CN111080041A - Comprehensive evaluation method and system for interactivity of power distribution network - Google Patents
Comprehensive evaluation method and system for interactivity of power distribution network Download PDFInfo
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Abstract
The invention provides a comprehensive evaluation method for interactivity of a power distribution network, which comprises the following steps: acquiring power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system; determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data; the comprehensive weight is brought into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network, and the interactivity of the power distribution network is evaluated; wherein the comprehensive weight corresponding to the interaction characteristic index is as follows: according to the method, the situation that the large-scale generalized load is accessed to the power distribution network is considered, a generalized load coupling interaction comprehensive evaluation index system is established from two aspects of interaction effect and interaction foundation, the problem of quantification of generalized load coupling interaction characteristic indexes is solved, and a development direction is provided for future construction of the urban power distribution network.
Description
Technical Field
The invention relates to the field of comprehensive evaluation of a power distribution network, in particular to a comprehensive evaluation method for interactivity of the power distribution network.
Background
In recent years, with the development of communication technology and network technology, the research and construction of smart grids has risen globally. The relatively mature test point projects comprise an intelligent substation, a power distribution automation system, a power utilization information acquisition system and the like.
As an important component of a power grid, the intellectualization of a power distribution network becomes a new trend of future power grid development, and plays a significant role in realizing the overall goal of smart power grid construction.
The construction of the smart grid is rapidly developed, and a future power system becomes cleaner, more efficient, safer and more reliable under the support of the smart grid. The generalized load actively participates in the operation of the power grid, and the demand side management means is combined, so that the consumption of renewable energy sources is promoted, the environment is improved, the peak clipping and valley filling are realized, and the development of the smart power grid is promoted. The generalized load refers to a load composed of elements such as a static load, a dynamic load, a low-voltage transmission network, a medium-and-small-capacity generator and the like in an electric power receiving end system, wherein the generator can be regarded as a negative load. Typical generalized loads include distributed power supplies, electric vehicles, energy storage devices and the like, but the problem of quantification of generalized load coupling interaction characteristic indexes still exists.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a comprehensive evaluation method for interactivity of a power distribution network.
The technical scheme provided by the invention is as follows:
a comprehensive evaluation method for interaction of a power distribution network comprises the following steps:
acquiring power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system;
determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data;
the comprehensive weight is brought into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network, and the interactivity of the power distribution network is evaluated;
wherein the comprehensive weight corresponding to the interaction characteristic index is as follows: and determining objective weight by adopting an entropy method and determining subjective weight by adopting an ANP method.
Preferably, the construction of the power distribution network interaction characteristic index system includes:
constructing a first-level index according to the influence factors of the generalized load coupling interaction characteristics;
constructing a secondary index for each primary index based on the influence factors of the primary indexes;
constructing a third-level index for each second-level index based on the influence factors of the second-level indexes;
the first-level index belongs to the control layer;
the second level index and the third level index belong to a network layer.
Preferably, the determining the comprehensive weight corresponding to the interaction characteristic index based on the power grid data includes:
1) determining objective weight of the index by using an entropy method;
2) determining subjective weight of the index by adopting a network analytic hierarchy process;
3) and determining the comprehensive weight by adopting an additive integration method according to the objective weight and the subjective weight of the index.
Preferably, the objective weight of the index is calculated as follows:
in the formula, pjObjective weight as jth index; e.g. of the typejEntropy value of j index;
wherein, the entropy of the j index is calculated according to the following formula:
in the formula, pijThe specific gravity of the ith index value under the jth index;
the proportion of the index value of the ith item under the jth index is calculated according to the following formula:
in the formula, rijIs the value of the ith index under the jth index.
Preferably, the determining the subjective weight of the index by using the network analytic hierarchy process includes:
comparing and analyzing every two of the three-level indexes corresponding to each two-level index according to the influence to form a judgment matrix;
obtaining normalized sorting weight by a characteristic root method by adopting a ninth division method scale proposed by Satty based on the judgment matrix;
constructing an ANP super matrix from the three-level indexes based on the normalized ranking weight;
comparing every two second indexes corresponding to the first-level indexes to construct a judgment matrix, and calculating an importance sorting vector of the judgment matrix to obtain a weighting matrix;
obtaining a weighted super matrix from the ANP super matrix and the weighted matrix;
and calculating subjective weight of the index based on the weighted hypermatrix.
Preferably, the subjective weight is calculated as follows:
in the formula, k is the step number of the dominance degree; n is the maximum number of steps;
is a weighted super matrix.
