Distributed power supply cluster division method considering flexible resource adjustable capacity and response speed

Abstract

The invention provides a distributed power supply cluster division method considering flexible resource adjustable capacity and response speed. The method comprises the following steps: firstly, on the basis of analyzing the flexibility response capability of a cluster, proposing a deviation accumulation area between a cluster output response curve and a net load curve as a cluster flexibility response capability index; secondly, providing a comprehensive performance index combining a modularity index, an active balance index, a cluster flexibility response capability index and an energy storage unit distribution index; and finally, dividing the distributed power supply cluster by adopting an improved adaptive genetic algorithm. According to the method, on the basis of ensuring the structural property of the cluster, the adjusting capability of the adjustable and flexible resources in the cluster is considered, the controllable potential of the cluster is fully played, and the power balance capability and the rapid adjusting capability of the cluster are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power supply, and more specifically, to a distributed power supply cluster partitioning method taking into account the adjustable capacity and response speed of flexible resources. Background Art

[0002] In order to cope with the energy crisis and global climate issues, renewable energy generation represented by wind power and photovoltaics has been widely used and developed rapidly around the world. Distributed power sources represented by photovoltaics and wind power are connected to the distribution network to provide power to users nearby, reduce large-scale, long-distance transmission of electricity, and make up for the shortcomings of centralized power generation. However, due to the characteristics of small single-unit capacity, strong output volatility, poor controllability, and decentralized access locations of distributed power sources, a series of problems have arisen, such as reverse flow of power grid lines and sections, and voltage over-limit and fluctuation at nodes. In order to cope with the challenges brought by large-scale distributed energy access to power grid operation and user power supply, and to achieve safe and efficient grid connection of distributed power sources, it is necessary to cluster distributed renewable energy generation to give full play to the autonomy and regulation capabilities of the clusters.

[0003] At present, the research on distributed power generation cluster grouping mainly focuses on the electrical coupling between nodes, considering the control indicators such as cluster voltage control and active power balance, while the research on cluster grouping methods for distributed power generation clusters in the power grid and the control objectives such as frequency regulation and peak regulation and network topology is rare. In order to give full play to the controllable potential of the cluster, it is necessary to study the cluster division method considering the flexibility regulation capability of the distributed generation cluster, so as to enhance the comprehensive regulation capability of the cluster. Summary of the invention

[0004] The present invention aims to solve the problem that large-scale distributed power sources are difficult to control in an orderly manner and have reduced flexibility after being connected to a distribution network, and provides a distributed power source cluster division method that takes into account the adjustable capacity and response speed of flexibility resources.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a distributed power cluster partitioning method considering the adjustable capacity and response speed of flexible resources, comprising the following steps:

[0007] S1: Collect basic data of the distribution network system and generate the initial population;

[0008] S2: Use electrical distance based on reactive sensitivity to calculate cluster modularity index;

[0009] S3: Calculate the cluster active power balance index according to the active power source output and load conditions;

[0010] S4: Calculate the cluster flexibility response capability index based on the adjustable capacity and adjustment speed of each adjustable resource in the cluster;

[0011] S5: Calculate the energy storage unit allocation index based on the flexibility response capability and energy storage capacity of each cluster;

[0012] S6: Calculate the comprehensive performance index that comprehensively considers the modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index as the objective function of cluster division, and use the improved adaptive genetic algorithm to solve the cluster division result.

[0013] Preferably, the basic data of the distribution network system includes the network topology, line parameters, various active source output data, load data, and power adjustment range and adjustment speed of the flexible resources.

[0014] Preferably, the cluster division comprehensive performance index includes a modularity index, an active power balance index, a cluster flexibility response capability index, and an energy storage unit allocation index;

[0015] The expression of the modularity index is:

[0016]

[0017] Where: ρ is the modularity index; A ij is the weight of the edge connecting nodes i and j; K i and K j are the sum of the weights of all edges connected to nodes i and j respectively; C i and C j are the clusters where nodes i and j are located respectively; δ is a 0-1 function. If C i =C j , then δ(C i , C j )=1, otherwise it is 0; m is the sum of the number of network edges.

