CN113393085A - Cluster dividing method considering flexibility supply and demand balance and response speed - Google Patents
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Abstract
The invention relates to a cluster partitioning method considering flexibility supply and demand balance and response speed. Firstly, the influence of wind-solar grid connection on a load is analyzed, and the capability of providing flexibility for a system by distributed power supplies and loads in a cluster is researched; secondly, providing a comprehensive index combining a flexibility supply and demand balance index, a response speed index and a modularity index of the distributed power supply cluster; and finally, dividing the distributed power supply cluster by adopting an improved genetic algorithm. Compared with the prior art, the method and the device can ensure the cluster structure, ensure the supply and demand balance of the flexible resources in the cluster, improve the autonomous capability of the cluster, give full play to the response characteristics of the flexible resources in the cluster and improve the capability of the cluster participating in the frequency modulation of the system.
Description
Technical Field
The invention relates to the technical field of distributed power supply regulation, in particular to a method for a distributed power supply to participate in frequency modulation and peak shaving of a system, and particularly relates to a cluster division method considering flexibility supply and demand balance and response speed.
Background
To achieve the carbon peak carbon neutralization goal, it has been a trend to vigorously develop renewable resources to replace non-renewable energy sources. Meanwhile, the access of large-scale renewable energy sources will bring great challenges to the planning and operation of the conventional power distribution network.
As the proportion of distributed power sources in the power grid continues to increase, it causes problems such as node voltage violations and reverse power transmission. The difficulty is increased for the frequency modulation, the voltage regulation and the peak regulation of the power grid. In addition, the uncertainty of the distributed power supply output increases the fluctuation of net load in a power grid, the flexibility requirement of the system is improved, and the difficulty of centralized control is greatly improved due to the fact that the distributed power supply has the characteristics of small single-machine capacity, scattered access points and various access modes. In order to enable the distributed power supply to be connected to a power grid more safely and reliably, the distributed power supply is divided into clusters, and scheduling and control are performed by taking the clusters as units, so that an effective solution is provided. However, at present, research on cluster division of distributed power supplies generally focuses on research on indexes, algorithms and the like of cluster division according to different requirements and control targets of power grid operation, but the problem that a cluster is responsible for frequency modulation and peak load regulation of a system is not researched. When the active power fluctuation of the system load is large, in order to enable the distributed power supply cluster to balance the load in the cluster and quickly respond to the frequency adjustment of the system, a cluster division method considering the flexibility supply and demand balance in the cluster and the response speed of the cluster needs to be researched, and the capability of the cluster participating in the frequency modulation and peak shaving of the system is improved.
Disclosure of Invention
The invention aims to provide a distributed power supply cluster division method for improving the capacity of a cluster participating in frequency modulation and peak shaving of a system, which integrates a cluster flexibility supply and demand balance index, a response speed index and a modularity index to carry out cluster division, thereby ensuring the cluster structure, ensuring the supply and demand balance of flexibility resources in a cluster, improving the autonomous capacity of the cluster, fully exerting the response characteristic of the flexibility resources in the cluster and improving the capacity of the cluster participating in frequency modulation of the system.
The purpose of the invention can be realized by the following technical scheme:
a method for partitioning a cluster considering flexibility supply and demand balance and response speed comprises the following steps:
1) adopting a genetic algorithm, inputting original data and initializing the genetic algorithm to generate an initial population, wherein the original data comprises network topology, line parameters, load data, distributed power output data and various flexible resource parameters;
2) performing flexible supply and demand matching calculation according to a flexible resource allocation strategy;
3) taking a result obtained by the flexibility supply and demand matching calculation as an input calculation cluster flexibility supply and demand balance index;
4) taking a result obtained by the flexible supply and demand matching calculation as an input calculation cluster response speed index;
5) load flow calculation is carried out according to input original data to obtain voltage sensitivity, and an electric distance is obtained through Euclidean distance calculation based on the node voltage sensitivity so as to calculate a cluster modularity index;
6) and integrating the cluster modularity index, the cluster flexibility supply and demand balance index and the cluster response speed index into a genetic algorithm fitness evaluation index, and performing cluster division by applying an elite reservation strategy.