Preferably, the comprehensive weight corresponding to the interaction characteristic index is calculated according to the following formula:
in the formula, wjIs the composite weight; k is a radical of1Is a constant number 1; k is a radical of2Is a constant number 2; p is a radical ofjIs an objective weight; q. q.sjIs a subjective weight.
Preferably, the bringing the comprehensive weight into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network to evaluate the interactivity of the power distribution network includes:
setting the maximum weight of the three-level indexes as a positive ideal value and the minimum weight as a negative ideal value, constructing a positive ideal system by using the positive ideal values of all the three-level indexes, and constructing a negative ideal system by using the negative ideal values of all the three-level indexes;
calculating Euclidean distances of a positive ideal system and a negative ideal system of each three-level index according to a weighted distance formula;
calculating a queuing indicating value of the three-level index by using a queuing indicating value formula;
the larger the queuing indicated value is, the stronger the interaction characteristic of the power distribution network is.
Preferably, the euclidean distance of the just-ideal system is calculated as follows:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the positive ideal system; m is the number of the evaluation objects; w is ajIs the composite weight of the jth element; x is the number ofijIs an index value before dimensionless;
is the positive ideal value of the jth element;
the Euclidean distance of the negative ideal system is calculated according to the following formula:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the negative ideal system; m is the number of the evaluation objects; w is ajIs the composite weight of the jth elementWeighing; is xijA non-dimensionalized index value;
is the positive ideal value of the jth element.
Preferably, the primary indicator includes: generalized load interaction effect and generalized load interaction foundation;
the secondary indexes of the generalized load interaction effect comprise: generalized load participation, daily load curve fluctuation of a power grid and electric power cleanliness;
the secondary indexes of the generalized load interaction basic index comprise: the system comprises a power grid interaction foundation, an electric vehicle interaction foundation, a distributed interaction foundation and an energy storage interaction foundation;
the secondary indicators of the generalized load participation degree comprise: daily generalized load supply rate, generalized load response degree;
the daily load curve fluctuation degree of the power grid comprises the following steps: the number of periodic peak-valley alternation times, daily peak-valley ratio, daily load rate and feeder line load curve variance;
the electric power cleanliness comprises: renewable energy consumption rate and unit generated energy CO2Discharge capacity;
the power grid interaction foundation comprises: coverage rate of the intelligent electric meter, the number of types of interactive service channels and the scale of an interactive business hall;
the electric automobile interaction basis include: the urban charging and battery replacing capacity area density, the utilization rate of a charging and battery replacing facility and the matching degree of the electric automobile and the charging and battery replacing facility;
the distributed interaction foundation comprises: the installed capacity ratio of the distributed power supply and the grid connection reliability of the distributed power supply are determined;
the energy storage interaction foundation comprises: the ratio of the maturity of the energy storage system to the installed capacity of the energy storage system is determined.
A comprehensive evaluation system for power distribution network interactivity comprises:
constructing a module: the method comprises the steps of obtaining power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system;
a determination module: determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data;
an evaluation module: and bringing the comprehensive weight into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network to evaluate the interactivity of the power distribution network.
Preferably, the building block comprises:
a first building submodule: the method is used for constructing a first-level index according to the influence factors of the generalized load coupling interaction characteristics;
a second building submodule: the method is used for constructing a secondary index for each primary index based on the influence factors of the primary indexes;
a third building submodule: the method is used for constructing a third-level index for each second-level index based on the influence factors of the second-level indexes;
preferably, the evaluation module comprises:
a fourth building submodule: the system comprises a three-level index, a positive ideal value, a negative ideal value, a positive ideal system and a negative ideal system, wherein the maximum weight of the three-level index is set as the positive ideal value, the minimum weight of the three-level index is set as the negative ideal value, the positive ideal values of all the three-level indexes construct the positive ideal system, and the negative ideal values of all the three-level indexes construct the negative ideal system;
a first calculation submodule: the Euclidean distance calculation method is used for calculating the Euclidean distance of a positive ideal system and a negative ideal system of each three-level index according to a weighted distance formula;
a second calculation submodule: the method is used for calculating the queuing indicating value of the three-level index by using a queuing indicating value formula.
An evaluation submodule: and comprehensively evaluating the power grid interactivity by the stronger power distribution network interactive characteristic when the queuing indicated value is larger.