[0018] Among them, the weight of the edge connecting nodes i and j is A ij The calculation formula is as follows:

[0019]

[0020] Where: D QV，ij is the electrical distance between nodes i and j based on the reactive voltage sensitivity matrix, and its calculation formula is as follows:

[0021]

[0022] Where: n is the number of nodes in the system; S QV，in and S QV，jnare the reactive voltage sensitivity coefficients of nodes i, j and their connected nodes respectively.

[0023] The expression of the active power balance index is as follows:

[0024]

[0025] Where: is the cluster active power balance index, γ P is the active balance of the cluster, N c is the total number of clusters.

[0026] Among them, the cluster active balance γ P The calculation formula is as follows:

[0027]

[0028] Where: γ P is the active balance of the cluster; P c represents the active power that can be provided by all active power sources in the cluster, and P n It represents the sum of all active loads in the cluster.

[0029] The cluster flexibility response capability index is expressed as follows:

[0030]

[0031] Where: φ F is the cluster flexibility response capability index, N c is the total number of clusters. dev，c is the deviation area of ​​the response curve of the cth cluster, S dev,0 It is the deviation area of ​​the output curve without adjustment means.

[0032] Among them, the cluster response curve deviation area S dev The calculation formula is as follows:

[0033]

[0034] Where: T is the research period, P L (t) is the net load curve of the cluster, P G (t) is the output curve of the cluster.

[0035] Output curve deviation area S without regulation means dev,0 The calculation formula is as follows:

[0036]

[0037] Where: P l0 is the cluster net load, P g0It is the total output of all active sources in the cluster without any regulation means. At this time, the energy storage device has no charging or discharging behavior.

[0038] The expression of the energy storage unit allocation index is as follows:

[0039]

[0040] in, represents the sum of all energy storage capacities in the cth cluster, Represents the sum of the capacities of all energy storage devices in the distribution network.

[0041] Preferably, in step S6, the expression of the comprehensive performance index is as follows:

[0042]

[0043] Where: k 1 , k 2 , k 3 and k 4 are the weights of modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index, and k 1 +k 2 +k 3 +k 4 =1.

[0044] Compared with the prior art, the beneficial effect of the technical solution of the present invention is that the comprehensive division index proposed in the present invention can reasonably allocate the flexible and adjustable resources within the cluster on the basis of taking into account the cluster structure and power autonomy capability, improve the problem of insufficient flexibility in distribution network regulation under large-scale distributed power source access, and give full play to the controllable potential of the cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a distributed power cluster partitioning method that takes into account the adjustable capacity and response speed of flexible resources in the present invention.

[0046] Figure 2 This is an improved IEEE33 node topology diagram in an embodiment of the present invention.

[0047] Figure 3 Flowchart for improving the objective function of genetic algorithm optimization.

[0048] Figure 4 This is the cluster division situation obtained after the comprehensive performance index is used for division in the embodiment of the present invention. DETAILED DESCRIPTION

[0049] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0050] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0051] Example 1

[0052] See also Figure 1 This embodiment proposes a distributed power cluster partitioning method considering the adjustable capacity and response speed of flexible resources, including the following steps:

[0053] S1: Collect basic data of the distribution network system and generate the initial population;

[0054] In this embodiment, the cluster division of the improved IEEE33 node system is studied, and the topology diagram of the distribution network system is as follows: Figure 2 As shown. The system includes 13 distributed photovoltaic nodes, 3 distributed wind power nodes, 12 small hydropower and other conventional energy nodes, and 5 pure load nodes. Among them, there are 13 nodes containing energy storage units, which are mainly distributed energy storage systems with smaller capacity used in distributed power generation and user sides. The total installed capacity of photovoltaic power in the system is 2500kW, the total installed capacity of wind power is 600kW, the total capacity of conventional energy such as small hydropower is 3800kW, and the total energy storage capacity is 690kW.

[0055] S2: Use electrical distance based on reactive sensitivity to calculate cluster modularity index;

[0056] In this embodiment, the electrical distance expression based on the reactive voltage sensitivity matrix is:

[0057]

[0058] Where: D QV，ij is the electrical distance between nodes i and j based on reactive voltage sensitivity; n is the number of nodes in the system; S QV，in and S QV，jn are the reactive voltage sensitivity coefficients of nodes i, j and their connected nodes respectively.