Further, the flexibility resources are three flexibility resources that consider a conventional unit, an energy storage device, and an interruptible load, wherein the energy storage device provides a flexibility resource capability expression that:
in the formula: pmax,char,sn,Pmax,disc,snRespectively representing the maximum charging power and the maximum discharging power of the nth energy storage equipment sn in the cluster c;
respectively representing the maximum capacity and the minimum capacity of the nth energy storage equipment sn in the cluster c; etasnRepresenting the charge and discharge efficiency of the nth energy storage device sn in the cluster c;
the capability of providing flexibility for the interruptible loads is mainly determined by the proportion of the total amount of interruptible loads which can participate in load regulation at the time t to the loads which can actively participate in demand side response, and the specific expression is as follows:
in the formula: pdi(t) is the total amount of interruptible loads di of the ith load node in the cluster c at the moment t;
the proportion of the interruptible load di actively participating in demand response for the ith load node in the cluster c at the moment t; emax,diThe maximum load shedding electric quantity allowed by the interruptible load di is the ith load node in the cluster c;
the expression of the flexibility supply capacity of the up regulation and the down regulation of the conventional unit is as follows:
in the formula: τ represents a response time scale; pmax,cgn、Pmin,cgnRespectively representing the maximum and minimum output of the nth conventional unit cgn in the cluster c;
respectively representing the upward and downward climbing speeds of the nth conventional unit cgn in the cluster c; pcgn(t) represents the output of the nth conventional unit cgn in the cluster c at time t.
Further, the flexible resource allocation strategy is to maximize the response speed of the cluster, and if the cluster has a need to climb upwards at the time t, the resource with the lower climbing speed is preferentially added with output, and finally the energy storage output is increased; and if the cluster has a downward climbing demand at the time t, the stored energy output is preferentially reduced, and the output is preferentially reduced by the resource with higher climbing speed.
Further, the cluster flexibility supply and demand balance index expression is as follows:
in the formula: λ is the cluster flexibility balance index, NcThe number of total clusters, T the study period,
indicating the lack of flexibility of the c-th cluster during the study period,
representing the maximum of the flexibility deficit of cluster c during the study period.
Further, the cluster response speed index expression is as follows:
in the formula: gamma is a cluster flexibility requirement index, NcNumber of total clusters, T study period, kc(t) denotes the response speed of the cluster c at time t, max kc(t) } denotes the maximum value of the response speed of the cluster c in the study period,
representing the sum of the remaining capacity of all the energy storage devices within the cluster c during the study period,
representing the maximum value of the sum of the remaining capacities of all energy storage devices in cluster c during the study period.
Further, of cluster c at time tResponse speed kcThe expression (t) is as follows:
in the formula: pgn,left(ii) a responsive capacity for each resource; flexible resource responsibilities time tleft＝Pleft/R。
Further, each resource can respond to the capacity Pgn,leftThe expression is as follows:
in the formula: pmax,gnFor flexible resource gn maximum capacity, Pgn(t) is the output at time gn,
capacity is supplied for time t to balance gn flexibility for satisfying flexibility demand and supply.
Further, the cluster modularity index is calculated according to an electrical distance calculated based on an euclidean distance of a node voltage sensitivity, the electrical distance is calculated according to a voltage sensitivity between nodes, the voltage sensitivity is calculated according to a relationship between voltage variations of other nodes caused by a variation of active power of a certain node, and an expression is as follows:
in the formula: eiRepresented as the node i voltage; pjRepresenting the active power of node j;
representing the variation of the voltage of the node i caused by the active power variation of the node j; u shapeNRepresenting a rated voltage of the distribution network; riRepresenting the equivalent resistance between node i and node j;
the electrical distance is calculated based on the euclidean distance of the node voltage sensitivity, namely:
in the formula: dijIs the electrical distance between nodes i, j; sijIs the element of the ith row and the jth column in the sensitivity matrix;
the maximum value of the data in the jth column in the sensitivity matrix; n represents the total number of nodes of the power system network;
the method comprises the following steps of describing the electrical coupling degree among nodes by adopting a modularity definition mode based on electrical distance weight, and determining the optimal division of the system by measuring the overall modularity of the system, namely:
in the formula: rho represents a cluster modularity index; m is expressed as the sum of the network side weights; k is a radical ofiThe sum of the edge weights representing the edges connected to node i; k is a radical ofjRepresenting the sum of the edge weights of the edges connected to node j. When the node i and the node j are in one cluster, δ (i, j) is 1, otherwise δ (i, j) is 0.