Preferably, the determining module includes:
a first determination sub-module: the objective weight of the index is determined by adopting an entropy method;
a second determination sub-module: the subjective weight used for determining the index by adopting a network analytic hierarchy process;
a third determination sub-module: and the method is used for determining the weight of the comprehensive evaluation model by adopting an additive integration method according to the objective weight of the index and the subjective weight of the index.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a comprehensive evaluation method for interactivity of a power distribution network, which comprises the following steps: acquiring power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system; determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data; the comprehensive weight is brought into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network, and the interactivity of the power distribution network is evaluated; wherein the comprehensive weight corresponding to the interaction characteristic index is as follows: and determining objective weight by adopting an entropy method and determining subjective weight by adopting an ANP method. According to the comprehensive evaluation method, the situation that large-scale generalized loads are connected into the power distribution network is considered, a generalized load coupling interaction comprehensive evaluation index system is established from two aspects of interaction effect and interaction foundation, the problem of quantification of generalized load coupling interaction characteristic indexes is solved, a comprehensive evaluation model is improved, an entropy method-ANP weighting method based on an addition integration method is selected to comprehensively consider subjective and objective factors, comprehensive evaluation results are more scientific and reasonable, evaluation basis is provided for the development level of the urban power distribution network, and development direction is provided for future construction of the urban power distribution network.
Drawings
FIG. 1 is a flow chart of a comprehensive evaluation method for interactivity of a power distribution network according to the present invention;
FIG. 2 is a flowchart of an embodiment of a comprehensive evaluation method for power distribution network interactivity of the present invention;
FIG. 3 is a generalized load coupling interaction characteristic index system diagram of the present invention;
FIG. 4 is a generalized load interaction index system model based on network partition according to the present invention;
FIG. 5 is an ANP network in the Super Decision software according to the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Detailed description of the preferred embodiment 1
The invention discloses a comprehensive evaluation method for interactivity of a power distribution network, which is shown in figure 1:
1. constructing a power distribution network interactive characteristic index system based on the selected power distribution network interactive characteristic indexes:
2. determining the weight of a comprehensive evaluation model based on the power grid data corresponding to the power distribution network interaction characteristic indexes:
3. according to the weight of the comprehensive evaluation model, comprehensive evaluation is carried out based on a TOPSIS model improved in advance, and the evaluation result of the interactivity of the power distribution network is obtained:
1. constructing a power distribution network interactive characteristic index system based on the selected power distribution network interactive characteristic indexes:
taking six areas of China A, B, C, D, E, F as an example, the method carries out reasonable evaluation on urban generalized load coupling interaction characteristics according to the thought of the research, firstly, a generalized load coupling interaction comprehensive evaluation index system of each area is established, then, for the six areas needing to be evaluated, the 18 three-level index data of each area in 2015 are collected, wherein data of a typical day in 2015 are extracted to obtain 6 three-level index data of daily generalized load supply rate, generalized load response degree, periodic peak-valley times, daily peak-valley ratio, daily load rate and feeder load curve variance of each area. For each type of index data of the selected region, the above-mentioned method of uniformization and non-dimensionalization is used to make all data within 01, as shown in table 1 below.
TABLE 1 nondimensional data of each index in each region
2. Determining the weight of a comprehensive evaluation model based on the power grid data corresponding to the power distribution network interaction characteristic indexes:
determining the index weights of all levels according to the entropy method step in the step 3, operating Super Decision software, establishing an index network in the software according to the mutual influence relation among indexes, wherein the influence relation is shown in the attached figure 4, specifically, the index network is shown in the attached figure 5, and obtaining the index weights under the ANP method by adopting the nine-division method scale proposed by Satty.
Applying the formula (16) to the formula (17) to obtain k1＝0.47，k2The index integrated weight was obtained as 0.53, as shown in the table below.
TABLE 2 index weights
And further, by using the comprehensive evaluation method in the step 4, the queue indicating value interactive ordering of the generalized load coupling interactive characteristics in different areas is obtained as shown in the following table.
TABLE 3 Interactive queuing indication values between generalized load and distribution network in the area
In the aspect of the weight of an index system, in the weight calculation result optimized by the 'addition' integration method, the proportion of an interaction effect and an interaction basis in a first-level index is equivalent, wherein the weight of the interaction effect is slightly higher than that of the interaction basis, and the reason is that the interaction effect proportion in the calculation result of the ANP method is higher, which indicates that the influence of experts on the power grid load after the generalized load participates in the interaction is more important. In the aspect of secondary weight, the generalized load participation degree, the electric vehicle interaction basis and the energy storage interaction basis are higher, which shows that the energy storage technology is the development direction of a future smart grid, and in the aspect of comprehensive weight of an index system, indexes such as daily generalized load supply rate, generalized load response degree and interactive business hall area density are higher, and the method is more critical in the evaluation of generalized load coupling interaction characteristics.
