A distributed photovoltaic cluster division method and system considering frequency modulation response rate

Abstract

The present invention provides a distributed photovoltaic cluster division method and system considering frequency modulation response rate, wherein the method includes determining the frequency response speed of the distributed photovoltaic cluster; using the frequency response speed as the distributed photovoltaic cluster division index to determine the distributed photovoltaic cluster frequency regulation flexibility response speed index; determining the comprehensive electrical distance based on impedance-power reserve; determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as the comprehensive electrical distance index; determining the active power balance index; assigning different weights to the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index respectively to obtain the comprehensive photovoltaic cluster division index; improving the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the optimal division result of the distributed photovoltaic cluster. The present invention improves the accuracy of photovoltaic cluster division.

Description

Technical Field

[0001] The present invention belongs to the technical field of active power distribution network optimization and control, and in particular relates to a distributed photovoltaic cluster division method and system considering frequency modulation response rate. Background Art

[0002] With the rapid development of my country's photovoltaic power generation industry, the large-scale grid-connected photovoltaic power stations have affected the safe and stable operation of the power grid. Photovoltaic power prediction technology can effectively reduce the uncertainty of photovoltaic output power. When there are a large number of photovoltaic power stations in a region, the power prediction of a single photovoltaic power station can no longer fully meet the application needs of the dispatching department, and it is urgent to realize the power prediction of regional photovoltaic power station groups.

[0003] The clustering of photovoltaic power stations is an important part of regional photovoltaic power prediction technology. Most of the current regional prediction methods use the prediction results of a single photovoltaic power station as the minimum unit, and calculate the regional prediction results through the accumulation method or statistical upscaling method. With the increase of the prediction area and the number of photovoltaic power stations, the calculation efficiency of the prediction model will gradually decrease. Therefore, it is necessary to reasonably divide the photovoltaic power station clusters to significantly improve the modeling efficiency without affecting the prediction accuracy.

[0004] The more commonly used method for dividing photovoltaic clusters is to divide them according to the administrative region or line where the photovoltaic power station is located. This method does not take into account the influence of meteorological factors in the power station area. The meteorological characteristics of photovoltaic power stations in the region may vary greatly, which has a certain impact on the accuracy of regional cluster power prediction. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a distributed photovoltaic cluster division method and system taking into account the frequency modulation response rate.

[0006] In a first aspect, the present invention provides a distributed photovoltaic cluster division method considering frequency modulation response rate, comprising:

[0007] Determine the frequency response speed of distributed photovoltaic clusters;

[0008] The frequency response speed is used as a distributed photovoltaic cluster division indicator to determine the distributed photovoltaic cluster frequency regulation flexibility response speed indicator;

[0009] Determine the integrated electrical distance based on impedance-power reserve;

[0010] Determine the degree of electrical coupling between PV clusters based on the comprehensive electrical distance as a comprehensive electrical distance indicator;

[0011] Determine active power balance index;

[0012] The distributed photovoltaic cluster frequency regulation flexibility response speed index, comprehensive electrical distance index and active power balance index are assigned different weights to obtain the comprehensive photovoltaic cluster division index.

[0013] Improve the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the optimal division result of the distributed photovoltaic cluster.

[0014] Furthermore, determining the frequency response speed of the distributed photovoltaic cluster includes:

[0015] The remaining capacity of each PV cluster after meeting the grid flexibility supply and demand balance is calculated according to the following formula:

[0016]

[0017] Among them, P m,left (t) is the remaining capacity of the mth PV cluster after meeting the grid flexibility supply and demand balance at time t; P max,m is the maximum capacity of the mth distributed photovoltaic cluster; P m (t) is the output power of the mth PV cluster at time t; It is the output power change of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M;

[0018] According to the remaining capacity of each photovoltaic cluster after meeting the grid flexibility supply and demand balance, the frequency response speed of the distributed photovoltaic cluster is calculated:

[0019]

[0020] Among them, k c (t) is the frequency response speed of the distributed photovoltaic cluster at time t; n is the total number of distributed photovoltaic clusters; t n,left is the response time of the nth distributed photovoltaic cluster.

[0021] Furthermore, the frequency response speed is used as a distributed photovoltaic cluster division index to determine a distributed photovoltaic cluster frequency regulation flexibility response speed index, including:

[0022] The distributed photovoltaic cluster frequency regulation flexibility response speed index is calculated according to the following formula

[0023]

[0024] Among them, N c is the total number of photovoltaic clusters; T is the research period of a typical time-varying scenario; E left,m (t) is the sum of the remaining capacities of all energy storage devices in the mth PV cluster at time t.

