also more and more receives people's attention. The invention measures the quality of electric energy from the technical level and analyzes the control and regulation capacity of the voltage steady-state quality and the transient-state quality respectively.
High efficiency: (1) the improvement of the operation efficiency of the power grid depends on the popularization and application of the intelligent power distribution technology, and the functions of improving the utilization rate of the assets of the power grid, prolonging the service life of equipment, optimizing the investment of the power grid, reducing the enterprise cost and the like can be achieved. The invention analyzes from three aspects of system capacity utilization level, operation management level and equipment utilization level. (2) The power grid interactivity refers to transaction interaction between a power grid and a user, namely the instant exchange capacity of power information between the power grid and the user, and power utilization behaviors such as peak avoidance and valley arrival of a load are realized through user demand response, so that the interactivity and the service quality of the power grid are improved, and finally, high-efficiency operation of related services of the power grid is realized. The invention analyzes the power utilization information interaction capacity and the demand side response level.
Reliability: the reliability of the traditional power grid or the future smart power grid is the root of the power grid construction and development and is an important guarantee for realizing other requirements of the power grid. The method analyzes the network structure level, the power supply standby condition, the load supply level, the fault self-healing capability, the disaster resistance capability and the like of the power grid.
Greening: under the background that the problems of energy shortage, environmental protection, climate change and the like are increasingly prominent, governments of various countries begin to implement a sustainable development strategy, and harmony and friendliness between human beings and the environment are taken as the future focus. In the face of many challenges, the power grid is required to have stronger development coordination and adaptability. The method analyzes the adaptive development of the power grid, the coordinated development of the power grid and new energy and the harmonious development of the power grid and the environment.
The macro index set mainly analyzes the actual requirements of the stakeholders; the microscopic index set is used for decomposing and converting the macroscopic indexes, the macroscopic indexes are further decomposed and converted through a two-dimensional benefit analysis mapping method, the formed indexes are more specific and complete and are mutually independent, and no coupling dependency relationship exists.
The macroscopic demand index set and the microscopic evaluation index set are combined together to form a complete intelligent power grid multi-level evaluation index system, wherein the system comprises 4 intelligent, efficient, reliable and green macroscopic demand indexes, each demand index corresponds to 1 microscopic evaluation index set, and each specific index is shown in table 1. The index system comprises the aspects of equipment intellectualization, information acquisition and processing, fault self-healing level, net rack strength, power supply reliability, clean energy access, environmental protection, energy conservation, emission reduction and the like, and fully embodies the development characteristics of the smart power grid.
TABLE 1
That is, in this embodiment, the data of the intelligent index data set includes a substation communication fiber ratio, a power fiber-to-the-home ratio, a substation fiber communication error rate, a distribution terminal on-line ratio, a distribution terminal communication fiber ratio, an information system integration degree, a feeder automation terminal coverage ratio, a station monitoring terminal coverage ratio, a substation integrated automation ratio, a microgrid EMS (energy management system) coverage ratio, an intermittent energy field station scheduling system coverage ratio, a distribution network automation system coverage ratio, a power grid monitorable electric vehicle charging and converting station occupation ratio, a main grid operation risk early warning function, a main grid operation risk defense control function, a large-scale intermittent energy consumption control function, a microgrid control level, an ordered charging guidance function, an intermittent energy power station active control ratio, an intermittent energy power station reactive power control ratio, a voltage reactive integrated automatic adjusting device ratio, a substation communication error rate, a distribution terminal on-line ratio, a distribution terminal communication fiber ratio, a, The method comprises the following steps of static reactive power compensation device proportion, dynamic voltage restorer installation proportion, fault current limiter installation proportion, transformer substation lightning arrester installation proportion and line lightning arrester installation proportion.
The data of the high-efficiency index data set comprise a variable capacitance load ratio, a medium-voltage line load rate, a transformer substation intelligent inspection proportion, a line integrity rate, a main equipment integrity rate, an electric vehicle charging station annual load rate, an electric vehicle battery changing station annual load rate, a high-voltage line availability factor, a main transformer availability factor, a circuit breaker availability factor of 110kV or more, an electric vehicle battery changing station monitoring rate, an intelligent electric meter installation rate, an electricity consumption information acquisition system coverage rate, a customer service information system coverage rate, a 95598 call center system coverage rate, an electricity consumption proportion for implementing dynamic electricity price, a load control proportion, a micro-grid control proportion and an electric vehicle battery changing station control proportion.