3. According to the weight of the comprehensive evaluation model, comprehensive evaluation is carried out based on a TOPSIS model improved in advance, and the evaluation result of the interactivity of the power distribution network is obtained:
in the aspect of comprehensive evaluation results, the evaluation sequence is A → D → B → C → F → E, and the area A mainly benefits from the advantages of high generalized load occupation ratio, relatively quick power grid intelligent process and the like. The improved TOPSIS method avoids the defects that the alternative scheme in the classic TOPSIS method has equal distance and can not be relatively good and bad when the connecting lines of the positive ideal street and the negative ideal street are symmetrical, and has higher universality.
Specific example 2
The comprehensive evaluation method for the interactivity of the power distribution network provided by the invention is shown in figure 2:
step 1: and selecting the interactive characteristic indexes of the power distribution network, classifying and grading the interactive characteristic indexes, and constructing a power distribution network interactive characteristic index system.
Step 2: and constructing an improved TOPSIS model for comprehensive evaluation of the interaction characteristics of the urban distribution network.
And step 3: and determining the weight of the comprehensive evaluation model. Collecting relevant data of the power grid based on the interaction characteristic indexes of the power distribution network selected in the step 1, calculating index weights by respectively adopting an entropy method and a network analytic hierarchy process, and obtaining the weights by the entropy method and the weights obtained by the network analytic hierarchy process according to the proportion obtained by an 'addition method' integration method so as to obtain the overall weight of the comprehensive evaluation model.
And 4, step 4: and (3) according to the weight obtained in the step (3), carrying out comprehensive evaluation by using the improved TOPSIS method in the step (2), evaluating the interactivity of the urban distribution network according to an evaluation result, and proposing a development direction.
Step 1:
in step 1, the method for constructing the power distribution network interaction characteristic index system comprises the following steps:
forming a first-level index of a comprehensive evaluation system of the interaction characteristics of the power distribution network by using two indexes of a generalized load interaction effect and an interaction basis;
the generalized load interaction effect indexes are divided into three secondary indexes of generalized load participation, power grid daily load curve fluctuation and electric power cleanliness; the generalized load interaction basic indexes are divided into four secondary indexes of a power grid interaction basis, an electric vehicle interaction basis, a distributed interaction basis and an energy storage interaction basis.
The generalized load participation degree is reflected by two three-level indexes of daily generalized load supply rate and generalized load response degree; the daily load curve fluctuation degree of the power grid is reflected by four-item three-level indexes of periodic peak-valley alternation times, daily peak-valley ratio, daily load rate and feeder line load curve variance; the electric power cleanliness is realized through the consumption rate of renewable energy sources and the unit generated energy CO2Two three-level indexes of the discharge amount are reflected; the power grid interaction foundation is reflected by three indexes of coverage rate of the intelligent electric meter, the type and quantity of interaction service channels and the scale of an interaction business hall; the electric automobile interaction foundation is reflected by three indexes of city battery charging and replacing capacity area density, battery charging and replacing facility utilization rate and matching degree of an electric automobile and a battery charging and replacing facility; the distributed interaction basis is reflected by two indexes of installed capacity ratio of the distributed power supply and grid connection reliability of the distributed power supply; the energy storage interaction basis is reflected by two indexes of maturity of an energy storage system and the proportion of installed energy storage capacity.
The index system is shown in figure 3.
Step 2:
in step 2, the method for constructing the TOPSIS model for the comprehensive evaluation of the interaction characteristics of the urban distribution network comprises the following steps:
and (3) setting the positive and negative ideal values of the indexes selected in the step (1), wherein the value is usually set as the extreme condition of each index. Is provided with
In order to be just as ideal for the system,
for a negative ideal system, the Euclidean distance of each scheme from the positive ideal system and the negative ideal system can be calculated according to a weighted distance formula.
Distance formula from positive ideal point:
distance formula from negative ideal point:
wherein:
and after the Euclidean distance is calculated, a queuing indicating value formula is used for calculating a queuing indicating value, and the larger the queuing indicating value formula is, the better the generalized load interactive coupling characteristic is. Queuing indication value formula:
and step 3:
in step 3, the generalized load interaction characteristic comprehensive evaluation weight determining method comprises the following steps:
(1) and collecting and sorting data. Selecting a region to be evaluated, and collecting the 18 three-level index data of the region, wherein 6 three-level indexes including daily generalized load supply rate, generalized load response degree, periodic peak-valley alternation times, daily peak-valley ratio, daily load rate and feeder load curve variance are obtained by selecting a typical day. Aiming at various index data of the selected region, a consistency and non-dimensionalization method is adopted to enable all data to be in the range of 01, so that the weight is determined by adopting an entropy method in the next step.
