CN108448620A - High permeability distributed generation resource assemblage classification method based on integrated performance index - Google Patents
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Abstract
The invention discloses the high permeability distributed generation resource assemblage classification methods based on integrated performance index, are related to the distribution network planning and control technology field of renewable energy source current, the index system of assemblage classification and the efficient algorithm of assemblage classification；The index definition of assemblage classification is integrated performance index, and integrated performance index includes the reactive balance degree index of modularity index ρ based on electrical distance, clusterWith the active balance degree index of clusterFor the objective requirement of the calculation expression and assemblage classification of adaptation integrated performance index system, the efficient algorithm of assemblage classification is to carry out distributed generation resource assemblage classification using genetic algorithm, simultaneously, improve basic genetic algorithmic, the coding mode of chromosome is devised according to the syntople of network, and uses adaptive crossover and mutation probability.The invention has the advantages that：The capacity of self-government that the complementarity and cluster between node can be given full play to, is beneficial to the consumption to extensive regenerative resource and control.
Description
Technical Field
The invention relates to the technical field of planning and control of a power distribution network of a renewable energy power source, in particular to a high-permeability distributed power source cluster division method based on comprehensive performance indexes.
Background
The increasing demand for renewable energy and the concern of people about environmental issues have prompted the rapid development of renewable energy power generation. Particularly, in a power distribution network in a remote area, along with further strengthening of a national poverty alleviation policy of new energy, a large amount of distributed renewable energy is accessed into a power grid, and the situation that the permeability is greater than 100% already occurs in part of areas, so that huge challenges are brought to planning, operation and control of a local power grid. The common distributed renewable energy regulation and control modes at present mainly include a microgrid mode, a centralized control mode and a cluster control mode. In a low-voltage power distribution network in a remote area, because the capacity of a single machine accessed by a renewable energy power supply is small, the quantity of the single machine is large, the geographic positions are scattered, a micro-grid and a centralized control mode are difficult to operate, and a regulation and control mode based on a cluster can fully utilize the autonomous characteristic of the cluster, ensure that large-scale distributed power generation is orderly, reliably and efficiently accessed to the power grid, and become an important solution for large-scale renewable energy grid connection.
The term cluster is from the field of computer science, and is a series of computer clusters which work independently but are connected through a high-speed network, and the computer clusters can be regarded as a whole by an upper layer to be managed, so that higher performance and reliability can be obtained under the condition that the overall cost of the system is lower. In an electrical power system, a cluster may be defined as: the working group is composed of a series of devices, and can be operated independently and can also be coordinated with each other. The cluster is an integral body, has a common target, receives single instruction control and is convenient to dispatch and manage; in the cluster, all the devices cooperate with each other to finish a common target, and the cooperation capability of the devices is effectively exerted.
In recent years, research and use of clusters in the field of power systems have attracted attention, and application scenarios of cluster division mainly include two fields: system planning and scheduling control. From the prior research results, most of the research work is focused on scheduling control, such as: the research of comprehensively considering the operation control behavior of the system can be divided into two problems: (1) a criterion and index system for cluster division; (2) efficient algorithm and implementation of cluster partitioning. The current division judgment takes the coupling of the clusters as an index, namely the clusters are closely related internally and the clusters are sparsely related; the partitioning algorithm can be divided into three categories: cluster analysis, community discovery of complex networks, and optimization algorithms. The simplest, intuitive cluster partitioning can be done based on geographical location or administrative area, but this partitioning is too coarse. To this end, the following types of cluster partitions occur: the Euclidean distance between nodes is used as an index, and the solution of the position of the distributed power supply in the power distribution network planning is simplified by using a hierarchical merging clustering algorithm; taking the distance between nodes in a cluster and between clusters as a basis, and obtaining a system operation management cluster through hierarchical clustering analysis; dividing control partitions by using a fuzzy clustering method, based on the voltage amplitude to sensitively define the electrical distance between nodes of reactive power, calculating dynamic partitions by using a transmission closure, and finally calculating statistic F to obtain optimal classification; dividing voltage control partitions by using a community mining technology of a complex network, and evaluating the partition quality of a power grid by using modularity as an evaluation index; from the perspective of digital optimization, the power network partition is regarded as a combined optimization problem, and voltage control partition and the like are realized by applying a Tabu search method.