Further, the genetic algorithm fitness evaluation index integrates the comprehensive goals of the cluster modularity index, the cluster activity supply and demand balance index and the cluster response speed index, and the objective function is shown as follows:
max{k1ρ+k2λ+k3γ}
in the formula: k is a radical of1、k2、k3Respectively, the weight given to three indexes, rho is the cluster modularity index, and lambda is the cluster flexibilityThe index of sexual balance, gamma is the demand index of cluster flexibility, k1The larger the cluster structure, the better k2The larger the size, the better the flexibility supply-demand balance within the cluster, k3The larger the cluster, the faster the cluster response speed.
Compared with the prior art, the invention has the following advantages:
(1) the cluster divided by combining the cluster flexibility balance degree index with the modularity index provided by the invention can ensure the cluster structure, namely the close connection in the cluster and the loose connection between the clusters, can ensure the supply and demand balance of the flexible resources in the cluster, and improve the autonomous ability of the cluster.
(2) The cluster divided by combining the cluster response speed index and the modularity index provided by the invention fully exerts the response characteristic of flexible resources in the cluster on the basis of ensuring the cluster structure, and improves the capability of the cluster participating in system frequency modulation.
Drawings
Fig. 1 is a schematic flow chart illustrating a cluster partitioning method considering power supply and demand balance and response speed in a distributed power supply cluster according to an embodiment of the present invention;
FIG. 2 is a topology diagram of an IEEE 33 node in an embodiment of the present invention;
fig. 3 shows the cluster partitioning result of the embodiment 4 of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Example (b):
as shown in fig. 1, an embodiment of the present invention provides a method for partitioning a cluster in consideration of flexibility supply-demand balance and response speed, where the method includes the following steps:
the method comprises the following steps of firstly, inputting original data such as network topology, line parameters, load data, distributed power output data and various flexible resource parameters by adopting a genetic algorithm, initializing the genetic algorithm, and generating an initial population.
In this embodiment, a cluster division study is performed on an IEEE 33 node system, a topological diagram of the IEEE 33 node system is shown in fig. 2, and a distributed power supply access situation in the system is shown in table 1, and includes two distributed power supplies, namely wind power and photovoltaic. Meanwhile, the system is provided with various types of flexible resource data as shown in table 2. The embodiment comprises 18 photovoltaic nodes, the total capacity is 2770kW, the total capacity is 470kW, and the capacity permeability of the distributed power supply is as high as 86.8%. And (3) performing cluster division calculation by adopting a genetic algorithm, setting the population number to be 100, setting the maximum iteration number to be 500, and ensuring the convergence of the algorithm by adopting an elite retention strategy.
TABLE 1 distributed Power Access situation within the System
TABLE 2 Flexible resource distribution in System
Step two, performing flexible supply and demand matching calculation according to a flexible resource allocation strategy;
in order to maximize the response speed of the cluster, the flexible resource allocation strategy is that if the cluster has a requirement of climbing upwards at the time t, the resource with the lower climbing speed is preferentially added with output, and finally the energy storage output is added; and if the cluster has a downward climbing requirement at the time t, preferentially reducing the stored energy output, and then preferentially reducing the output of the resource with higher climbing speed.
Step three, taking a result obtained by the flexibility supply and demand matching calculation as an input calculation cluster flexibility supply and demand balance index;
the flexible supply and demand balance index expression is as follows:
in the formula: λ is the flexibility balance index of the cluster, NcIs a total ofT is the study period.
Indicating the lack of flexibility of the c-th cluster during the study period,
representing the maximum of the flexibility deficit of cluster c during the study period.
Step four, taking the result obtained by the flexibility supply and demand matching calculation as an input calculation cluster response speed index;
wherein the cluster response speed index expression is as follows:
in the formula: gamma is the flexibility requirement index of the cluster, NcT is the study period, the number of total clusters. k is a radical ofc(t) represents the response speed of cluster c at time t. max kc(t) } represents the maximum value of the response speed of the cluster c during the study period.
Representing the sum of the remaining capacity of all the energy storage devices within the cluster c during the study period,
representing the maximum value of the sum of the remaining capacities of all energy storage devices in cluster c during the study period.