[0059] The expression of cluster modularity index is:

[0060]

[0061] Where: ρ is the modularity index; A ij is the weight of the edge connecting nodes i and j; K i and K j are the sum of the weights of all edges connected to nodes i and j respectively; C i and C j are the clusters where nodes i and j are located respectively; δ is a 0-1 function. If C i =C j , then δ(Ci , C j )=1, otherwise it is 0; m is the sum of the number of network edges.

[0062] Among them, the weight of the edge connecting nodes i and j is A ij The calculation formula is as follows:

[0063]

[0064] S3: Calculate the cluster active power balance index according to the active power source output and load conditions;

[0065] In this embodiment, the expression of the cluster active power balance index is as follows:

[0066]

[0067] Where: is the cluster active power balance index, γ P is the active balance of the cluster, N c is the total number of clusters.

[0068] Among them, the cluster active balance γ P The calculation formula is as follows:

[0069]

[0070] Where: γ P is the active balance of the cluster; P c represents the active power that can be provided by all active power sources in the cluster, and P n It represents the sum of all active loads in the cluster.

[0071] S4: Calculate the cluster flexibility response capability index based on the adjustable capacity and adjustment speed of each adjustable resource in the cluster;

[0072] In this embodiment, the cluster flexibility response capability index is expressed as follows:

[0073]

[0074] Where: φ F is the cluster flexibility response capability index, N c is the total number of clusters. dev，c is the deviation area of ​​the response curve of the cth cluster, S dev,0 It is the deviation area of ​​the output curve without adjustment means.

[0075] Among them, the cluster response curve deviation area S dev The calculation formula is as follows:

[0076]

[0077] Where: T is the research period, P L (t) is the net load curve of the cluster, P G (t) is the output curve of the cluster.

[0078] Output curve deviation area S without regulation means dev，0 The calculation formula is as follows:

[0079]

[0080] Where: P l0 is the cluster net load, P g0 It is the total output of all active sources in the cluster without any regulation means. At this time, the energy storage device has no charging or discharging behavior.

[0081] S5: Calculate the energy storage unit allocation index based on the flexibility response capability and energy storage capacity of each cluster;

[0082] In this embodiment, the expression of the energy storage unit allocation index is as follows:

[0083]

[0084] in, represents the sum of all energy storage capacities in the cth cluster, Represents the sum of the capacities of all energy storage devices in the distribution network.

[0085] S6: Calculate the comprehensive performance index that comprehensively considers the modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index as the objective function of cluster division, and use the improved adaptive genetic algorithm to solve the cluster division result.

[0086] The use of genetic algorithms to solve the clustering results mainly includes five steps: encoding, selection, crossover, mutation, and termination condition judgment:

[0087] A1: Use binary coding to encode different cluster divisions and randomly generate the initial population;

[0088] A2: Calculate the fitness value of each individual in the population, and select excellent individuals with a certain probability to form a new population to reproduce the next generation of population:

[0089] A3: Use single-point crossover to generate a new population with an adaptive crossover probability for the selected offspring;

[0090] A4: Perform mutation operation on the population obtained after crossover with an adaptive mutation probability to obtain a new population;

[0091] A5: Record the individual with the highest fitness in the population and determine whether the iteration termination condition is met. If the number of iterations is greater than the set value, terminate the operation and output the optimal solution. Otherwise, return to step A2 and continue the loop operation.

[0092] Among them, the comprehensive performance index that comprehensively considers the modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index is used as the fitness of the population, and its expression is as follows:

[0093]

[0094] Where: k 1 , k 2 , k 3 and k 4 are the weights of modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index, and k 1 +k 2 +k 3 +k 4 =1.

[0095] In order to verify the effectiveness of the cluster partitioning method proposed in the present invention, the cluster partitioning of the improved IEEE33 node system under different partitioning schemes and the various indicators of the cluster after partitioning are investigated. The calculation results of the cluster partitioning indicators under the three schemes are shown in Table 1. Among them, Scheme 1 is the partitioning situation that only considers the modularity index, Scheme 2 is the partitioning situation that takes into account both the modularity index and the active power balance index, and Scheme 3 is the partitioning situation that comprehensively considers the modularity, active power balance, flexibility response capability and energy storage unit allocation index.