In summary, the existing cluster partitioning method is based on a single index, partitions a certain process in planning, operation and control of a system, and lacks theoretical support and comprehensive performance index of the system for the planning cluster partitioning considering operation and control.
Disclosure of Invention
The invention aims to solve the technical problem that the conventional cluster division method is based on a single index, divides a certain process in planning, operation and control of a system, and lacks theoretical support and comprehensive performance indexes of the system for the planning cluster division considering the operation and the control.
The invention solves the technical problems through the following technical scheme, and the specific technical scheme is as follows:
the high-permeability distributed power cluster division method based on the comprehensive performance indexes comprises the following steps: an index system of cluster division and an effective algorithm of cluster division; the index of the cluster division is defined as a comprehensive performance index, and the comprehensive performance index comprises a modularity index rho based on the electrical distance and a reactive power balance index of the clusterAnd the active balance index of the clusterThe effective algorithm of the cluster division is to divide the distributed power supply cluster by using a genetic algorithm; the high-permeability distributed power supply cluster division method comprises the following steps:
s1, calculating the modularity index rho, wherein the modularity index rho is expressed as follows:
where e is a matrix of weights of edges, element eijA weight of an edge connecting node i and node j;is the sum of the side weights of all sides of the network;represents the sum of the weights of all edges connected to node i;represents the sum of the weights of all edges connected to node j; δ (i, j) ═ 1 indicates that the node i and the node j are in the same cluster, and δ (i, j) ═ 0 indicates that the node i and the node j are not in the same cluster;
s2, calculating the reactive balance indexThe index of reactive power balanceIs represented as follows:
in the formula, QiThe reactive power balance degree of the ith cluster; c is the number of clusters; qiThe calculation method of' is as follows:
in the formula, nCkIs the CkThe number of nodes in each cluster; qsup,iMaximum value provided for reactive power of node i, including reactive power Q provided by reactive compensation device of node icAnd reactive power Q that the inverter can providetI.e. Qsup,i＝Qc+QtWherein the maximum reactive power Q that the inverter can providet maxExpressed as:
in the formula,is the maximum power factor angle of the inverter; t represents a certain moment in a typical time scene, which can be determined as required; ptThe active power output of the inverter at the moment t; smaxIs the inverter maximum capacity; qt maxThe maximum reactive power which can be output by the inverter at the moment t;Pcut、Pmaxcutting power into the inverter; qneed,iFor a value of the demand for reactive power of node i, which includes the normal reactive demand Q of the nodeNMinimum reactive power required for node overvoltageWherein Q isVThe minimum reactive power required to regulate node i; Δ ViIs the voltage variation of node i; sVQ,iiFor the reactive voltage sensitivity of node i with respect to itself, then Qneed,i＝QN+QV；
S3: calculating the active balance indexThe active power balance indexIs represented as follows:
in the formula, PiIs the active balance degree, P, of the ith clusterclu(t)iIs the net power value for cluster i in a typical time scenario, denoted as [ Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i]The power value is obtained by adding power values of all nodes under a typical time scene; t represents the number of time points T in a typical time scene; c is the number of clusters;
s4: and (4) carrying out distributed power supply cluster division by utilizing a genetic algorithm.
More specifically, the step of dividing the distributed power cluster by using a genetic algorithm in S4 is as follows:
① taking the division mode of the cluster division as a solution, wherein one solution is an individual, and encoding the individual according to the established encoding mode;
② the genetic algorithm starts with the N individuals as initial pointsIteration, calculating the fitness value of each individual according to a fitness calculation mode, wherein the fitness value is a comprehensive performance index value gamma, and the comprehensive performance index value gamma is as follows:wherein λ is1、λ2、λ3The weight is set according to the requirement;
③ selecting, crossing and mutating according to the algorithm flow of the genetic algorithm;
④ repeating ② and ③ until reaching the maximum genetic iteration times, and solving the individual with the maximum fitness value in the obtained population, namely obtaining the optimal cluster division.