1) Method for making uniform
In the constructed index system, indexes such as the periodic peak-valley alternation times, the daily peak-valley ratio, the feeder load curve variance, the unit generated energy CO2 emission, the distributed power supply grid connection reliability and the like are extremely small indexes, and the other indexes are extremely large indexes, and the extremely small indexes are converted into the extremely large indexes based on a formula (6).
x′＝M-x (6)
In the formula, x' is a post-alignment index, and M is an allowable upper bound of the index x.
2) Dimensionless method
Wherein j is the index number, i is the sample number, xijIs a non-dimensionalized index value, xijIs an index value after non-dimensionalization.
(2) Determining objective weights using entropy method
1) Calculating the specific gravity p of the index value of the ith item under the jth indexij：
2) Calculating the entropy E of the jth indexj：
3) Calculating the weight w of the jth indexj：
(3) Subjective weight determination using ANP
1) And (5) analyzing the problem. And forming elements and element sets, judging whether the elements are independent or not, and judging whether a dependent feedback relation exists or not.
2) And constructing an ANP structure. The ANP comprises a control layer and a network layer, wherein the control layer at least comprises a target and can comprise a decision criterion; the network layer is composed of all elements governed by the control layer, and internally is an interoperable network structure. Let the control layer element of ANP be B1，B2，…，Bm(ii) a Network layer element is C1，C2，…，Cm. Wherein, CiIn is ei1，ei2，…，ein(i ═ 1, 2, …, n). To controlLayer element Bs(s-1, 2, …, m) with C as criterionjMiddle element ejl(l ═ 1, 2, …, n) is a secondary criterion.
3) And calculating the normalized sorting weight.
And determining the influence and influenced relation of all elements in the system based on the established structure. Element set CiIn accordance with its pair ejlThe influence is compared and analyzed pairwise, namely a judgment matrix B is constructeds. In order to quantify each pairwise judgment matrix, nine-division method scale proposed by Satty is adopted, and then a characteristic root method is used for obtaining a sequencing vector
4) An ANP hypermatrix is constructed.
Constructing a matrix from all the network element ordering vectors, and recording as
5) And comparing every two criteria in the control layer to construct a judgment matrix, and calculating an importance sorting vector by using a calculator so as to obtain a weighting matrix A.
Further, a weighted super matrix can be obtained:
6) the limit supermatria were calculated as follows.
If the limits converge and are unique, then
Column j of (A) is a certain standardThe extreme relative ordering of the elements of the network layer below the layer to element j, i.e. the weight value of each element relative to the highest target.
(4) Determination of synthetic weights using additive integration
The entropy method is an objective weighting method based on a 'difference drive' principle, the ANP method is a subjective weighting method based on a 'function drive' principle, and an index x generated by the two methods is setjAre each pj，qjThe "additive" integration method can organically combine the two weighting methods.
When the maximum difference between the objects to be evaluated is to be reflected, k is determined1，k2The value of the following formula is maximized.
In the formula, m and n are the number of evaluation targets and the number of evaluation indexes, respectively.
In satisfying
And k is1＞0，k2If the value is more than 0, applying Lagrange conditional extremum principle to obtain:
in the formula, wjIs the integrated weight.
And 4, step 4:
in step 4, the comprehensive evaluation method comprises the following steps:
(1) the comprehensive evaluation was performed by using the TOPSIS method described in step 2, based on the weights obtained in step 3.
1) And (3) processing the data collected in the step (3) to determine a positive ideal point and a negative ideal point of each index, wherein a vector consisting of the maximum elements of each column is the positive ideal point, and a vector consisting of the minimum elements of each column is the negative ideal point.
2) Substituting the weights of the indexes obtained by the formula (17) into the formulas (1) to (2), and calculating the distance y between different index values and the positive and negative ideal points in different regions by using the collected datai +And yi -。
3) The distance y between different index values and positive and negative ideal points in different areasi +And yi -And substituting the formula (5) to calculate a queuing indicating value, and sequencing the queuing indicating value, wherein the larger the indicating value is, the stronger the generalized load coupling interaction characteristic of the region is.
(2) And evaluating the interactivity of the urban distribution network based on the evaluation result, analyzing the reasons of the strong and weak interactive characteristics of different regions, and providing the future intelligent power grid construction and development directions of all regions.
Specific example 3
A comprehensive evaluation system for power distribution network interactivity comprises:
constructing a module: the method comprises the steps of obtaining power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system;
a determination module: determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data;
an evaluation module: and bringing the comprehensive weight into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network to evaluate the interactivity of the power distribution network.