[0025] Further, the determining of the integrated electrical distance based on impedance-power reserve includes:

[0026] Construct an expression for the active power reserve degree of distributed photovoltaic clusters to evaluate the reserve capacity between photovoltaic clusters:

[0027]

[0028] Among them, β ij,t is the active power reserve degree of PV cluster i and PV cluster j at time t; is the active power provided by the PV grid-connected inverter in PV cluster i and PV cluster j at time t; is the active power required between PV cluster i and PV cluster j at time t;

[0029] The integrated electrical distance based on impedance-power reserve is calculated according to the following formula:

[0030] d ij,t =μ1Z′ ij +μ2β ij,t ;

[0031] Among them, d ij,t is the comprehensive electrical distance between photovoltaic cluster i and photovoltaic cluster j based on impedance-power reserve at time t; Z' ij is the impedance value between photovoltaic cluster i and photovoltaic cluster j; μ1 is Z' ij The weighting coefficient of ij,t The weighting coefficient of .

[0032] Furthermore, determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as a comprehensive electrical distance indicator includes:

[0033] The edge weight e between photovoltaic cluster i and photovoltaic cluster j is calculated according to the following formula ij :

[0034]

[0035] Among them, max(d) is the maximum electrical distance between PV clusters;

[0036] The degree of electrical coupling ρ between PV clusters is calculated according to the following formula:

[0037]

[0038] Among them, in the undirected unweighted network, when photovoltaic cluster i and photovoltaic cluster j are connected, e ij =1; when PV cluster i and PV cluster j are not connected, e ij=0; m' is the sum of the weights of the edges of all interconnected PV clusters; k i is the sum of the weights of all edges connected to PV cluster i; k j is the sum of the weights of all edges connected to PV cluster j; when PV cluster i and PV cluster j are in the same cluster, δ(i, j) = 1; when PV cluster i and PV cluster j are not in the same cluster, δ(i, j) = 0.

[0039] Further, the determining of the active power balance index includes:

[0040] Construct the active power balance index expression of a single photovoltaic cluster:

[0041]

[0042] Among them, P m is the active power balance index of the mth photovoltaic cluster; T is the research period of the typical time-varying scenario; P clu (t) m is the net power characteristic of the mth photovoltaic cluster at time t; max(P clu (t)) is the maximum net power characteristic of all PV clusters;

[0043] Calculate the active balance index of all photovoltaic clusters according to the following formula:

[0044]

[0045] Among them, N c The total number of clusters divided into PV clusters.

[0046] Furthermore, the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index are respectively assigned different weights to obtain a comprehensive photovoltaic cluster division index, including:

[0047] The comprehensive photovoltaic cluster division index γ is calculated according to the following formula:

[0048]

[0049] Among them, λ1 is the weight of the degree of electrical coupling ρ between photovoltaic clusters; λ2 is the active balance index of all photovoltaic clusters The weight of λ3 is the frequency regulation flexibility response speed index of distributed photovoltaic clusters. The weight of .

[0050] Furthermore, the method of improving the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the optimal division result of the distributed photovoltaic cluster includes:

[0051] C1, according to the parameters and operating status of the distributed photovoltaic cluster, the parameters of the adaptive genetic algorithm will be initialized based on the supply and demand balance of the cluster flexibility, combined with the line impedance, the power generation capacity of each photovoltaic cluster, and the power generation power;

[0052] C2, through the genetic algorithm based on the adjacency matrix, the connection relationship between each two photovoltaic clusters is represented by 0 and 1; where the simplified adjacency matrix of each group of photovoltaic clusters represents the chromosome of an individual;

[0053] C3, by constructing a comprehensive clustering index system, the frequency regulation potential of individual chromosomes and the new energy absorption capacity represented by the simplified adjacency matrix of the photovoltaic cluster are evaluated, and the chromosome individuals in the current matrix are scored;

[0054] C4, judge whether the current number of matrix chromosome individual score calculations meets the termination condition; if the termination condition is met, the loop ends and goes to C6; if the termination condition is not met, the number of iterations is increased by 1 and goes to C5;

[0055] C5, judge whether the individual fitness of the chromosome in the current adjacency matrix is ​​greater than the average value; if it is greater than the average value, return to C3; if it is not greater than the average value, modify the crossover probability and mutation probability, and return to C3;

[0056] Among them, the probability of increasing chromosome individual mutation is calculated according to the following formula:

[0057]

[0058]

[0059] Among them, p c and p m are the crossover and mutation probabilities respectively; p c,max and p c,min are the maximum and minimum values ​​of the crossover probability respectively; p m,max and p m,min are the maximum and minimum values ​​of the mutation probability respectively; I is the number of iterations; I max is the maximum number of iterations; f is the maximum fitness value of the two individuals undergoing the crossover operation; f' is the fitness value of the individual undergoing the mutation operation; f avg is the average fitness of the population;

[0060] C6, selects the best chromosome individual that satisfies the fitness function during the iteration process; the best chromosome selected by iteration is used as the partition obtained by simplifying the adjacency matrix, and the final partition result is obtained by partitioning.

[0061] In a second aspect, the present invention provides a distributed photovoltaic cluster division system considering frequency modulation response rate, comprising:

[0062] The first determination module is used to determine the frequency response speed of the distributed photovoltaic cluster;

[0063] The second determination module is used to use the frequency response speed as a distributed photovoltaic cluster division index to determine the distributed photovoltaic cluster frequency regulation flexibility response speed index;

[0064] A third determination module is used to determine the integrated electrical distance based on impedance-power reserve;

[0065] a fifth determination module, for determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as a comprehensive electrical distance indicator;

[0066] A sixth determination module, used to determine an active power balance index;

[0067] A calculation module is used to assign different weights to the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index to obtain a comprehensive photovoltaic cluster division index;

[0068] The convergence speed improvement module is used to improve the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the best division result of the distributed photovoltaic cluster.

[0069] The present invention provides a distributed photovoltaic cluster division method and system considering the frequency modulation response rate, wherein a comprehensive cluster division index is selected in the method. On the one hand, the selection of the index comprehensively considers the comprehensive electrical distance of impedance-power reserve between photovoltaic clusters, which can better reflect the coupling degree of each photovoltaic cluster compared with the traditional single electrical distance, and improves the accuracy of photovoltaic cluster division. On the other hand, the photovoltaic cluster division method of the present invention takes into account the frequency modulation rate problem, and uses the frequency modulation response speed index as one of the comprehensive photovoltaic cluster division indicators, which can respond faster when facing the frequency fluctuation problem, and play the role of frequency regulation.

[0070] The present invention also uses an adaptive genetic algorithm based on an adjacency matrix. Compared with the traditional genetic algorithm, this encoding method not only ensures node connectivity, but also greatly reduces the search range of the genetic algorithm and reduces the search time because each individual satisfies the connectivity. At the same time, the genetic algorithm with this encoding method does not have the node merging process of the general algorithm, and has a strong search capability for irregular clusters. The distributed photovoltaic cluster division obtained by the genetic algorithm is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 A flow chart of a distributed photovoltaic cluster division method considering frequency modulation response rate provided by an embodiment of the present invention;

[0073] Figure 2 A system network topology diagram with distributed power sources involved provided by an embodiment of the present invention;

[0074] Figure 3 A comparison chart of the convergence speed of two genetic algorithms provided in an embodiment of the present invention;

[0075] Figure 4 A result diagram of photovoltaic cluster division considering only modularization indicators provided in an embodiment of the present invention;

[0076] Figure 5 A result diagram of photovoltaic cluster division considering modularization index and active power balance index provided by an embodiment of the present invention;

[0077] Figure 6 A photovoltaic cluster division result diagram under different cluster division indicators provided by an embodiment of the present invention;

[0078] Figure 7 A flow chart of an adaptive genetic algorithm provided by an embodiment of the present invention;

[0079] Figure 8 A schematic structural diagram of a distributed photovoltaic cluster division system taking frequency modulation response rate into consideration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In one embodiment, if Figure 1 As shown, an embodiment of the present invention provides a distributed photovoltaic cluster division method considering frequency modulation response rate, including:

[0082] Step 101, determining the frequency response speed of the distributed photovoltaic cluster.

[0083] In this step, by analyzing the current situation of severe net load fluctuations under the intervention of distributed photovoltaics, the importance of distributed power generation clusters to the flexible supply and demand requirements is proposed. Considering the sudden changes in grid frequency caused by load fluctuations, distributed power sources need to respond quickly in a short time scale. On this basis, a photovoltaic cluster frequency response speed model is proposed.