More specifically, the weight e of the edge connecting the node i and the node j in S1ijObtained by calculation as follows:
under the section of time t, the expression of the conventional power flow equation is as follows:
in the formula, delta, delta V, delta P and delta Q are respectively power angle, voltage, active power and reactive power change increment of each node of the power distribution network under a typical day integral point time t section, and are n-dimensional vectors, and n is the number of the nodes to be divided; sδP、SVP、SVQ、SδQRespectively representing a power angle active sensitivity coefficient matrix, a voltage reactive sensitivity coefficient matrix and a power angle reactive sensitivity coefficient matrix under the section at the moment t; matrix SVQRow i and column j of the middleVQ,ijRepresenting the unit value of the reactive power change of the node j corresponds to the change value of the voltage of the node i, then SVQ,iiRepresenting the change value of the voltage of the node i corresponding to the unit value of the reactive power change of the node i; let the electrical distance between nodes be L, and the electrical distance between node i and node j be LijComprises the following steps:
then, the weight e of the edge is reflected in the electrical distance L between the nodes, wherein the weight e of the edge of the node i and the node jijExpressed as:
eij＝1-Lij/max(L)。
more specifically, the determination of a certain time t of the typical time scenario in S2 is calculated as follows:
performing correlation calculation at the time of highest permeability of renewable energy output in a typical time scene, namely R (t) ═ Pre(t)/Pload(t) maximum, where R (t) represents the permeability of the renewable energy source, Pre(t) represents the output value of renewable energy at time t, Pload(t) represents the required value of the load at time t.
More specifically, the predetermined encoding method in S4 is performed as follows:
the power network can be seen as a graph consisting of points and edges, the number of the edges in the graph is counted as x, and an x-bit gene is constructed; each digit of the gene represents a certain edge in the network, the parameter of each digit represents the connection state of the corresponding edge, and the parameters only comprise 0 and 1, wherein 0 represents the disconnection of the corresponding edge, and 1 represents the connection of the corresponding edge;
the coding mode is as follows: constructing an initial gene according to the connection state of the power network, randomly sampling all bits in the initial gene, modifying all parameters in the selected bits to be 0, and indicating that the corresponding edge is changed from connection to disconnection, namely that two nodes at two ends of the corresponding edge are changed from connection to disconnection;
and forming a new gene after sampling, wherein the new gene is an encoded individual and also represents a cluster division result.
More specifically, the crossover and mutation probabilities of the genetic algorithm in S4 are determined as follows:
in the formula, Pc、PmRespectively representing cross probability and mutation probability; pc_max、Pc_min、Pm_max、Pm_minRespectively representing maximum cross probability, minimum cross probability, maximum mutation probability and minimum mutation probability; f' represents the larger fitness value of the two individuals needing to be subjected to the crossover operation; f represents the adaptability value of the individual needing mutation operation; f. ofavgThe mean fitness value of the population is represented.
Compared with the prior art, the invention has the following advantages:
1. the method comprehensively considers the relevant indexes of node electrical connection and power balance in the cluster, and the electrical connection is tight in the nodes in the cluster and loose in the connection between the clusters, so that the operation management of the clusters is facilitated; in power balance, the cluster has certain reactive power supply capacity, so that the cluster has certain self-regulation capacity when the node voltage exceeds the limit, and meanwhile, in the external characteristics of the cluster, the division takes characteristic complementation between nodes as a principle, so that the self-consumption capacity of the cluster on renewable energy sources can be fully exerted, and subsequent cluster planning and cluster regulation and control are facilitated.
2. The cluster division is carried out by adopting an improved genetic algorithm, so that the expression of comprehensive performance indexes is adapted; compared with the conventional partitioning algorithm, the genetic algorithm is a global optimization algorithm, and the global search capability of the genetic algorithm can ensure that the genetic algorithm is gradually close to the global optimal solution along with the increase of the iteration times.
3. Coding is carried out on the basis of the adjacency relation among the nodes, so that cluster division and a genetic algorithm are combined together, and the logic rationality of a cluster division result is ensured, namely the cluster division meets the requirements of a network structure, and isolated nodes do not exist in the cluster. In addition, according to the specific iterative process of the algorithm, the self-adaptively changed cross mutation probability is adopted, so that the optimal solution searching capability and the convergence rate of the algorithm are optimized to a certain extent.