[0096] Table 1 Calculation results of cluster partitioning indicators

[0097]

[0098] The results show that compared with the modularity division method, after cluster division with the comprehensive performance index proposed in the present invention, the modularity index of the 33-node system decreased by 3.77%, while the cluster active balance index, flexibility response capability index and energy storage unit allocation index increased by 34.64%, 32.15% and 11.12% respectively; compared with the second scheme which comprehensively considers the modularity and active balance indicators, after adopting the division method proposed in the present invention, the modularity index and active balance index of the 33-node system decreased by 1.91% and 0.18% respectively, while the flexibility response capability index and energy storage unit allocation index increased by 9.65% and 1.96% respectively.

[0099] In summary, the method proposed in this invention can ensure that the cluster has a strong structure and good source-load complementarity within the cluster, and improve the cluster's autonomy. At the same time, the proposed flexibility response capability index can effectively enhance the cluster's comprehensive regulation capability by adjusting the reasonable allocation of different resources among clusters.

[0100] The cluster division situation after the cluster division method proposed by the present invention is as follows: Figure 4 shown.

[0101] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0102] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A distributed power cluster partitioning method considering the adjustable capacity and response speed of flexible resources, characterized in that: The following steps are involved: S1: Collect basic data of the distribution network system and generate the initial population; S2: Use electrical distance based on reactive sensitivity to calculate cluster modularity index; S3: Calculate the cluster active power balance index according to the active power source output and load conditions; S4: Calculate the cluster flexibility response capability index based on the adjustable capacity and adjustment speed of each adjustable resource in the cluster; S5: Calculate the energy storage unit allocation index based on the flexibility response capability and energy storage capacity of each cluster; S6: Calculate the comprehensive performance index that comprehensively considers the modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index as the objective function of cluster division, and use the improved adaptive genetic algorithm to solve the cluster division result.

2. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S1, the basic data of the distribution network system includes the network topology, line parameters, various active source output data, load data, and power adjustment range and adjustment speed of the flexible resources.

3. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S2, the electrical distance expression based on the reactive voltage sensitivity matrix is: Where: D QV，ij is the electrical distance between nodes i and j based on reactive voltage sensitivity; n is the number of nodes in the system; S QV，in and S QV，jn are the reactive voltage sensitivity coefficients of nodes i, j and their connected nodes respectively. The expression of the modularity index is: Where: ρ is the modularity index; A ij is the weight of the edge connecting nodes i and j; K i and K j are the weights and values ​​of all edges connected to nodes i and j respectively; C i and C j are the clusters where nodes i and j are located respectively; δ is a 0-1 function, if C i =C j , then δ(C i , C j )=1, otherwise it is 0; m is the sum of the number of network edges. Among them, the weight of the edge connecting nodes i and j is A ij The calculation formula is as follows:

4. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S3, the expression of the active power balance index is as follows: Where: is the cluster active power balance index, γ P is the active balance of the cluster, N c is the total number of clusters. Among them, the cluster active balance γ P The calculation formula is as follows: Where: γ P is the active balance of the cluster; P c represents the active power that can be provided by all active power sources in the cluster, and P n It represents the sum of all active loads in the cluster.

5. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S4, the cluster flexibility response capability index is expressed as follows: Where: φ F is the cluster flexibility response capability index, N c is the total number of clusters. dev,c is the deviation area of ​​the response curve of the cth cluster, S dev,0 It is the deviation area of ​​the output curve without adjustment means. Among them, the cluster response curve deviation area S dev The calculation formula is as follows: Where: T is the research period, P L (t) is the net load curve of the cluster, P G (t) is the output curve of the cluster. Output curve deviation area S without regulation means dev，0 The calculation formula is as follows: Where: P l0 is the cluster net load, P g0 It is the total output of all active sources in the cluster without any regulation means. At this time, the energy storage device has no charging or discharging behavior.

6. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S5, the expression of the energy storage unit allocation index is as follows: in, represents the sum of all energy storage capacities in the cth cluster, Represents the sum of the capacities of all energy storage devices in the distribution network.

7. A distributed power cluster partitioning method considering flexible resource adjustable capacity and response speed according to claim 1, characterized in that: In S6, the expression of the comprehensive performance index is as follows: Where: k1, k2, k3 and k4 are the weights of the modularity index, active power balance index, cluster flexibility response capability index and energy storage unit allocation index respectively, and k1+k2+k3+k4=1.

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