The building module comprises:
a first building submodule: the method is used for constructing a first-level index according to the influence factors of the generalized load coupling interaction characteristics;
a second building submodule: the method is used for constructing a secondary index for each primary index based on the influence factors of the primary indexes;
a third building submodule: the method is used for constructing a third-level index for each second-level index based on the influence factors of the second-level indexes;
the evaluation module comprises:
a fourth building submodule: the system comprises a three-level index, a positive ideal value, a negative ideal value, a positive ideal system and a negative ideal system, wherein the maximum weight of the three-level index is set as the positive ideal value, the minimum weight of the three-level index is set as the negative ideal value, the positive ideal values of all the three-level indexes construct the positive ideal system, and the negative ideal values of all the three-level indexes construct the negative ideal system;
a first calculation submodule: the Euclidean distance calculation method is used for calculating the Euclidean distance of a positive ideal system and a negative ideal system of each three-level index according to a weighted distance formula;
a second calculation submodule: the method is used for calculating the queuing indicating value of the three-level index by using a queuing indicating value formula.
An evaluation submodule: and comprehensively evaluating the power grid interactivity by the stronger power distribution network interactive characteristic when the queuing indicated value is larger.
The determining module comprises:
a first determination sub-module: the objective weight of the index is determined by adopting an entropy method;
a second determination sub-module: the subjective weight used for determining the index by adopting a network analytic hierarchy process;
a third determination sub-module: and the method is used for determining the weight of the comprehensive evaluation model by adopting an additive integration method according to the objective weight of the index and the subjective weight of the index.
Preferably, the first calculation submodule includes: a first calculation unit and a second calculation unit;
the first calculation unit calculates the Euclidean distance of the ideal system according to the following formula:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the positive ideal system; m is the number of the evaluation objects; w is ajIs the composite weight of the jth element; x is the number ofijIs an index value before dimensionless;
is the positive ideal value of the jth element;
the second calculation unit calculates the Euclidean distance of the negative ideal system by:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the negative ideal system; m is the number of the evaluation objects; w is ajIs the composite weight of the jth element; is xijA non-dimensionalized index value;
is the positive ideal value of the jth element.
Preferably, the determining module includes:
a first determination sub-module: the objective weight of the index is determined by adopting an entropy method;
a second determination sub-module: the subjective weight used for determining the index by adopting a network analytic hierarchy process;
a third determination sub-module: and the method is used for determining the weight of the comprehensive evaluation model by adopting an additive integration method according to the objective weight of the index and the subjective weight of the index.
Preferably, the first determination submodule includes: a third calculation unit, a fourth calculation unit, and a fifth calculation unit;
the third calculation unit: the objective weight is calculated by:
in the formula, pjObjective weight as jth index; e.g. of the typejEntropy value of j index;
the fourth calculation unit: calculating an entropy value of the indicator by:
in the formula, pijThe specific gravity of the index value of the ith item under the jth index;
the fifth calculation unit: the specific gravity of the index value is calculated by the following formula:
in the formula, rijThe index value of the ith item under the jth index attribute.
Preferably, the second determination submodule includes:
a first determination unit: the device is used for carrying out pairwise comparison analysis on the influence of each element concentration element on another element concentration element based on a network layer to form a judgment matrix, and obtaining a normalized sorting weight by a characteristic root method by adopting a nine-division method scale proposed by Satty;
a construction unit: means for constructing an ANP super matrix from the network layer elements based on the normalized ordering weights;
a second determination unit: comparing every two control layers based on the criterion in the control layer to construct a judgment matrix, and calculating an importance ordering vector of the judgment matrix to obtain a weighting matrix;
a third determination unit: the weighted super matrix is obtained by the ANP super matrix and the weighted matrix;
a sixth calculation unit: and the subjective weight used for calculating the index based on the weighted super matrix.
Preferably, the sixth calculation unit includes: a calculation subunit;
the calculation subunit: subjective weights were calculated by:
in the formula, k is the step number of the dominance degree; n is the maximum number of steps;
is a weighted super matrix.
Preferably, the third determining sub-module includes: seventh calculation Unit
The seventh calculation unit: by calculating the composite weight:
in the formula, wjIs the composite weight; k is a radical of1Is a constant number 1; k is a radical of2Is a constant number 2; p is a radical ofjIs an objective weight; q. q.sjIs a subjective weight.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (14)
1. A comprehensive evaluation method for interaction of a power distribution network is characterized by comprising the following steps:
acquiring power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system;
determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data;
the comprehensive weight is brought into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network, and the interactivity of the power distribution network is evaluated;
wherein the comprehensive weight corresponding to the interaction characteristic index is as follows: and determining objective weight by adopting an entropy method and determining subjective weight by adopting an ANP method.