Wherein the cluster response speed expression is as follows:
in the formula: pgn,left(ii) a responsive capacity for each resource; flexible resource responsibilities time tleft＝Pleft/R。
Wherein the cluster responsibilities capacity expression is as follows:
in the formula, Pmax,gnFor flexible resource gn maximum capacity, Pgn(t) is the output at time gn,
capacity is supplied for time t to balance gn flexibility for satisfying flexibility demand and supply.
Performing load flow calculation according to input original data to obtain voltage sensitivity, and calculating an electrical distance based on Euclidean distance of the node voltage sensitivity to further calculate a cluster modularity index;
the cluster modularity index is calculated according to an electrical distance calculated according to an Euclidean distance based on node voltage sensitivity, and the electrical distance is mainly obtained according to the voltage sensitivity among the nodes. Obtaining the sensitivity according to the relation between the voltage variations of other nodes caused by the active power variation of a certain node, wherein the expression is as follows:
in the formula: eiRepresented as the node i voltage; pjRepresenting the active power of node j;
representing the variation of the voltage of the node i caused by the active power variation of the node j; u shapeNRepresenting a rated voltage of the distribution network; riRepresenting the equivalent resistance between node i and node j.
The electrical distance is calculated based on the euclidean distance of the node voltage sensitivity, namely:
in the formula: dijIs the electrical distance between nodes i, j; sijIs the element of the ith row and the jth column in the sensitivity matrix;
the maximum value of the data in the jth column in the sensitivity matrix; n represents the total number of nodes of the power system network.
The method comprises the following steps of describing the electrical coupling degree among nodes by adopting a modularity definition mode based on electrical distance weight, and determining the optimal division of the system by measuring the overall modularity of the system, namely:
in the formula: ρ represents the system modularity; m is expressed as the sum of the network side weights; k is a radical ofiThe sum of the edge weights representing the edges connected to node i; k is a radical ofjRepresenting the sum of the edge weights of the edges connected to node j. When the node i and the node j are in one cluster, δ (i, j) is 1, otherwise δ (i, j) is 0.
And step six, integrating the cluster modularity index, the cluster flexibility balance index and the cluster response speed index into a genetic algorithm fitness evaluation index, and performing cluster division by applying an elite reservation strategy.
In the genetic algorithm, the method comprises five steps of coding, fitness calculation, selection, crossing and mutation:
1. and (3) encoding: generating an initial population by randomly modifying 1 element in the matrix to be 0 to represent different cluster division results in an encoding mode based on an adjacent matrix representing the node connection condition;
2. and (3) fitness calculation: the designed index is used as a fitness function to evaluate the quality of a cluster division result, and different division indexes are set according to different control targets to flexibly divide the clusters;
3. selecting: selecting the offspring with better fitness function values in the population to carry out the next operation so as to ensure that the population evolves towards a better direction;
4. and (3) crossing: performing single-point crossing on the selected offspring according to the self-adaptive crossing probability to form a new population;
5. mutation: and (4) carrying out mutation on the new population obtained after crossing by using the self-adaptive mutation probability to form a new population.
The target function is shown as follows by combining the cluster modularity index, the cluster flexibility supply and demand balance index and the fitness evaluation index of the cluster response speed index:
max{k1ρ+k2λ+k3γ}
in the formula: k is a radical of1、k2、k3Weights given to three indexes, k1The larger the cluster size, the better the cluster structure, k2The larger the size, the better the flexibility supply-demand balance within the cluster, k3The larger the cluster response speed is.
Taking the IEEE 33 node system as an example, different partitioning schemes are made for verifying the validity of the index provided by the present invention. In the first scheme, cluster division is carried out only by adopting a modularity index; according to the scheme II, cluster division is carried out by integrating modularity and flexibility balance indexes; and thirdly, integrating the modularity and the cluster response speed index to perform cluster division. The fourth scheme is the cluster division scheme provided by the text, the modularity index, the flexibility balance index and the cluster response speed index are comprehensively considered, the priority target is the maximum participation of the cluster in the system power regulation, and the weights are respectively k1＝0.3，k2＝0.3，k3Cluster partitioning was performed at 0.4. The results of the division indexes of the respective schemes are shown in table 3.