The data in the reliability index data set comprise high-voltage power grid N-2 passing rate, medium-voltage line contact rate, medium-voltage line inter-station contact rate, medium-voltage line average segmentation number, transformer substation single power connection rate, transformer substation single transformation rate, main transformer 'N-1' passing rate, medium-voltage line 'N-1' passing rate, main transformer 'N-2' passing rate, medium-voltage line 'N-2' passing rate, low-voltage average power failure time, self-healing control accuracy, dynamic uninterruptible power supply application rate, self-supply user proportion such as distributed power supply and energy storage, micro-grid power supply user proportion, electric automobile power change station power supply radius user proportion, 110kV and above line wind-proof capacity grade proportion, 10kV line wind-proof capacity grade proportion, typhoon early warning high-voltage line coverage rate, typhoon early warning medium-voltage line coverage rate, thunder early warning high-voltage line coverage rate, The lightning early warning medium-voltage line coverage rate, the pollution flashover early warning high-voltage line coverage rate and the pollution flashover early warning medium-voltage line coverage rate.
The data in the greening index data set comprise a length out-of-limit line proportion, a high-loss distribution transformation proportion, an energy-saving type substation proportion, a new energy power station annual energy generation ratio, a new energy power station installed capacity permeability, a microgrid new energy annual energy generation proportion, a distributed power generation and energy storage capacity proportion, a distributed power capacity grid-connection rate, a renewable energy power generation capacity proportion and an electric vehicle charging station electricity consumption proportion.
Table 2 lists the specific types and ideal values of the microscopic evaluation indexes, and the values are in percent.
TABLE 2
When a specific microscopic index is selected, each index is clearly defined, the indexes are all quantitative indexes, the calculation method is simple, basic data are easy to obtain, and the method is convenient to apply to practical problems.
The core idea of the combined weighting method is that the deviation between the evaluation value corresponding to the combined weight vector and the evaluation value vector corresponding to the original weight vector should be as small as possible. Assuming that k weighting methods are provided, the comprehensive weight vector of n evaluation indexes is A ═ a1，a2，...，aj，...，an]TThe k weighting methods can be considered as samples taken from the population. For subjective weight, if the number of weighted weights tends to be large, the statistical majority theorem can know that the comprehensive result of the judged weight vector is close to the comprehensive weight vector A, and for objective weight, the results obtained by adopting different algorithms have repeatability. The synthetic weight vector a can thus be estimated using the existing objective and subjective weights.
In one embodiment, step S120 is step 122 through step 126.
Step 122: and performing sample extraction on the preset subjective weight totality and objective weight totality to obtain weight samples of each microscopic evaluation index.
Suppose that p samples are extracted from the subjective weight population and k-p samples are extracted from the objective weight population, and k weight samples exist for the ith evaluation index.
Step 124: and establishing a comprehensive weight model corresponding to the microscopic evaluation indexes according to the weight samples.
For the ith evaluation index, forming a comprehensive weight a of the evaluation index according to k weight samplesiNeed to satisfy aiThe smaller the deviation from k subjective and objective weights, the better. The integrated weight model is as follows:
wherein, aiThe weight of the ith index after combination, α and β are relative importance degree coefficients of the main weight and the objective weight respectively, gis、gitThe weighting results of the ith index by the s-th subjective weighting method and the t-th objective weighting method are respectively obtained.
k samples come from 2 populations, and for the ith evaluation index, the subjective weight g of the index is calculated according to the principle of mathematical statisticsisAnd objective weight gitDesired value of (a):
according to the above formula and the basic idea of moment estimation, for the ith index, the main and objective importance coefficients are respectively:
for n indexes, n samples can be taken from 2 populations respectively, and the main importance coefficient and the objective importance coefficient in the comprehensive index can be obtained according to the basic idea of moment estimation:
A＝[a1，a2，...，aj，...，an]the resulting final weight vectors are combined for the k weighting methods. The optimization combination weighting method based on the minimum total deviation not only needs to utilize the weight information, but also takes the evaluation vector as the basis of combination, and the weight vector and the evaluation value are fused to establish an optimization model.
