Distribution network distributed photovoltaic control method, device, terminal equipment and storage medium

Abstract

The present invention discloses a distributed photovoltaic control method, device, terminal equipment and storage medium for a distribution network. The method comprises: acquiring basic data of the distribution network; establishing a cluster division comprehensive index according to the basic data; wherein the cluster division comprehensive index comprises: a source-load matching index, a modularity index, an active matching index and a reactive matching index; dividing the distribution network photovoltaic clusters according to the cluster division comprehensive index, determining the number of photovoltaic clusters in the distribution network and the nodes contained in each photovoltaic cluster; constructing an objective function according to the basic data with the minimum abandonment rate as the goal; and constructing the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint of the objective function based on the basic data; solving the objective function to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimum; and regulating each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed photovoltaic control in distribution networks, and in particular to a distributed photovoltaic control method, device, terminal equipment and storage medium for distribution networks. Background Art

[0002] Photovoltaic power generation, as an emerging energy source, can replace traditional high-pollution and high-energy-consuming energy sources, and it is an inevitable trend that it will gradually occupy a dominant position; the advantage of a large number of photovoltaic power generation connected to the distribution network is that it meets the electricity needs of some users, especially enhancing the power supply needs and system reliability in areas with energy shortages; at the same time, the large-scale distributed photovoltaic power generation connected to the distribution network has also brought adverse effects on the planning and operation of the distribution network. At present, many studies have adopted the group control mode based on cluster division. This method classifies the characteristics of each node according to different needs, and can make full use of the similarity of each node within the cluster to perform cluster management of the distribution network system, which provides great convenience for the operation and regulation of the distribution network containing a high proportion of distributed photovoltaic power generation.

[0003] Distributed power generation cluster division refers to the process of optimizing the boundaries and number of clusters according to the system network structure, node load characteristics and cluster division indicators, dividing the system into several cluster (or sub-area) structures, and thus obtaining the partitioning results; the purpose of cluster division is to aggregate nodes with high electrical coupling and relatively complementary power into a cluster, and to make the power flowing through the interactive branches between clusters stable and the interactive power small.

[0004] The clustering methods proposed in existing literature usually use a single indicator to divide the distributed power planning and operation control in a certain stage of the distribution network, and the main research work focuses on the clustering of the operation control stage, without considering the correlation between the distributed power planning stage and the operation control stage. Commonly used clustering index criteria mainly include: 1) The electrical distance index in the power grid partitioning theory, which is used to measure the sensitivity of the voltage amplitude to the injected reactive power; 2) The modularity index in the complex network community theory, which is used to quantitatively describe the community structure characteristics of the power grid. Based on previous studies, some literature takes the planning of low-voltage distribution networks as an application scenario, adds the power balance index, and conducts further research on the index criteria and division methods of clustering. Based on the existing research, modularity index, active power matching index and reactive power matching index can be considered comprehensively; among them, modularity index belongs to structural function, which can make the electrical coupling between nodes in the cluster strong, and the electrical connection between clusters loose, which is convenient for the management of different clusters; active power matching index and reactive power matching index belong to functional index, which can improve the local absorption capacity of distributed photovoltaic connected to the distribution network cluster and reduce the transmission of active power and reactive power between clusters, so that the cluster has sufficient self-regulation ability. However, active power matching index and reactive power matching index only indicate the matching of active power and reactive power capacity in the cluster when the distributed photovoltaic and load are both rated capacity, and cannot reflect the fluctuation of distributed photovoltaic output characteristics and load power consumption patterns in different regions. In fact, the load power in the distribution network system is not a single value, but will change with the power consumption pattern, and the photovoltaic output will also fluctuate. Therefore, it is necessary to further consider the source-load matching at different times in the cluster, reduce the energy transfer between clusters, and thus improve the photovoltaic regulation effect. Therefore, it is urgent to introduce source-load characteristic matching indicators based on the existing indicators to make up for the deficiencies in active power matching and reactive power matching, reflect the matching of photovoltaic output and load power consumption in the distribution network, and improve the photovoltaic regulation effect. Summary of the invention

[0005] The embodiments of the present invention provide a distributed photovoltaic control method, device, terminal equipment and storage medium for a distribution network, which can improve the photovoltaic cluster division and photovoltaic control effect.

[0006] An embodiment of the present invention provides a distributed photovoltaic control method for a distribution network, comprising:

[0007] Obtain basic data of the distribution network; wherein, the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network;

[0008] Establishing a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index and a reactive matching index;

[0009] Divide the distribution network photovoltaic clusters according to the comprehensive cluster division index, and determine the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster;

[0010] According to the basic data, an objective function is constructed with the goal of minimizing the abandoned light rate; and based on the basic data, a power system flow constraint, a node voltage constraint, a branch flow constraint and a DG injection power constraint of the objective function are constructed;

[0011] Under the constraints of the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint, the objective function is solved to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized;

[0012] Each photovoltaic cell connected to the distribution network is regulated according to the photovoltaic capacity of each photovoltaic cluster.