[0084] For example, due to the fluctuation of net load, each photovoltaic cluster will continuously switch between supply and demand modes, thereby introducing a flexible supply and demand balance, that is, determining the supply and demand status of the distributed photovoltaic cluster according to the supply and demand relationship:

[0085]

[0086]

[0087]

[0088] in, is the flexible demand of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M; is the power demand of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M; P is the power supply of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M; k (t) is the net load power of PV cluster k at time t; P k (t-τ) is the net load power of PV cluster k at time t-τ; is the power supply of conventional thermal power unit g at time t; is the power supply of interruptible load d at time t; is the power supply of the energy storage system at time t;

[0089] The remaining capacity P after considering the photovoltaic cluster to meet the grid flexibility supply and demand balance m,left (t), which is the remaining capacity of each PV cluster after removing the original PV cluster output power and the output power change when the net load fluctuates.

[0090] Exemplarily, the remaining capacity of each photovoltaic cluster after meeting the grid flexibility supply and demand balance is calculated according to the following formula:

[0091]

[0092] Among them, P m,left (t) is the remaining capacity of the mth PV cluster after meeting the grid flexibility supply and demand balance at time t; P max,m is the maximum capacity of the mth distributed photovoltaic cluster; Pm (t) is the output power of the mth PV cluster at time t; It is the output power change of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M;

[0093] According to the remaining capacity of each photovoltaic cluster after meeting the grid flexibility supply and demand balance, the frequency response speed of the distributed photovoltaic cluster is calculated:

[0094]

[0095] Among them, k c (t) is the frequency response speed of the distributed photovoltaic cluster at time t; n is the total number of distributed photovoltaic clusters; t n,left is the response time of the nth distributed photovoltaic cluster.

[0096] Step 102: Use the frequency response speed as a distributed photovoltaic cluster division index to determine a distributed photovoltaic cluster frequency regulation flexibility response speed index.

[0097] The comprehensive photovoltaic cluster classification index considering the frequency regulation response speed can be classified into two parts: structural index and functional index. The functional index is divided into two parts, the most important of which is the frequency regulation flexibility response speed index. The frequency regulation response speed index is an assessment of the net power size of the photovoltaic cluster on a short time scale. When a photovoltaic cluster generates more power surplus by consuming more new energy units, it will have greater frequency regulation potential and speed. Through the photovoltaic cluster frequency response speed model k obtained in step 101 c (t) This model is used as a cluster division indicator, and after normalization, a PV cluster frequency regulation flexibility response speed indicator is proposed

[0098]

[0099] Among them, N c is the total number of photovoltaic clusters; T is the research period of a typical time-varying scenario; E left,m (t) is the sum of the remaining capacities of all energy storage devices in the mth PV cluster at time t.

[0100] Step 103, determining the comprehensive electrical distance based on impedance-power reserve.

[0101] In this step, the structural index uses the comprehensive electrical distance based on impedance-power reserve as the modular index for photovoltaic cluster division. The cluster frequency control of the distribution network mainly relies on the nodes to flexibly adjust the active output of the photovoltaic grid-connected inverters in each cluster. To achieve flexible output adjustment, each node should have a huge power reserve. At this time, the concept of effective reserve degree is introduced to evaluate the reserve capacity between nodes, that is, to construct the expression of the active power reserve degree of the distributed photovoltaic cluster:

[0102]

[0103] Among them, β ij,t is the active power reserve degree of PV cluster i and PV cluster j at time t; is the active power provided by the PV grid-connected inverter in PV cluster i and PV cluster j at time t; is the active power required between PV cluster i and PV cluster j at time t.

[0104] The integrated electrical distance based on impedance-power reserve is calculated according to the following formula:

[0105] d ij,t =μ1Z′ ij +μ2β ij,t .

[0106] Among them, d ij,t is the comprehensive electrical distance between photovoltaic cluster i and photovoltaic cluster j based on impedance-power reserve at time t; Z' ij is the impedance value between photovoltaic cluster i and photovoltaic cluster j; μ1 is Z' ij The weighting coefficient of ij,t The weighting coefficient of .

[0107] Step 104 : determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance to serve as a comprehensive electrical distance index.

[0108] In this step, the network edge weight e ij is the electrical distance d ij,t Determined by. Using the electrical distance between nodes as the edge weight to describe the modularization index can not only reflect the structural performance of the cluster, but also describe the degree of electrical coupling between nodes within the cluster. In order to meet the relationship between the edge weight and the electrical distance between photovoltaic clusters, that is, the smaller the electrical distance, the greater the edge weight.