2. The comprehensive evaluation method for interaction of the power distribution network according to claim 1, wherein the construction of the interaction characteristic index system of the power distribution network comprises:
constructing a first-level index according to the influence factors of the generalized load coupling interaction characteristics;
constructing a secondary index for each primary index based on the influence factors of the primary indexes;
constructing a third-level index for each second-level index based on the influence factors of the second-level indexes;
the first-level index belongs to the control layer;
the second level index and the third level index belong to a network layer.
3. The method according to claim 1, wherein the determining a comprehensive weight corresponding to the interaction characteristic index based on the grid data includes:
1) determining objective weight of the index by using an entropy method;
2) determining subjective weight of the index by adopting a network analytic hierarchy process;
3) and determining the comprehensive weight by adopting an additive integration method according to the objective weight and the subjective weight of the index.
4. The comprehensive evaluation method for the interactivity of the power distribution network according to claim 3, wherein the objective weight of the index is calculated according to the following formula:
in the formula, pjObjective weight as jth index; e.g. of the typejEntropy value of j index;
wherein, the entropy of the j index is calculated according to the following formula:
in the formula, pijThe specific gravity of the ith index value under the jth index;
the proportion of the index value of the ith item under the jth index is calculated according to the following formula:
in the formula, rijIs the value of the ith index under the jth index.
5. The comprehensive evaluation method for the interactivity of the power distribution network according to claim 3, wherein the determining the subjective weight of the index by using the network analytic hierarchy process comprises:
comparing and analyzing every two of the three-level indexes corresponding to each two-level index according to the influence to form a judgment matrix;
obtaining normalized sorting weight by a characteristic root method by adopting a ninth division method scale proposed by Satty based on the judgment matrix;
constructing an ANP super matrix from the three-level indexes based on the normalized ranking weight;
comparing every two second indexes corresponding to the first-level indexes to construct a judgment matrix, and calculating an importance sorting vector of the judgment matrix to obtain a weighting matrix;
obtaining a weighted super matrix from the ANP super matrix and the weighted matrix;
and calculating subjective weight of the index based on the weighted hypermatrix.
6. The comprehensive evaluation method for the interactivity of the power distribution network according to claim 5, wherein the subjective weight is calculated according to the following formula:
in the formula, k is the step number of the dominance degree; n is the maximum number of steps;
is a weighted super matrix.
7. The comprehensive evaluation method for the interaction of the power distribution network according to claim 1, wherein the comprehensive weight corresponding to the interaction characteristic index is calculated according to the following formula:
in the formula, wjIs the composite weight; k is a radical of1Is a constant number 1; k is a radical of2Is a constant number 2; p is a radical ofjIs an objective weight; q. q.sjIs a subjective weight.
8. The comprehensive evaluation method for the interaction of the power distribution network according to claim 2, wherein the step of bringing the comprehensive weight into a pre-constructed comprehensive evaluation model for the characteristics of the power distribution network to evaluate the interaction of the power distribution network comprises the steps of:
setting the maximum weight of the three-level indexes as a positive ideal value and the minimum weight as a negative ideal value, constructing a positive ideal system by using the positive ideal values of all the three-level indexes, and constructing a negative ideal system by using the negative ideal values of all the three-level indexes;
calculating Euclidean distances of a positive ideal system and a negative ideal system of each three-level index according to a weighted distance formula;
calculating a queuing indicating value of the three-level index by using a queuing indicating value formula;
the larger the queuing indicated value is, the stronger the interaction characteristic of the power distribution network is.
9. The comprehensive evaluation method for the interactivity of the power distribution network according to claim 8, wherein the euclidean distance of the ideal system is calculated according to the following formula:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the positive ideal system; m is the number of the evaluation objects; wjIs the composite weight of the jth element; x is the number ofijIs an index value before dimensionless;
is the positive ideal value of the jth element;
the Euclidean distance of the negative ideal system is calculated according to the following formula:
in the formula (I), the compound is shown in the specification,
is the Euclidean distance of the negative ideal system; m is the number of the evaluation objects; wjIs the composite weight of the jth element; is xijA non-dimensionalized index value;
is the positive ideal value of the jth element.