[0013] Furthermore, the source-load matching index is specifically:

[0014]

[0015] in, It is the source-load matching index; is the total number of clusters; Indicates the number of clusters; Indicates Moment The sum of the photovoltaic output values ​​within the cluster; Indicates Moment The total power consumption of the loads in the cluster;

[0016] The modularity index is specifically:

[0017]

[0018]

[0019]

[0020] in, is the modularity index; Nodes in the power network and nodes The edge weights between them; is the sum of the weights of all edges in the power network; To connect nodes The sum of all edge weights of ; To connect nodes The sum of all edge weights of ; Representation Node and nodes Whether they belong to the same cluster, in the node and nodes When they belong to the same cluster, The value is 1, at the node and nodes When they do not belong to the same cluster, The value is 0;

[0021] The active power matching index is specifically:

[0022]

[0023] in, It is the active power matching index; is the number of clusters; For the The sum of the maximum active outputs of all photovoltaic power sources in a cluster; For the The sum of the maximum active power of each node load in the cluster;

[0024] The reactive power matching index is specifically:

[0025]

[0026]

[0027] in, It is the reactive power matching index; For cluster The reactive power matching amount within the group; is the number of clusters; is the reactive power supply value of PV in the cluster; is the reactive power demand value of the nodes in the cluster;

[0028] The cluster division comprehensive index is specifically:

[0029]

[0030] in, Classify comprehensive indicators for clusters; , , and is the weight parameter.

[0031] Furthermore, the objective function is specifically:

[0032]

[0033] in, is the objective function; For Node The current value; node The resistance value of the branch; is the number of branches in the distribution network.

[0034] Furthermore, the power system flow constraint is specifically:

[0035]

[0036] in, Injection node Active power of Injection node Reactive power; For Node The voltage amplitude of Contains all nodes in the system connected nodes; is the real part of the node admittance matrix; is the imaginary part of the node admittance matrix; For Node and nodes The phase angle difference between

[0037] The node voltage constraint is specifically:

[0038]

[0039] in, For Node The upper limit of the voltage amplitude is For Node The lower limit of the voltage amplitude; For Node The voltage amplitude of

[0040] The branch power flow constraints are specifically:

[0041]

[0042] in, For Line Active power of For Line The maximum active power allowed to pass;

[0043] The DG injection power constraint is specifically:

[0044]

[0045] in, Distributed photovoltaic active power injected into the distribution network; It is the maximum active power of distributed photovoltaic that can be injected into the distribution network.

[0046] Furthermore, it also includes: generating an optimization configuration model according to a particle swarm algorithm and a grey wolf algorithm;

[0047] The method of dividing the photovoltaic clusters of the distribution network according to the comprehensive index of cluster division, and determining the number of photovoltaic clusters of the distribution network and the nodes included in each photovoltaic cluster, includes:

[0048] The photovoltaic clusters of the distribution network are divided according to the optimization configuration model and the comprehensive index of cluster division, and the number of photovoltaic clusters in the distribution network and the nodes contained in each photovoltaic cluster are determined.

[0049] Furthermore, generating an optimization configuration model according to the particle swarm algorithm and the grey wolf algorithm includes:

[0050] A mathematical model of a basic particle swarm is constructed according to the particle swarm algorithm; wherein the mathematical model of the basic particle swarm includes the positions of the particles;

[0051] According to the gray wolf algorithm, a mathematical model of the gray wolf surrounding its prey is constructed; wherein the mathematical model of the gray wolf surrounding its prey includes the current position of the gray wolf, the positions of several leader wolves related to the gray wolf, and the distances between the gray wolf and the leader wolves;

[0052] The position of the particle is used as the current position of the gray wolf in the gray wolf algorithm;

[0053] The optimal configuration model for solving the final position of the particle is determined according to the position of the particle, the position of each leader wolf and the distance between each leader wolf and the position of the particle.

[0054] Furthermore, the photovoltaic clusters of the distribution network are divided according to the optimization configuration model and the comprehensive index of cluster division, and the number of photovoltaic clusters of the distribution network and the nodes included in each photovoltaic cluster are determined, including:

[0055] Under the comprehensive index of cluster division with different weight combinations, the photovoltaic clusters of the distribution network are divided according to the optimal configuration model. When the final position of the particles is determined, the number of photovoltaic clusters in the distribution network and the nodes contained in each photovoltaic cluster are obtained.