[0109] Exemplarily, the edge weight e between photovoltaic cluster i and photovoltaic cluster j is calculated according to the following formula: ij :

[0110]

[0111] Among them, max(d) is the maximum electrical distance between PV clusters.

[0112] The degree of electrical coupling ρ between PV clusters is calculated according to the following formula:

[0113]

[0114] Among them, in the undirected unweighted network, when photovoltaic cluster i and photovoltaic cluster j are connected, e ij =1; when PV cluster i and PV cluster j are not connected, e ij = 0; m' is the sum of the weights of the edges of all interconnected PV clusters, k i is the sum of the weights of all edges connected to PV cluster i, k j is the sum of the weights of all edges connected to PV cluster j, When photovoltaic cluster i and photovoltaic cluster j are in the same cluster, δ(i, j) = 1; when photovoltaic cluster i and photovoltaic cluster j are not in the same cluster, δ(i, j) = 0.

[0115] Step 105, determining the active power balance index.

[0116] Another functional indicator is the active power balance index. Using the active power balance index to divide photovoltaic clusters can achieve the coordinated planning of distributed photovoltaic clusters. Through this indicator, the matching degree between the internal capacity and output power of the photovoltaic cluster and the load can be more clearly observed.

[0117] For example, construct an expression for the active power balance index of a single photovoltaic cluster:

[0118]

[0119] Among them, P m is the active power balance index of the mth photovoltaic cluster; T is the research period of the typical time-varying scenario; P clu (t) m is the net power characteristic of the mth PV cluster at time t, which can be obtained based on the typical time-varying scenario of each node; max(P clu (t)) is the maximum net power characteristic of all PV clusters.

[0120] Calculate the active balance index of all photovoltaic clusters according to the following formula:

[0121]

[0122] Among them, N c The total number of clusters divided into PV clusters.

[0123] Step 106, assigning different weights to the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index, respectively, to obtain a comprehensive photovoltaic cluster division index.

[0124] In this step, illustratively, the comprehensive photovoltaic cluster division index γ is calculated according to the following formula:

[0125]

[0126] Among them, λ1 is the weight of the degree of electrical coupling ρ between photovoltaic clusters; λ2 is the active balance index of all photovoltaic clusters The weight of λ3 is the frequency regulation flexibility response speed index of distributed photovoltaic clusters. The weight of .

[0127] Step 107, improving the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the best distributed photovoltaic cluster division result.

[0128] In this step, an adaptive genetic algorithm based on a weighted network adjacency matrix encoding method is used to find the best cluster division result. For example, Figure 7 As shown, including:

[0129] C1, according to the parameters and operating status of the distributed photovoltaic cluster, the parameters of the adaptive genetic algorithm will be initialized based on the supply and demand balance of the cluster flexibility, combined with the line impedance, the power generation capacity of each photovoltaic cluster, and the power generation power;

[0130] C2, through the genetic algorithm based on the adjacency matrix, the connection relationship between each two photovoltaic clusters is represented by 0 and 1; where the simplified adjacency matrix of each group of photovoltaic clusters represents the chromosome of an individual;

[0131] C3, by constructing a comprehensive clustering index system, the frequency regulation potential of individual chromosomes and the new energy absorption capacity represented by the simplified adjacency matrix of the photovoltaic cluster are evaluated, and the chromosome individuals in the current matrix are scored;

[0132] C4, judge whether the current number of matrix chromosome individual score calculations meets the termination condition; if the termination condition is met, the loop ends and goes to C6; if the termination condition is not met, the number of iterations is increased by 1 and goes to C5;

[0133] C5, judge whether the individual fitness of the chromosome in the current adjacency matrix is ​​greater than the average value; if it is greater than the average value, return to C3; if it is not greater than the average value, modify the crossover probability and mutation probability, and return to C3;

[0134] As the iteration progresses, the greater the number of iterations, the more similar the patterns of the chromosome individuals in each adjacency matrix are, that is, the closer the results of the photovoltaic cluster divisions are, at this time, the excessively high chromosome individual crossover probability loses its meaning, and at this time, the mutation probability is appropriately increased to improve the local search ability of the algorithm. The increased chromosome individual mutation probability is calculated according to the following formula:

[0135]

[0136]

[0137] Among them, p c and p m are the crossover and mutation probabilities respectively; p c,max and p c,min are the maximum and minimum values ​​of the crossover probability respectively; p m,max and p m,min are the maximum and minimum values ​​of the mutation probability respectively; I is the number of iterations; I max is the maximum number of iterations; f is the maximum fitness value of the two individuals undergoing the crossover operation; f' is the fitness value of the individual undergoing the mutation operation; f avg is the average fitness of the population.