Step 126: and constructing a Lagrangian function for each comprehensive weight model, and calculating according to the Lagrangian function to obtain the weight of each data in the micro-evaluation index data set.
To solve for a1Constructing Lagrangian function L (a) for the model1μ) for a, respectively, according to the requirement that the extreme value existsiAnd μ taking the first partial derivative:
let the partial derivative be 0, and develop with i ═ 1, 2.
Thereby obtaining an integrated weight vector a ═ a1，a2，...，aj，...，an]. Wherein, anThe weight of the nth data in the index data set is evaluated for the microscopic scale.
The microscopic evaluation indexes are more in number, mainly quantitative indexes, and have no coupling dependence relationship among the indexes and strong relative independence. By adopting the combined weighting method based on the moment estimation theory, the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that a single weighting method determines the weight is avoided, and the scientificity and the reasonability of the evaluation result are improved.
In one embodiment, step S140 includes steps 142 through 146.
Step 142: and calculating subjective weight of each macroscopic demand index according to a DEMATEL-ANP method.
Specifically, step 142 includes steps 1421 through 1426.
Step 1421: and establishing an ANP network structure according to the incidence relation between every two macroscopic demand indexes.
The macro demand indicators serve as network layer elements of the ANP network structure. For the problem of comprehensive evaluation of the smart power grid, the whole evaluation of the power grid can be used as a control layer (target), the macroscopic indicators can be used as network layer elements, and an ANP network structure between the elements is established according to the incidence relation between every two macroscopic indicators, as shown in fig. 8.
Step 1422: and obtaining a direct influence matrix according to the ANP network structure.
In the network layer with ANP there is an element E1，E2，…，EnElement Ej(j ≠ i) for EiHas a direct influence of yij. In turn with Ei(i 1, 2.. times, n) as a secondary criterion, and the rest of the elements (except E)iExternal) comparing two by two the direct influence degrees of the criterion elements to obtain corresponding judgment matrixes, and obtaining a judgment matrix E by using a characteristic root methodiWeight vector under criterion
The weight vectors under all the secondary criteria are synthesized into a weight matrix, and the influence of elements on the weight matrix is not considered in the process of constructing the judgment matrix, so that the formed internal dependency matrix is deficient in diagonal. Filling 0 (representing that the element has no direct influence on the element) on the diagonal line of the weight matrix can obtain the direct influence matrix W in the DEMATEL methodd。
Step 1423: and obtaining an average comprehensive influence matrix among elements of each network layer by utilizing a DEMATEL method according to the direct influence matrix.
When the comprehensive influence matrix is obtained according to the DEMATEL method, the condition of matrix convergence needs to be met, otherwise, whether the comprehensive influence matrix can be obtained cannot be determined, so that the method calculates the average comprehensive influence matrix W among indexes of each layer to avoid the problem.
When n is sufficiently large, W ═ W can be usedd(I-Wd)-1And performing approximate calculation, wherein I is an identity matrix, and W is an internal dependency matrix to be constructed, which is called a comprehensive influence weight matrix for short. Merging the internal dependency matrixes of the element sets to obtain a system super matrix. Depending on whether the limit of the integrated impact weight matrix W is unique, the discussion can be divided into two cases:
(1) when a unique limit value exists, W ═ limn→∞(Wd)n；
(2) When a plurality of limit values exist, the matrix shows periodic change, and the point P is set as the beginning of a certain cycle period, and the limit value of the point P is set asAnd T is the cycle period, the limit values in the whole period are respectivelyAnd taking the average value of each point to obtain the limit value of the average comprehensive influence matrix.
Step 1424: and calculating the influence degree among the network layer elements by using a DEMATEL method to obtain a weighting matrix.