4. The time-varying typical output characteristic of the node in a certain time period replaces a fixed value used in the traditional cluster division at a certain time, so that the cluster division is closely related to the characteristics of the node, and the division result is more reasonable.
Drawings
Fig. 1 is a flowchart illustrating steps of a high permeability distributed power cluster partitioning method based on an overall performance index according to an embodiment of the present invention.
Fig. 2 is an encoding method of a high-permeability distributed power cluster partitioning method based on an integrated performance index according to an embodiment of the present invention.
Fig. 3 is a flowchart of the distributed power cluster division using the genetic algorithm according to the embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1, the cluster partitioning method of the high permeability distributed power source cluster partitioning method based on the comprehensive performance index of the present invention is divided into: an index system of cluster division and an effective algorithm of cluster division; the index of cluster division is defined as the comprehensive performance index, and the comprehensive performance index comprises a modularity index rho and a reactive power balance index of the clusterAnd the active balance index of the clusterThe effective algorithm of cluster division is to use a genetic algorithm to divide the distributed power supply clusters; the high-permeability distributed power supply cluster division method comprises the following steps:
s1, calculating the modularity index as rho, wherein the modularity index rho is expressed as follows:
where e is a matrix of weights of edges, element eijFor the weight of the edge connecting node i and node j, e.g. the weight takes 1, e when node i and node j are directly connectedijWhen not connected, eij0, weight eijOther values can be set according to requirements;is the sum of the side weights of all sides of the network;represents the sum of the weights of all edges connected to node i;represents the sum of the weights of all edges connected to node j; δ (i, j) ═ 1 indicates that the node i and the node j are in the same cluster, and δ (i, j) ═ 0 indicates that the node i and the node j are not in the same cluster.
Wherein the weight e of the edge connecting node i and node jijObtained by calculation as follows:
under the section of time t, the expression of the conventional power flow equation is as follows:
in the formula, delta, delta V, delta P and delta Q are respectively power angle, voltage, active power and reactive power change increment of each node of the power distribution network under a typical day integral point time t section, and are n-dimensional vectors, and n is the number of the nodes to be divided; sδP、SVP、SVQ、SδQRespectively representing a power angle active sensitivity coefficient matrix, a voltage reactive sensitivity coefficient matrix and a power angle reactive sensitivity coefficient matrix under the section at the moment t; matrix SVQRow i and column j of the middleVQ,ijRepresenting the unit value of the reactive power change of the node j corresponds to the change value of the voltage of the node i, then SVQ,iiRepresenting the change value of the voltage of the node i corresponding to the unit value of the reactive power change of the node i; let the electrical distance between nodes be L, and the electrical distance between node i and node j be LijComprises the following steps:
then, the weight e of the edge is reflected in the electrical distance L between the nodes, wherein the weight e of the edge of the node i and the node jijExpressed as:
eij＝1-Lij/max(L)。
s2 calculating the index of reactive balanceIndex of reactive power balanceIs represented as follows:
wherein,Qithe reactive power balance degree of the ith cluster; c is the number of clusters; qiThe calculation method of' is as follows:
in the formula, nCkIs the CkThe number of nodes in each cluster; qsup,iMaximum value provided for reactive power of node i, including reactive power Q provided by reactive compensation device of node icAnd reactive power Q that the inverter can providetI.e. Qsup,i＝Qc+QtWherein the maximum reactive power Q that the inverter can providet maxExpressed as:
wherein,is the maximum power factor angle of the inverter; t represents a certain moment in a typical time scene, the typical time scene can be determined as required, a typical day, a typical year and the like can be selected, and correlation calculation is carried out at the moment of highest permeability of renewable energy output under the typical time scene, namely R (t) ═ Pre(t)/Pload(t) maximum, where R (t) represents the permeability of the renewable energy source, Pre(t) represents the output value of renewable energy at time t, Pload(t) represents the required value of the load at time t; ptThe active power output of the inverter at the moment t; smaxIs the inverter maximum capacity; qt maxThe maximum reactive power which can be output by the inverter at the moment t;Pcut、Pmaxcutting power into the inverter; qneed,iFor the required value of node i reactive power, node i reactive powerThe demand value of (A) includes the normal reactive demand Q of the nodeNAnd the minimum reactive power required for adjusting the node overvoltage when the permeability of the renewable energy source output is overhighWherein Q isVThe minimum reactive power required to regulate node i; Δ ViIs the voltage variation of node i; sVQ,iiFor the reactive voltage sensitivity of node i with respect to itself, then Qneed,i＝QN+QV；