[0138] C6, selects the best chromosome individual that satisfies the fitness function during the iteration process; the best chromosome selected by iteration is used as the partition obtained by simplifying the adjacency matrix, and the final partition result is obtained by partitioning.

[0139] Taking the IEEE-33 node system as an example, based on this model, the system consists of 18 distributed photovoltaic nodes with a total capacity of 2770kW. 13 distributed wind power nodes with a total capacity of 480kW, and a distributed power capacity penetration rate of 86.8%. 4 distributed energy storage nodes with a total capacity of 1480kWh. 4 micro gas turbine nodes with a total capacity of 2440kW. The improved IEEE33 node system network topology is as follows: Figure 2 As shown in Figure 2, an improved genetic algorithm is used for cluster partition calculation, the population size is set to 200, the maximum number of iterations is set to 800, and the elite retention strategy is used to ensure algorithm convergence.

[0140] The system is simulated, and the convergence speed of the common genetic algorithm and the adaptive genetic algorithm provided by the present invention are compared. Figure 3 As shown. Figure 3 It can be seen that the improved adaptive genetic algorithm proposed in the present invention can approach the optimal value more quickly, thereby obtaining the optimal cluster division result more quickly.

[0141] On the basis of the above, the cluster division results obtained under different cluster division indicators are simulated and compared. There are three scenarios in total. Scenario 1 only uses the modular indicator of the comprehensive electrical distance based on impedance-power reserve for division. Scenario 2 adds the active balance indicator on the basis of Scenario 1. Finally, Scenario 3 uses the comprehensive cluster division indicator. The photovoltaic cluster division results are as follows: Figure 4 , Figure 5 and Figure 6 As shown, it can be seen that the comprehensive cluster division index taking into account the frequency regulation response rate can accurately describe the frequency regulation response characteristics of the flexible resources within the cluster. On the basis of flexible supply and demand matching, it can match the response speed of each flexible resource with the flexible demand, maximize the cluster response speed, and improve the cluster's ability to participate in frequency regulation.

[0142] The embodiment of the present invention provides a distributed photovoltaic cluster division method that considers the frequency modulation response rate, and selects a comprehensive cluster division index. On the one hand, the selection of the index comprehensively considers the comprehensive electrical distance of impedance-power reserve between photovoltaic clusters, which can better reflect the coupling degree of each photovoltaic cluster compared with the traditional single electrical distance, and improves the accuracy of photovoltaic cluster division. On the other hand, the photovoltaic cluster division method of the present invention takes into account the frequency modulation rate problem, and uses the frequency modulation response speed index as one of the comprehensive photovoltaic cluster division indicators, which can respond faster when facing frequency fluctuation problems and play a role in frequency regulation.

[0143] The embodiment of the present invention also uses an adaptive genetic algorithm based on an adjacency matrix. Compared with the traditional genetic algorithm, this encoding method not only ensures node connectivity, but also greatly reduces the search range of the genetic algorithm and reduces the search time because each individual satisfies the connectivity. At the same time, the genetic algorithm with this encoding method does not have the node merging process of the general algorithm, and has a strong search capability for irregular clusters. The distributed photovoltaic cluster division obtained by the genetic algorithm is more accurate.

[0144] Based on the same inventive concept, an embodiment of the present invention also provides a distributed photovoltaic cluster division system that considers the frequency modulation response rate. Since the principle of solving the problem by this system is similar to the distributed photovoltaic cluster division method that considers the frequency modulation response rate, the implementation of this system can refer to the implementation of the distributed photovoltaic cluster division method that considers the frequency modulation response rate, and the repeated parts will not be repeated.

[0145] In another embodiment, a distributed photovoltaic cluster division system considering frequency modulation response rate is provided in an embodiment of the present invention, such as Figure 8 As shown, including:

[0146] The first determination module 10 is used to determine the frequency response speed of the distributed photovoltaic cluster.

[0147] The second determination module 20 is configured to use the frequency response speed as a distributed photovoltaic cluster division index to determine a distributed photovoltaic cluster frequency regulation flexibility response speed index.

[0148] The third determination module 30 is used to determine the comprehensive electrical distance based on impedance-power reserve.

[0149] The fifth determination module 40 is used to determine the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as a comprehensive electrical distance indicator.

[0150] The sixth determination module 50 is used to determine an active power balance index.