For the weighting matrix A of the system, the influence degree among the element sets is calculated by taking the DEMATEL method as reference.
Step 1425: and obtaining a system weighting super matrix according to the average comprehensive influence matrix and the weighting matrix.
Combining the weighting matrix with the average comprehensive influence matrix to obtain the system weighting super matrix
Step 1426: and evolving the system weighting super matrix to obtain the subjective weight of each macroscopic demand index.
Carrying out evolution (k → + ∞) for 2k +1 times on the matrix to finally form a long-term stable matrix, wherein nonzero values of all rows of the long-term stable matrix are the same, and obtaining subjective weight vectors of all evaluation indexes
The method has the advantages that the relation of interdependency and mutual influence exists among all macro demand indexes of the intelligent power grid, the ANP judgment matrix needs to be obtained by comparing indirect dominance degrees, meanwhile, the construction method of the internal dependency matrix is improved by using the DEMATEL method for reference, the subjective estimation of the influence degree of elements on the self is avoided, and the problem that the direct influence and the indirect influence are not uniform when the judgment matrix is constructed is solved.
Step 144: and calculating the objective weight of each macroscopic demand index according to an entropy weight resisting method.
Objective weight vector omega of macroscopic indexoDetermining by adopting an anti-entropy weight method, wherein when determining the index weight, the larger the difference of the index is, the smaller the entropy value is, and the larger the weight coefficient of the index is; on the contrary, the smaller the difference of the indexes, the larger the entropy value, and the smaller the weight coefficient of the indexes. In order to avoid the extreme condition that the index is too small during weight distribution due to high sensitivity of index difference in the entropy weight method, the entropy weight method is selected to determine the objective weight of the index.
Wherein p is not less than 0jLess than or equal to 1 andthe characteristics of the entropy resistance value and the entropy resistance value are different, and the larger the difference of the indexes is, the larger the weight coefficients of the entropy resistance value and the indexes are; conversely, the smaller the difference of the indexes is, the smaller the weight coefficients of the anti-entropy value and the indexes are.
Specifically, step 144 includes step 1442 and step 1444.
Step 1442: and calculating the inverse entropy of each macroscopic demand index according to an inverse entropy weight method.
Setting m evaluation objects and n evaluation indexes in the evaluation problem, wherein the index value is xijThe evaluation matrix is X (X), which is 1, 2, n, and j 1, 2ij)n×m. Determining the inverse entropy of each index according to the evaluation matrix X as follows:
wherein,hiinverse entropy, r, representing the ith macroscopic demand indexijTo evaluate the data in the ith row and the jth column of the matrix.
Step 1444: and calculating objective weight corresponding to the macroscopic demand index according to the inverse entropy.
Further determining objective weight omega of each index according to the inverse entropy valueoi。
Wherein, ω isoiIs the objective weight of the ith macroscopic demand index, hiAnd (3) expressing the inverse entropy of the ith macroscopic demand index.
Step 146: and calculating the weight of the corresponding macroscopic demand index according to the subjective weight and the objective weight.
According to the DEMATEL-ANP method, an index subjective weight set omega is determineds＝{ωsiI is more than or equal to 1 and less than or equal to n, and determining an objective weight set omega of each index according to an entropy weight resisting methodo＝{ωoiI is more than or equal to 1 and less than or equal to n, the relative importance degree of the subjective weight and the objective weight is also different according to different indexes, the relative importance degree of the subjective weight and the objective weight is respectively represented as α and β, and the subjective weight and the objective weight importance coefficients α of each index are finally calculated by combining the basic idea of the moment estimation theoryiAnd βi。
Finally, the obtained subjective weight set, objective weight set and relative importance coefficients of the subjective and objective weights can be used for calculating comprehensive subjective and objective information to determine the weight omega corresponding to the macroscopic demand indexi。
An ANP (Analytic Network processing) method can effectively Process complex association relation existing among macro demand indexes through an internal circulation And feedback Network structure, And improves the ANP method by utilizing a DEMATEL (Decision making experiment And Evaluation Laboratory) theory, so that the problems that the influence of elements on the ANP method And the interrelation among the elements cannot be objectively expressed are solved. The use of the anti-entropy weight method is beneficial to the objective evaluation of the index attribute, so that the obtained evaluation result is more objective and reasonable, the sensitivity of the weight to the index difference can be effectively reduced, and the frequency of extreme situations during weight distribution is reduced.