S3: calculating the active power balance indexIndex of active power balanceIs represented as follows:
in the formula, PiIs the active balance degree, P, of the ith clusterclu(t)iIs the net power value for cluster i in a typical time scenario, denoted as [ Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i]The power value is obtained by adding power values of all nodes under a typical time scene; t represents the number of time points T in a typical time scene; c is the number of clusters;
s4: and (3) carrying out distributed power supply cluster division by using a genetic algorithm:
① taking the division mode of the cluster division as a solution, wherein one solution is an individual, and encoding the individual according to the established encoding mode;
② the genetic algorithm starts iteration with the N individuals as initial points, and calculates the fitness value of each individual according to a fitness calculation mode, wherein the fitness value is a comprehensive performance index value gamma which is:wherein λ is1、λ2、λ3The weight is set according to the requirement;
③ selecting, crossing and mutating according to the algorithm flow of the genetic algorithm;
④ repeating ② and ③ until reaching the maximum genetic iteration times, and solving the individual with the maximum fitness value in the obtained population, namely obtaining the optimal cluster division.
Wherein, the predetermined coding method is as follows: the power network can be seen as a graph consisting of points and edges, the number of the edges in the graph is counted as x, and an x-bit gene is constructed; each gene represents a certain edge in the network, and the parameter of each bit represents the connection state of the corresponding edge, and only comprises parameters 0 and 1, wherein 0 represents that the corresponding edge is disconnected, and 1 represents that the corresponding edge is connected; the coding method comprises the following steps: constructing an initial gene according to the connection state of the power network, randomly sampling all bits in the initial gene, modifying all parameters in the selected bits to be 0, and indicating that two nodes are disconnected from connection; after sampling, a new gene is formed, the gene is an encoded individual and also represents a cluster division result. For example, as shown in FIG. 2, the number of sides in the figure is 3, a 3-bit gene is constructed, three sides are represented by numerals 1,2,3, the initial gene is constructed according to the network connection state, the initial gene is [ 111 ], then random sampling is performed, the side with the number of 3 is drawn, the parameter in the selected bit is modified to 0, and after coding, the value is [ 110 ].
Wherein, the crossover and mutation probability of the genetic algorithm is determined as follows:
in the formula, Pc、PmRespectively representing cross probability and mutation probability; pc_max、Pc_min、Pm_max、Pm_minRespectively representing maximum cross probability, minimum cross probability, maximum mutation probability and minimum mutation probability; f' represents the larger fitness value of the two individuals needing to be subjected to the crossover operation; f represents the adaptability value of the individual needing mutation operation; f. ofavgThe mean fitness value of the population is represented.
A flow chart for dividing a distributed power supply cluster by using a genetic algorithm is shown in fig. 3, node parameters are input, coding is carried out on the basis of an adjacent matrix to construct an initial population, after the initial population is constructed, individual fitness is calculated, an optimal individual is stored, whether circulation is finished or not is judged, if the circulation is finished, the optimal individual is output, and decoding is carried out to obtain a cluster division result; if the circulation is not finished, selecting individuals, crossing and mutating, calculating the individual fitness and storing the best individual, judging whether the circulation is finished again after calculation, if the circulation finishing condition is not met, continuing the circulation, and if the circulation finishing condition is met, not repeating the process; the final result is to output the best individual and decode to obtain the cluster division result.