[0151] The calculation module 60 is used to assign different weights to the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index to obtain a comprehensive photovoltaic cluster division index.

[0152] The convergence speed improving module 70 is used to improve the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the best distributed photovoltaic cluster division result.

[0153] For more specific working processes of the above modules, please refer to the corresponding contents disclosed in the above embodiments, which will not be repeated here.

[0154] In another embodiment, the present invention provides a computer device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the steps of the above-mentioned distributed photovoltaic cluster division method considering the frequency modulation response rate are implemented.

[0155] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0156] In another embodiment, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the above-mentioned distributed photovoltaic cluster division method considering the frequency modulation response rate are implemented.

[0157] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0158] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system, device, and storage medium disclosed in the embodiment, since they correspond to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0159] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0160] The present invention has been described in detail above in conjunction with specific implementations and exemplary examples, but these descriptions cannot be understood as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, a variety of equivalent substitutions, modifications or improvements may be made to the technical solution of the present invention and its implementation methods, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A distributed photovoltaic cluster division method considering frequency modulation response rate, characterized in that: include: Determine the frequency response speed of distributed photovoltaic clusters; The frequency response speed is used as a distributed photovoltaic cluster division indicator to determine the distributed photovoltaic cluster frequency regulation flexibility response speed indicator; including: The distributed photovoltaic cluster frequency regulation flexibility response speed index is calculated according to the following formula Among them, N c is the total number of photovoltaic clusters; T is the research period of a typical time-varying scenario; E left,m (t) is the sum of the remaining capacities of all energy storage devices in the mth PV cluster at time t; Determine the integrated electrical distance based on impedance-power reserve; Determine the degree of electrical coupling between PV clusters based on the comprehensive electrical distance as a comprehensive electrical distance indicator; Determine active power balance index; The distributed photovoltaic cluster frequency regulation flexibility response speed index, comprehensive electrical distance index and active power balance index are assigned different weights to obtain the comprehensive photovoltaic cluster division index. Improve the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the optimal division result of the distributed photovoltaic cluster.

2. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 1 is characterized in that: Determining the frequency response speed of the distributed photovoltaic cluster includes: The remaining capacity of each PV cluster after meeting the grid flexibility supply and demand balance is calculated according to the following formula: Among them, P m,left (t) is the remaining capacity of the mth PV cluster after meeting the grid flexibility supply and demand balance at time t; P max,m is the maximum capacity of the mth distributed photovoltaic cluster; P m (t) is the output power of the mth PV cluster at time t; It is the output power change of the mth PV cluster at time t under the conditions of response time scale τ and PV cluster capacity peak M; According to the remaining capacity of each photovoltaic cluster after meeting the grid flexibility supply and demand balance, the frequency response speed of the distributed photovoltaic cluster is calculated: Among them, k c (t) is the frequency response speed of the distributed photovoltaic cluster at time t; n is the total number of distributed photovoltaic clusters; t n,left is the response time of the nth distributed photovoltaic cluster.

3. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 1 is characterized in that: The determining of the integrated electrical distance based on impedance-power reserve includes: Construct an expression for the active power reserve degree of distributed photovoltaic clusters to evaluate the reserve capacity between photovoltaic clusters: Among them, β ij,t is the active power reserve degree of PV cluster i and PV cluster j at time t; is the active power provided by the PV grid-connected inverter in PV cluster i and PV cluster j at time t; is the active power required between PV cluster i and PV cluster j at time t; The integrated electrical distance based on impedance-power reserve is calculated according to the following formula: d ij,t =μ1Z′ ij +μ2β ij,t ; Among them, d ij,t is the comprehensive electrical distance between photovoltaic cluster i and photovoltaic cluster j based on impedance-power reserve at time t; Z′ ij is the impedance value between photovoltaic cluster i and photovoltaic cluster j; μ1 is Z′ ij The weighting coefficient of ij,t The weighting coefficient of .

4. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 3 is characterized in that: Determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as a comprehensive electrical distance indicator includes: The edge weight e between photovoltaic cluster i and photovoltaic cluster j is calculated according to the following formula ij : Among them, max(d) is the maximum electrical distance between PV clusters; The degree of electrical coupling ρ between PV clusters is calculated according to the following formula: Among them, in the undirected unweighted network, when photovoltaic cluster i and photovoltaic cluster j are connected, e ij =1; when PV cluster i and PV cluster j are not connected, e ij = 0; m′ is the sum of the weights of the edges of all interconnected PV clusters; k i is the sum of the weights of all edges connected to PV cluster i; k j is the sum of the weights of all edges connected to PV cluster j; when PV cluster i and PV cluster j are in the same cluster, δ(i, j) = 1; when PV cluster i and PV cluster j are not in the same cluster, δ(i, j) = 0.

5. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 1 is characterized in that: Determining the active power balance index includes: Construct the active power balance index expression of a single photovoltaic cluster: Among them, P m is the active power balance index of the mth photovoltaic cluster; T is the research period of the typical time-varying scenario; P clu (t) m is the net power characteristic of the mth photovoltaic cluster at time t; max(P clu (t)) is the maximum net power characteristic of all PV clusters; Calculate the active balance index of all photovoltaic clusters according to the following formula: Among them, N c The total number of clusters divided into PV clusters.

6. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 1 is characterized in that: The distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index are respectively assigned different weights to obtain the comprehensive photovoltaic cluster division index, including: The comprehensive photovoltaic cluster division index γ is calculated according to the following formula: Among them, λ1 is the weight of the degree of electrical coupling ρ between photovoltaic clusters; λ2 is the active balance index of all photovoltaic clusters The weight of λ3 is the frequency regulation flexibility response speed index of distributed photovoltaic clusters. The weight of .

7. The distributed photovoltaic cluster division method considering frequency modulation response rate according to claim 1 is characterized in that: The method of improving the convergence speed of the distributed photovoltaic cluster partitioning process based on the comprehensive photovoltaic cluster partitioning index to obtain the optimal partitioning result of the distributed photovoltaic cluster includes: C1, according to the parameters and operating status of the distributed photovoltaic cluster, the parameters of the adaptive genetic algorithm will be initialized based on the supply and demand balance of the cluster flexibility combined with the line impedance, the power generation capacity of each photovoltaic cluster, and the power generation power; C2, through the genetic algorithm based on the adjacency matrix, the connection relationship between each two photovoltaic clusters is represented by 0 and 1; where the simplified adjacency matrix of each group of photovoltaic clusters represents the chromosome of an individual; C3, by constructing a comprehensive clustering index system, the frequency regulation potential of individual chromosomes represented by the simplified adjacency matrix of the photovoltaic cluster and the size of the new energy consumption capacity are evaluated, and the chromosome individuals in the current matrix are scored; C4, judge whether the current number of matrix chromosome individual score calculations meets the termination condition; if the termination condition is met, the loop ends and goes to C6; if the termination condition is not met, the number of iterations is increased by 1 and goes to C5; C5, judge whether the individual fitness of the chromosome in the current adjacency matrix is ​​greater than the average value; if it is greater than the average value, return to C3; if it is not greater than the average value, modify the crossover probability and mutation probability, and return to C3; Among them, the probability of increasing chromosome individual mutation is calculated according to the following formula: Among them, p c and p m are the crossover and mutation probabilities respectively; p c,max and p c,min are the maximum and minimum values ​​of the crossover probability respectively; p m,max and p m,min are the maximum and minimum values ​​of the mutation probability respectively; I is the number of iterations; I max is the maximum number of iterations; f is the maximum fitness value of the two individuals performing the crossover operation; f′ is the fitness value of the individual performing the mutation operation; f avg is the average fitness of the population; C6, selects the best chromosome individual that satisfies the fitness function during the iteration process; the best chromosome selected by iteration is used as the partition obtained by simplifying the adjacency matrix, and the final partition result is obtained by partitioning.

8. A system for the distributed photovoltaic cluster division method considering frequency modulation response rate as described in any one of claims 1 to 7, characterized in that: include: The first determination module is used to determine the frequency response speed of the distributed photovoltaic cluster; The second determination module is used to use the frequency response speed as a distributed photovoltaic cluster division index to determine the distributed photovoltaic cluster frequency regulation flexibility response speed index; A third determination module is used to determine the integrated electrical distance based on impedance-power reserve; a fifth determination module, for determining the degree of electrical coupling between photovoltaic clusters according to the comprehensive electrical distance as a comprehensive electrical distance indicator; A sixth determination module, used to determine an active power balance index; A calculation module is used to assign different weights to the distributed photovoltaic cluster frequency regulation flexibility response speed index, the comprehensive electrical distance index and the active power balance index to obtain a comprehensive photovoltaic cluster division index; The convergence speed improvement module is used to improve the convergence speed of the distributed photovoltaic cluster division process based on the comprehensive photovoltaic cluster division index to obtain the best division result of the distributed photovoltaic cluster.

Images