The combination weighting method based on the moment estimation theory and the DEMATEL-ANP-entropy-resisting weight method are combined to obtain the combination weight hierarchical optimization model shown in the figure 9. After the weights of the macroscopic demand index and the microscopic evaluation index are respectively determined, objective facts, professional knowledge and engineering experience are comprehensively considered, the cost type, the benefit type and the specific type of the microscopic indexes are respectively subjected to percentage scoring, and finally, the corresponding index evaluation values are subjected to weighted summation, so that the comprehensive evaluation result of the whole intelligent power grid demonstration engineering and the four aspects of intellectualization, high efficiency, reliability and greenness can be obtained.
According to the comprehensive evaluation method for the demonstration engineering of the smart power grid, the weight of each data in the microscopic evaluation index data set is calculated by using the combined weighting method based on the moment estimation theory, and the problem that the weighting result is biased due to the fact that the weighting method is easily influenced because the weighting method is determined by a single weighting method is solved. Macroscopic index weight is determined through a DEMATEL-ANP-entropy-resisting weight method, microscopic index weight is determined through a combined weighting method based on a moment estimation theory, weighting is carried out on microscopic evaluation indexes of the demonstration engineering of the intelligent power grid, the subjective intention of a decision maker can be effectively reflected, statistics among objective data can be reflected in the weights, and weighting results are enabled to be more fair. The method has the advantages that the requirements and attributes of the smart power grid are comprehensively considered, the index weight and the score are reasonably and objectively determined, the comprehensive benefit can be scientifically evaluated, the evaluation reliability is high, the guiding effect is achieved, and the method has certain significance for project planning and post evaluation of the smart power grid demonstration project.
In one embodiment, an exemplary engineering comprehensive evaluation system of a smart grid, as shown in fig. 10, includes an index data receiving module 110, a micro weight calculating module 120, an index value calculating module 130, a macro weight calculating module 140, and a comprehensive evaluation output module 150.
The index data receiving module 110 is configured to receive a microscopic evaluation index data set of the smart grid.
The microscopic evaluation index data set comprises various data, and each data is a microscopic evaluation index of the smart grid. The type of the microscopic evaluation index is not unique, and specifically includes a benefit type index, a cost type index and a specific type index.
The microscopic weight calculation module 120 is configured to calculate a weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory.
The weight of each data in the microscopic evaluation index data set is calculated by using a combined weighting method based on a moment estimation theory, so that the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that the weighting method is used for determining the weight by a single weighting method is solved.
The index value calculation module 130 is configured to calculate a macro demand index value according to the microscopic evaluation index data set and the weight of the corresponding data.
The types of data included in the microscopic evaluation index data set are not unique, and the types of macroscopic demand index values calculated according to the microscopic evaluation index data set are correspondingly different. In this embodiment, the microscopic evaluation index data set includes microscopic evaluation index data including an intelligent index data set, a high efficiency index data set, a reliability index data set, and a greening index data set; the macro demand index value comprises an intellectualization index value, an efficiency index value, a reliability index value and a greening index value.
The types of data included in the microscopic evaluation index data set are not unique, and the types of macroscopic demand index values calculated according to the microscopic evaluation index data set are correspondingly different. In this embodiment, the microscopic evaluation index data set includes microscopic evaluation index data including an intelligent index data set, a high efficiency index data set, a reliability index data set, and a greening index data set; the macro demand index value comprises an intellectualization index value, an efficiency index value, a reliability index value and a greening index value.
The macroscopic weight calculation module 140 is configured to calculate the weight of each macroscopic demand indicator by using a DEMATEL-ANP-entropy-inversion weight method.