The invention provides a dividing method for comprehensively considering the electrical connection and the power balance degree in a cluster by taking the power distribution network plan containing high-permeability distributed renewable energy as an application scene, wherein the electrical connection of the cluster is represented by a modularity index rho based on an electrical distance, and the power balance degree is represented by the reactive power balance degree of the clusterAnd degree of active balanceAs an index, a comprehensive performance index system is constructed. Meanwhile, in order to adapt to the objective requirements of calculation expression and cluster division of a comprehensive performance index system, the basic genetic algorithm is improved, a coding mode of chromosomes is designed according to the adjacency relation of the network, and the self-adaptive cross mutation probability is adopted. The new cluster division idea provided by the invention can fully exert the complementarity between the nodes and the autonomous capability of the cluster, and is beneficial to the consumption and control of large-scale renewable energy sources.
The method selects the low-voltage distribution network plan containing the high-permeability distributed renewable energy power generation as an application scene, aims to give full play to the autonomous capacity of the cluster, deeply discusses the cluster division criterion, index and algorithm implementation process, comprehensively considers the complementarity and the relevance among the nodes in the cluster, and ensures the coupling relation and the voltage regulation capacity among the nodes on the basis of ensuring the reasonable matching of the loads in the cluster. Furthermore, the method always satisfies the following general principles: 1. logic principle: isolated nodes do not exist in the clusters, coincident nodes do not exist among the clusters, and connectivity must be ensured among all the nodes; 2. the structure principle is as follows: on the geographic space or the electrical coupling, the connection inside the clusters is tight, and the connection among the clusters is sparse; 3. functional principle: the characteristics of the cluster are comprehensively expressed by the characteristics of all nodes in the cluster, and the nodes in the cluster have good cooperation capability.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. The high-permeability distributed power cluster division method based on the comprehensive performance indexes is characterized by comprising the following steps of: an index system of cluster division and an effective algorithm of cluster division; the index of the cluster division is defined as a comprehensive performance index, and the comprehensive performance index comprises a modularity index rho based on the electrical distance and a reactive power balance index of the clusterAnd the active balance index of the clusterThe effective algorithm of the cluster division is to divide the distributed power supply cluster by using a genetic algorithm; the high-permeability distributed power supply cluster division method comprises the following steps:
s1: calculating the modularity index rho, wherein the modularity index rho is expressed as follows:
where e is a matrix of weights of edges, element eijA weight of an edge connecting node i and node j;is the sum of the side weights of all sides of the network;represents the sum of the weights of all edges connected to node i;represents the sum of the weights of all edges connected to node j; δ (i, j) ═ 1 indicates that the node i and the node j are in the same cluster, and δ (i, j) ═ 0 indicates that the node i and the node j are not in the same cluster;
s2: calculating the index of reactive power balanceThe index of reactive power balanceIs represented as follows:
in the formula, QiThe reactive power balance degree of the ith cluster; c is a clusterThe number of the cells; qiThe calculation method of' is as follows:
in the formula, nCkIs the CkThe number of nodes in each cluster; qsup,iMaximum value provided for reactive power of node i, including reactive power Q provided by reactive compensation device of node icAnd reactive power Q that the inverter can providetI.e. Qsup,i＝Qc+QtWherein the maximum reactive power Q that the inverter can providet maxExpressed as:
in the formula,is the maximum power factor angle of the inverter; t represents a certain moment in a typical time scene, which can be determined as required; ptThe active power output of the inverter at the moment t; smaxIs the inverter maximum capacity; qt maxThe maximum reactive power which can be output by the inverter at the moment t;Pcut、Pmaxcutting power into the inverter; qneed,iFor a value of the demand for reactive power of node i, which includes the normal reactive demand Q of the nodeNMinimum reactive power required for node overvoltageWherein Q isVThe minimum reactive power required to regulate node i; Δ ViIs the voltage variation of node i; sVQ,iiFor the reactive voltage sensitivity of node i with respect to itself, then Qneed,i＝QN+QV；
S3: calculating the active balance indexThe active power balance indexIs represented as follows:
in the formula, PiIs the active balance degree, P, of the ith clusterclu(t)iIs the net power value for cluster i in a typical time scenario, denoted as [ Pclu(1)i,Pclu(2)i,…,Pclu(t)i,…,Pclu(T)i]The power value is obtained by adding power values of all nodes under a typical time scene; t represents the number of time points T in a typical time scene; c is the number of clusters;
s4: and (4) carrying out distributed power supply cluster division by utilizing a genetic algorithm.