TABLE 3 Cluster partition index results for each scheme
And comparing the cluster division results of the schemes with the scheme four, and verifying the effectiveness of the cluster division results of the comprehensive indexes provided by the text.
Compared with the scheme I only considering the modularity index, the modularity index value of the scheme I is increased by 11.1% compared with the comprehensive index provided by the scheme I, namely the cluster division result of the scheme I has better cluster structure, but the cluster flexibility balance degree and the response speed are respectively reduced by 15.8% and 38.7% compared with the scheme provided by the scheme.
Compared with the second scheme considering the modularity index and the flexibility balance index, the modularity index value of the second scheme is increased by 6.7% compared with the comprehensive index provided by the text, the cluster flexibility balance index value is increased by 13.1%, but the response speed index value is reduced by 48.5%, namely the cluster structure of the second scheme is better, the cluster flexibility shortage is smaller, but the improvement is not obvious compared with the scheme in the text, and the cluster response speed is greatly reduced compared with the scheme in the text, so that the cluster is not beneficial to participating in the power adjustment of the system.
Compared with the third scheme which considers the modularity index and the response speed index, the cluster modularity index value, the flexibility balance index value and the response speed index value of the third scheme are respectively reduced by 0.75%, 15.79% and 5.8%.
Based on the analysis, the comprehensive index of cluster division provided by the invention meets the cluster structure, improves the flexibility supply and demand balance capability in the cluster, gives full play to the cluster autonomy capability, gives consideration to the power regulation speed of the cluster response system, and provides a theoretical basis for the cluster auxiliary regulation system to regulate the frequency fluctuation.
The cluster division result of the fourth scheme for performing cluster division by using the cluster division method provided by the invention is shown in fig. 3.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A cluster partitioning method considering flexibility supply and demand balance and response speed is characterized in that: the method comprises the following steps:
1) adopting a genetic algorithm, inputting original data and initializing the genetic algorithm to generate an initial population, wherein the original data comprises network topology, line parameters, load data, distributed power supply output data and various flexible resource parameters;
2) performing flexible supply and demand matching calculation according to a flexible resource allocation strategy;
3) taking a result obtained by the flexibility supply and demand matching calculation as an input calculation cluster flexibility supply and demand balance index;
4) taking a result obtained by the flexible supply and demand matching calculation as an input calculation cluster response speed index;
5) load flow calculation is carried out according to the original data to obtain voltage sensitivity, and an electric distance is obtained through Euclidean distance calculation based on the node voltage sensitivity so as to calculate the index of the cluster modularity;
6) and integrating the cluster modularity index, the cluster flexibility supply and demand balance index and the cluster response speed index into a genetic algorithm fitness evaluation index, and applying an elite reservation strategy to carry out cluster division.
2. The method for partitioning a cluster considering power supply and demand balance and response speed in a distributed power cluster according to claim 1, wherein the flexible resources are three flexible resources considering a conventional unit, an energy storage device and an interruptible load, wherein the energy storage device provides a flexible resource capacity expression as follows:
in the formula: pmax,char,sn,Pmax,disc,snRespectively representing the maximum charging power and the maximum discharging power of the nth energy storage equipment sn in the cluster c;
respectively representing the maximum capacity and the minimum capacity of the nth energy storage equipment sn in the cluster c; etasnRepresenting the charge and discharge efficiency of the nth energy storage device sn in the cluster c;
the capability of providing flexibility for the interruptible loads is mainly determined by the proportion of the total amount of interruptible loads which can participate in load regulation at the time t to the loads which can actively participate in demand side response, and the specific expression is as follows:
in the formula: pdi(t) is the total amount of interruptible loads di of the ith load node in the cluster c at the moment t;
the proportion of the interruptible load di actively participating in demand response for the ith load node in the cluster c at the moment t; emax,diThe maximum load shedding electric quantity allowed by the interruptible load of the ith load node in the cluster c;
the expression of the flexibility supply capacity of the up regulation and the down regulation of the conventional unit is as follows:
in the formula: τ represents a response time scale; pmax,cgn、Pmin,cgnRespectively representing the maximum and minimum output of the nth conventional unit cgn in the cluster c;
respectively represents the upward and downward climbing speeds of the nth conventional unit cgn in the cluster c; pcgn(t) represents the output of the nth conventional unit cgn in the cluster c at time t.