And determining the weight of the macro demand index by adopting an optimal combined weighting method combining DEMATEL-ANP and an entropy weight resisting method, thereby integrating the expert experience information of the index and the objective attribute of the index. Correspondingly, in this embodiment, the macro demand index includes an intelligent index, a high efficiency index, a reliability index, and a greening index. And respectively calculating the weights of the intelligent index, the high efficiency index, the reliability index and the greening index by utilizing a DEMATEL-ANP-entropy weight resisting method.
And the comprehensive evaluation output module 150 is used for calculating according to the macro demand index value and the weight corresponding to the macro demand index to obtain and output a comprehensive evaluation result.
And carrying out weighted summation on the corresponding index evaluation value and the corresponding weight to obtain a comprehensive evaluation result of the smart grid demonstration project. The output mode of the comprehensive evaluation result is not exclusive, and may be output to a memory for storage or output to a display for display.
In one embodiment, the microscopic weight calculation module 120 includes a weight sample sampling unit, a weight model building unit, and a microscopic weight calculation unit.
The weight sample sampling unit is used for sampling the preset subjective weight population and objective weight population to obtain the weight sample of each microscopic evaluation index.
The weight model establishing unit is used for establishing a comprehensive weight model corresponding to the microscopic evaluation indexes according to the weight samples.
And the microscopic weight calculation unit is used for constructing a Lagrangian function for each comprehensive weight model and calculating the weight of each data in the microscopic evaluation index data set according to the Lagrangian function.
By adopting the combined weighting method based on the moment estimation theory, the problem that the weighting result is biased due to the influence of the weighting method easily caused by the fact that a single weighting method determines the weight is avoided, and the scientificity and the reasonability of the evaluation result are improved.
In one embodiment, the macroscopic weight calculation module 140 includes a subjective weight calculation unit, an objective weight calculation unit, and a macroscopic weight calculation unit.
And the subjective weight calculation unit is used for calculating the subjective weight of each macroscopic demand index according to the DEMATEL-ANP method.
Specifically, the subjective weight calculation unit comprises an ANP network structure establishment unit, a direct influence matrix calculation unit, an average comprehensive influence matrix calculation unit, a weighting hypermatrix calculation unit and a subjective weight calculation unit.
The ANP network structure establishing unit is used for establishing the ANP network structure according to the incidence relation between every two macroscopic demand indexes. The macro demand indicators serve as network layer elements of the ANP network structure.
The direct influence matrix calculation unit is used for obtaining a direct influence matrix according to the ANP network structure.
And the average comprehensive influence matrix calculation unit is used for obtaining an average comprehensive influence matrix among the network layer elements by utilizing a DEMATEL method according to the direct influence matrix.
And the weighting matrix calculation unit is used for calculating the influence degree between the network layer elements by using a DEMATEL method to obtain a weighting matrix.
And the weighted super matrix calculation unit is used for obtaining a system weighted super matrix according to the average comprehensive influence matrix and the weighted matrix.
And the subjective weight calculation unit is used for evolving the system weighting super matrix to obtain the subjective weight of each macroscopic demand index.
The method has the advantages that the relation of interdependency and mutual influence exists among all macro demand indexes of the intelligent power grid, the ANP judgment matrix needs to be obtained by comparing indirect dominance degrees, meanwhile, the construction method of the internal dependency matrix is improved by using the DEMATEL method for reference, the subjective estimation of the influence degree of elements on the self is avoided, and the problem that the direct influence and the indirect influence are not uniform when the judgment matrix is constructed is solved.
And the objective weight calculation unit is used for calculating the objective weight of each macroscopic demand index according to the entropy weight resisting method.
Specifically, the objective weight calculation unit includes an index inverse entropy calculation unit and an objective weight calculation unit.
And the index inverse entropy calculation unit is used for calculating the inverse entropy of each macroscopic demand index according to an inverse entropy weight method.
And the objective weight calculation unit is used for calculating objective weights corresponding to the macroscopic demand indexes according to the inverse entropy.
Objective weight vector omega of macroscopic indexoDetermining by adopting an anti-entropy weight method, wherein when determining the index weight, the larger the difference of the index is, the smaller the entropy value is, and the larger the weight coefficient of the index is; on the contrary, the smaller the difference of the indexes, the larger the entropy value, and the smaller the weight coefficient of the indexes. In order to avoid the extreme condition that the index is too small during weight distribution due to high sensitivity of index difference in the entropy weight method, the entropy weight method is selected to determine the objective weight of the index.