2. The method for partitioning a high-permeability distributed power supply cluster based on the comprehensive performance index according to claim 1, wherein the step of partitioning the distributed power supply cluster by using a genetic algorithm in S4 is as follows:
① taking the division mode of the cluster division as a solution, wherein one solution is an individual, and encoding the individual according to the established encoding mode;
② the genetic algorithm starts iteration with the N individuals as initial points, and calculates the fitness value of each individual according to a fitness calculation mode, wherein the fitness value is a comprehensive performance index value gamma which is:wherein λ is1、λ2、λ3The weight is set according to the requirement;
③ selecting, crossing and mutating according to the algorithm flow of the genetic algorithm;
④ repeating ② and ③ until reaching the maximum genetic iteration times, and solving the individual with the maximum fitness value in the obtained population, namely obtaining the optimal cluster division.
3. The method for partitioning high-permeability distributed power source clusters based on comprehensive performance indicators of claim 1, wherein the weight e of the edge connecting the node i and the node j in S1 isijObtained by calculation as follows:
under the section of time t, the expression of the conventional power flow equation is as follows:
in the formula, delta, delta V, delta P and delta Q are respectively power angle, voltage, active power and reactive power change increment of each node of the power distribution network under a typical day integral point time t section, and are n-dimensional vectors, and n is the number of the nodes to be divided; sδP、SVP、SVQ、SδQRespectively representing a power angle active sensitivity coefficient matrix, a voltage reactive sensitivity coefficient matrix and a power angle reactive sensitivity coefficient matrix under the section at the moment t; matrix SVQRow i and column j of the middleVQ,ijRepresenting the unit value of the reactive power change of the node j corresponds to the change value of the voltage of the node i, then SVQ,iiRepresenting the change value of the voltage of the node i corresponding to the unit value of the reactive power change of the node i; let the electrical distance between nodes be L, and the electrical distance between node i and node j be LijComprises the following steps:
then, the weight e of the edge is reflected in the electrical distance L between the nodes, wherein the weight e of the edge of the node i and the node jijExpressed as:
eij＝1-Lij/max(L)。
4. the method for partitioning a high-permeability distributed power source cluster based on an integrated performance index according to claim 1, wherein the determination of a certain time t of the typical time scenario in S2 is obtained by calculating as follows:
performing correlation calculation at the time of highest permeability of renewable energy output in a typical time scene, namely R (t) ═ Pre(t)/Pload(t) maximum, where R (t) represents the permeability of the renewable energy source, Pre(t) represents the output value of renewable energy at time t, Pload(t) represents the required value of the load at time t.
5. The method for partitioning a high-permeability distributed power cluster based on an integrated performance indicator of claim 2, wherein the predetermined coding manner in S4 is performed as follows:
the power network can be seen as a graph consisting of points and edges, the number of the edges in the graph is counted as x, and an x-bit gene is constructed; each digit of the gene represents a certain edge in the network, the parameter of each digit represents the connection state of the corresponding edge, and the parameters only comprise 0 and 1, wherein 0 represents the disconnection of the corresponding edge, and 1 represents the connection of the corresponding edge;
the coding mode is as follows: constructing an initial gene according to the connection state of the power network, randomly sampling all bits in the initial gene, modifying all parameters in the selected bits to be 0, and indicating that the corresponding edge is changed from connection to disconnection, namely that two nodes at two ends of the corresponding edge are changed from connection to disconnection;
and forming a new gene after sampling, wherein the new gene is an encoded individual and also represents a cluster division result.
6. The method for partitioning the high-permeability distributed power source cluster based on the comprehensive performance index as claimed in claim 2, wherein the crossover and variation probability of the genetic algorithm in the step S4 is determined as follows:
in the formula, Pc、PmRespectively representing cross probability and mutation probability; pc_max、Pc_min、Pm_max、Pm_minRespectively representing maximum cross probability, minimum cross probability, maximum mutation probability and minimum mutation probability; f' represents the larger fitness value of the two individuals needing to be subjected to the crossover operation; f represents the adaptability value of the individual needing mutation operation; f. ofavgThe mean fitness value of the population is represented.
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