3. The method according to claim 1, wherein the flexible resource allocation strategy is to increase output by a resource with a lower climbing speed preferentially and increase output by an energy storage output by a cluster with a lower climbing speed if the cluster has an upward climbing demand at time t in order to maximize the cluster response speed; and if the cluster has a downward climbing requirement at the time t, preferentially reducing the stored energy output, and then preferentially reducing the output of the resource with higher climbing speed.
4. The method for partitioning a cluster considering power supply and demand balance and response speed in a distributed power supply cluster according to claim 1, wherein the cluster flexibility supply and demand balance index expression is as follows:
in the formula: λ is the cluster flexibility balance index, NcThe number of total clusters, T the study period,
indicating the lack of flexibility of the c-th cluster during the study period,
representing the maximum of the flexibility deficit of cluster c during the study period.
5. The method for partitioning a cluster considering power supply and demand balance and response speed in a distributed power supply cluster according to claim 1, wherein the cluster response speed index expression is as follows:
in the formula: gamma is a cluster flexibility requirement index, NcNumber of total clusters, T study period, kc(t) denotes the response speed of the cluster c at time t, max kc(t) } denotes the maximum value of the response speed of the cluster c during the study period,
representing the sum of the remaining capacities of all energy storage devices within cluster c during the study period,
representing the maximum value of the sum of the remaining capacities of all energy storage devices in cluster c during the study period.
6. The method for partitioning a cluster considering power supply and demand balance and response speed in a distributed power cluster as claimed in claim 5, wherein the response speed k of the cluster c at time t iscThe expression (t) is as follows:
in the formula: pgn,left(ii) a responsive capacity for each resource; flexible resource responsibilities time tleft＝Pleft/R。
7. The method of claim 6 wherein each resource responsive capacity P is a capacity of a cluster of a distributed power supply and demand balance and response speedgn,leftThe expression is as follows:
in the formula, Pmax,gnFor flexible resource gn maximum capacity, Pgn(t) is the output at time gn,
capacity is supplied for time t to balance gn flexibility for satisfying flexibility demand and supply.
8. The method for dividing a cluster considering power supply and demand balance and response speed in a distributed power cluster according to claim 1, wherein the cluster modularity index is calculated according to an electrical distance calculated according to an euclidean distance based on node voltage sensitivity, the electrical distance is obtained from voltage sensitivity among nodes, and the voltage sensitivity is obtained according to a relationship between voltage variations of other nodes caused by variations of active power of a certain node, and an expression is as follows:
in the formula: eiRepresented as the node i voltage; pjRepresenting the active power of node j;
representing the variation of the voltage of the node i caused by the active power variation of the node j; u shapeNRepresenting a rated voltage of the distribution network; riRepresenting the equivalent resistance between node i and node j;
the electrical distance is calculated based on the euclidean distance of the node voltage sensitivity, namely:
in the formula: dijIs the electrical distance between nodes i, j; sijIs the element of the ith row and the jth column in the sensitivity matrix;
the maximum value of the data in the jth column in the sensitivity matrix; n represents the total number of nodes of the power system network;
the method adopts a modularity definition mode based on electrical distance weight to describe the electrical coupling degree among nodes, and determines the optimal division of the system by measuring the overall modularity of the system, namely:
in the formula: rho represents a cluster modularity index; m is expressed as the sum of the network side weights; k is a radical ofiThe sum of the edge weights representing the edges connected to node i; k is a radical ofjAnd representing the sum of the edge weights of the edges connected with the node j, wherein when the node i and the node j are in one cluster, the delta (i, j) is 1, and otherwise, the delta (i, j) is 0.
9. The method for partitioning a cluster in consideration of power supply and demand balance and response speed in a distributed power supply cluster according to claim 1, wherein the genetic algorithm fitness evaluation index integrates a comprehensive objective of a cluster modularity index, a cluster flexibility supply and demand balance index and a cluster response speed index, and an objective function is as follows:
max{k1ρ+k2λ+k3γ}
in the formula: k is a radical of1、k2、k3The weight given to three indexes is respectively, rho is the index of cluster modularity, lambda is the index of cluster flexibility balance, gamma is the index of cluster flexibility requirement, k1The larger the cluster structure, the better k2The larger the size, the better the flexibility supply-demand balance within the cluster, k3The larger the cluster, the faster the cluster response speed.
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