And the macroscopic weight calculation unit is used for calculating the weight of the corresponding macroscopic demand index according to the subjective weight and the objective weight.
According to the DEMATEL-ANP method, an index subjective weight set omega is determineds＝{ωsiI is more than or equal to 1 and less than or equal to n, and determining an objective weight set omega of each index according to an entropy weight resisting methodo＝{ωoiI is more than or equal to 1 and less than or equal to n, the relative importance degree of the subjective weight and the objective weight is also different according to different indexes, the relative importance degree of the subjective weight and the objective weight is respectively represented as α and β, and the subjective weight and the objective weight importance coefficients α of each index are finally calculated by combining the basic idea of the moment estimation theoryiAnd βi。
Finally, the obtained subjective weight set, objective weight set and relative importance coefficients of the subjective and objective weights can be used for calculating comprehensive subjective and objective information to determine the weight omega corresponding to the macroscopic demand indexi。
The ANP method can effectively process the complex incidence relation existing among macro demand indexes through an internal circulation and feedback network structure, and improves the ANP method by utilizing the DEMATEL theory, thereby solving the problems that the influence of elements on the ANP method and the mutual relation among the elements cannot be objectively expressed. The use of the anti-entropy weight method is beneficial to the objective evaluation of the index attribute, so that the obtained evaluation result is more objective and reasonable, the sensitivity of the weight to the index difference can be effectively reduced, and the frequency of extreme situations during weight distribution is reduced.
According to the comprehensive evaluation system for the demonstration engineering of the smart power grid, the weight of each data in the microscopic evaluation index data set is calculated by using the combined weighting method based on the moment estimation theory, and the problem that the weighting result is biased due to the fact that the weighting method is easily influenced because the weighting method is determined by a single weighting method is solved. The macroscopic index weight is determined by a DEMATEL-ANP-inverse entropy weight method, the microscopic index weight is determined by a combined weighting method based on a moment estimation theory, the comprehensive benefit can be scientifically evaluated, the evaluation reliability is high, and the method has an instructive effect.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A comprehensive evaluation method for demonstration engineering of an intelligent power grid is characterized by comprising the following steps:
receiving a microscopic evaluation index data set of the smart grid;
calculating the weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory;
calculating to obtain a macroscopic demand index value according to the microscopic evaluation index data set and the weight of the corresponding data;
calculating the weight of each macroscopic demand index by using a DEMATEL-ANP-entropy weight inversion method;
and calculating according to the macro demand index value and the weight of the corresponding macro demand index to obtain and output a comprehensive evaluation result.
2. The comprehensive assessment method for smart grid demonstration engineering according to claim 1, wherein the microscopic assessment index data set comprises microscopic assessment index data including an intelligent index data set, a high efficiency index data set, a reliability index data set, and a greening index data set; the macro demand index value comprises an intellectualization index value, an efficiency index value, a reliability index value and a greening index value.
3. The comprehensive assessment method for the demonstration engineering of the smart grid according to claim 1, wherein the step of calculating the weight of each data in the microscopic assessment index data set by using a combined weighting method based on a moment estimation theory comprises the following steps:
performing sample extraction on a preset subjective weight totality and an objective weight totality to obtain a weight sample of each microscopic evaluation index;
establishing a comprehensive weight model corresponding to the microscopic evaluation indexes according to the weight samples;
and constructing a Lagrangian function for each comprehensive weight model, and calculating according to the Lagrangian function to obtain the weight of each data in the calculated microscopic evaluation index data set.
4. The comprehensive evaluation method for demonstration engineering of the smart grid according to claim 1, wherein the step of calculating the weight of each macroscopic demand indicator by using a DEMATEL-ANP-entropy weight method comprises the following steps:
calculating subjective weight of each macroscopic demand index according to a DEMATEL-ANP method;
calculating objective weight of each macroscopic demand index according to an entropy weight resisting method;
and calculating the weight of the corresponding macroscopic demand index according to the subjective weight and the objective weight.