10. The comprehensive evaluation method for interactivity of the power distribution network according to claim 2, wherein the primary index comprises: generalized load interaction effect and generalized load interaction foundation;
the secondary indexes of the generalized load interaction effect comprise: generalized load participation, daily load curve fluctuation of a power grid and electric power cleanliness;
the secondary indexes of the generalized load interaction basic index comprise: the system comprises a power grid interaction foundation, an electric vehicle interaction foundation, a distributed interaction foundation and an energy storage interaction foundation;
the secondary indicators of the generalized load participation degree comprise: daily generalized load supply rate, generalized load response degree;
the daily load curve fluctuation degree of the power grid comprises the following steps: the number of periodic peak-valley alternation times, daily peak-valley ratio, daily load rate and feeder line load curve variance;
the electric power cleanliness comprises: renewable energy consumption rate and unit generated energy CO2Discharge capacity；
The power grid interaction foundation comprises: coverage rate of the intelligent electric meter, the number of types of interactive service channels and the scale of an interactive business hall;
the electric automobile interaction basis include: the urban charging and battery replacing capacity area density, the utilization rate of a charging and battery replacing facility and the matching degree of the electric automobile and the charging and battery replacing facility;
the distributed interaction foundation comprises: the installed capacity ratio of the distributed power supply and the grid connection reliability of the distributed power supply are determined;
the energy storage interaction foundation comprises: the ratio of the maturity of the energy storage system to the installed capacity of the energy storage system is determined.
11. The utility model provides a distribution network interactivity comprehensive evaluation system which characterized in that includes:
constructing a module: the method comprises the steps of obtaining power grid data corresponding to interaction characteristic indexes based on the interaction characteristic indexes in a pre-constructed power distribution network interaction characteristic index system;
a determination module: determining comprehensive weight corresponding to the interaction characteristic index based on the power grid data;
an evaluation module: and bringing the comprehensive weight into a pre-constructed comprehensive evaluation model of the characteristics of the power distribution network to evaluate the interactivity of the power distribution network.
12. The system according to claim 11, wherein the building module comprises:
a first building submodule: the method is used for constructing a first-level index according to the influence factors of the generalized load coupling interaction characteristics;
a second building submodule: the method is used for constructing a secondary index for each primary index based on the influence factors of the primary indexes;
a third building submodule: and constructing a third-level index for each second-level index based on the influence factors of the second-level indexes.
13. The system according to claim 12, wherein the evaluation module comprises:
a fourth building submodule: the system comprises a three-level index, a positive ideal value, a negative ideal value, a positive ideal system and a negative ideal system, wherein the maximum weight of the three-level index is set as the positive ideal value, the minimum weight of the three-level index is set as the negative ideal value, the positive ideal values of all the three-level indexes construct the positive ideal system, and the negative ideal values of all the three-level indexes construct the negative ideal system;
a first calculation submodule: the Euclidean distance calculation method is used for calculating the Euclidean distance of a positive ideal system and a negative ideal system of each three-level index according to a weighted distance formula;
a second calculation submodule: the method is used for calculating the queuing indicating value of the three-level index by using a queuing indicating value formula.
An evaluation submodule: and comprehensively evaluating the power grid interactivity by the stronger power distribution network interactive characteristic when the queuing indicated value is larger.
14. The system according to claim 11, wherein the determining module comprises:
a first determination sub-module: the objective weight of the index is determined by adopting an entropy method;
a second determination sub-module: the subjective weight used for determining the index by adopting a network analytic hierarchy process;
a third determination sub-module: and the method is used for determining the weight of the comprehensive evaluation model by adopting an additive integration method according to the objective weight of the index and the subjective weight of the index.
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| CN113177739A (en) * | 2021-05-26 | 2021-07-27 | 广东电网有限责任公司 | Power grid comprehensive evaluation method, device, equipment and medium |
| CN113469533A (en) * | 2021-07-02 | 2021-10-01 | 河海大学 | Power distribution network operation evaluation method containing comprehensive energy system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112001606A (en) * | 2020-08-10 | 2020-11-27 | 广西斯达市场信息咨询有限公司 | Environment-friendly work evaluation method and system |
| CN111949939A (en) * | 2020-08-26 | 2020-11-17 | 北京航空航天大学 | Intelligent electric meter running state evaluation method based on improved TOPSIS and cluster analysis |
| CN112215512A (en) * | 2020-10-22 | 2021-01-12 | 上海交通大学 | Comprehensive evaluation index weight quantification method and system considering functional characteristics of microgrid |
| CN113177739A (en) * | 2021-05-26 | 2021-07-27 | 广东电网有限责任公司 | Power grid comprehensive evaluation method, device, equipment and medium |
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Application publication date: 20200428 |