5. The comprehensive evaluation method for the demonstration engineering of the smart grid according to claim 4, wherein the step of determining the subjective weight of each macroscopic demand indicator according to the DEMATEL-ANP method comprises the following steps:
establishing an ANP network structure according to the incidence relation between every two macroscopic demand indexes; the macro demand indicator is used as a network layer element of the ANP network structure;
obtaining a direct influence matrix according to the ANP network structure;
according to the direct influence matrix, obtaining an average comprehensive influence matrix among elements of each network layer by using a DEMATEL method;
calculating the influence degree between network layer elements by using a DEMATEL method to obtain a weighting matrix;
obtaining a system weighting super matrix according to the average comprehensive influence matrix and the weighting matrix;
and evolving the system weighting super matrix to obtain the subjective weight of each macroscopic demand index.
6. A comprehensive evaluation system for demonstration engineering of a smart grid is characterized by comprising:
the index data receiving module is used for receiving a microscopic evaluation index data set of the smart grid;
the microscopic weight calculation module is used for calculating the weight of each data in the microscopic evaluation index data set by using a combined weighting method based on a moment estimation theory;
the index value calculation module is used for calculating to obtain a macro demand index value according to the micro evaluation index data set and the weight of the corresponding data;
the macroscopic weight calculation module is used for calculating the weight of each macroscopic demand index by utilizing a DEMATEL-ANP-entropy weight inversion method;
and the comprehensive evaluation output module is used for calculating according to the macro demand index value and the weight corresponding to the macro demand index to obtain and output a comprehensive evaluation result.
7. The smart grid demonstration engineering comprehensive assessment system according to claim 6, wherein said microscopic weight calculation module comprises:
the weight sample sampling unit is used for sampling the preset subjective weight totality and objective weight totality to obtain weight samples of each microscopic evaluation index;
the weight model establishing unit is used for establishing a comprehensive weight model corresponding to the microscopic evaluation indexes according to the weight samples;
and the microscopic weight calculation unit is used for constructing a Lagrangian function for each comprehensive weight model and calculating the weight of each data in the microscopic evaluation index data set according to the Lagrangian function.
8. The smart grid demonstration engineering comprehensive assessment system according to claim 6, wherein said macroscopic weight calculation module comprises:
the subjective weight calculation unit is used for calculating the subjective weight of each macroscopic demand index according to the DEMATEL-ANP method;
the objective weight calculation unit is used for calculating objective weights of all the macroscopic demand indexes according to an entropy weight method;
and the macroscopic weight calculating unit is used for calculating the weight corresponding to the macroscopic demand index according to the subjective weight and the objective weight.
9. The comprehensive evaluation system for demonstration projects of smart grid according to claim 8, wherein said subjective weight calculation unit comprises:
the ANP network structure establishing unit is used for establishing an ANP network structure according to the incidence relation between every two macroscopic demand indexes; the macro demand indicator is used as a network layer element of the ANP network structure;
the direct influence matrix calculation unit is used for obtaining a direct influence matrix according to the ANP network structure;
the average comprehensive influence matrix calculation unit is used for obtaining an average comprehensive influence matrix among elements of each network layer by utilizing a DEMATEL method according to the direct influence matrix;
the weighting matrix calculation unit is used for calculating the influence degree between the network layer elements by using a DEMATEL method to obtain a weighting matrix;
the weighted super matrix calculation unit is used for obtaining a system weighted super matrix according to the average comprehensive influence matrix and the weighted matrix;
and the subjective weight calculation unit is used for evolving the system weighted super matrix to obtain the subjective weight of each macroscopic demand index.
10. The comprehensive evaluation system for smart grid demonstration engineering according to claim 8, wherein the objective weight calculation unit comprises:
the index inverse entropy calculation unit is used for calculating the inverse entropy of each macroscopic demand index according to an inverse entropy weight method;
and the objective weight calculation unit is used for calculating objective weights corresponding to the macroscopic demand indexes according to the inverse entropy.
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Application publication date: 20170510 |