[0056] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0057] An embodiment of the present invention provides a distributed photovoltaic control device for a distribution network, including: a data acquisition module, an indicator establishment module, a cluster division module, an objective function construction module, a function solving module and a control module;

[0058] The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network;

[0059] The index establishment module is used to establish a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index and a reactive matching index;

[0060] The cluster division module is used to divide the distribution network photovoltaic clusters according to the cluster division comprehensive index, and determine the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster;

[0061] The objective function construction module is used to construct an objective function based on the basic data with the goal of minimizing the abandonment rate; and construct the power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints of the objective function based on the basic data;

[0062] The function solving module is used to solve the objective function under the constraints of the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized;

[0063] The control module is used to control each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster.

[0064] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a distributed photovoltaic control method for a distribution network described in the above-mentioned embodiment of the invention is implemented.

[0065] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distributed photovoltaic control method for a distribution network as described in the above-mentioned embodiment of the invention.

[0066] The following beneficial effects are achieved by implementing the present invention:

[0067] The present invention provides a distributed photovoltaic control method, device, terminal equipment and storage medium for a distribution network. The control method obtains basic data of the distribution network, establishes a cluster division comprehensive index including a source-load matching index, a modularity index, an active matching index and a reactive matching index according to the basic data, and then divides the distribution network photovoltaic clusters according to the cluster division comprehensive index, and determines the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster; according to the basic data, establishes an objective function and its corresponding power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints with the goal of minimizing the abandonment rate, and solves the objective function under the above constraints to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized, and then regulates each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster; by introducing the source-load matching index, the insufficiency of active matching and reactive matching during cluster division is compensated, and the matching situation of photovoltaic output and load power consumption in the distribution network can be reflected, thereby improving the photovoltaic cluster division and photovoltaic control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flow chart of a distributed photovoltaic control method for a distribution network provided by an embodiment of the present invention.

[0069] Figure 2 This is a 10kV 57-node feeder topology diagram in a certain county.

[0070] Figure 3 It is the photovoltaic output curve.

[0071] Figure 4 It is the load curve of each area.

[0072] Figure 5 This is a comparison chart of various indicators of cluster division results.

[0073] Figure 6 It is the cluster division result obtained by traditional indicators.

[0074] Figure 7 It is the cluster division result obtained by the indicator of the present invention.

[0075] Figure 8 It is a structural schematic diagram of a distributed photovoltaic control device for a distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] like Figure 1 As shown, a distributed photovoltaic control method for a distribution network provided by an embodiment of the present invention includes:

[0078] Step S1: Obtain basic data of the distribution network; wherein the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network;

[0079] Step S2: establishing a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index, and a reactive matching index;

[0080] Step S3: Divide the distribution network photovoltaic clusters according to the comprehensive cluster division index, and determine the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster;

[0081] Step S4: constructing an objective function based on the basic data and taking the minimum abandonment rate as the goal; and constructing the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint of the objective function based on the basic data;

[0082] Step S5: Under the constraints of the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint, the objective function is solved to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized;

[0083] Step S6: regulating each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster.

[0084] Before step S1, the method further includes establishing a distribution network system model according to the application scenario of the photovoltaic control method.

[0085] For step S1, basic data of the distribution network is obtained according to the established distribution network system model; wherein the basic data include: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network.

[0086] In addition, it is also necessary to obtain distribution network system data and relevant data on high-proportion distributed photovoltaics connected to the distribution network, including the rated active power, rated reactive power, electrical distance between load nodes, power consumption curves of different load types, relevant locations of photovoltaics connected to the distribution network, rated capacity of photovoltaics connected to the distribution network and typical daily output data curve of photovoltaics.

[0087] For step S2, a comprehensive index for cluster division is established based on the basic data obtained in step S1. The comprehensive index for cluster division includes a source-load matching index that considers the matching of the photovoltaic output curve with the power consumption patterns of different loads, a modularity index that measures the degree of electrical coupling between load nodes in the system, an active matching index that reflects the matching of the sum of the rated capacity of distributed photovoltaics and the rated load of the nodes in the same cluster, and a reactive matching index that reflects the balance of reactive supply and demand in the cluster.

[0088] The source-load matching index is to use the matching degree between the photovoltaic output curve and the load curve of each node as the first index for photovoltaic cluster division; among them, the photovoltaic output curve With load curve Taking a day as the length of time, divide it into 24 hours, and use the cosine of the angle between the photovoltaic output value and the load value corresponding to each hour to define the similarity measure of the two curves. The source-load matching index is specifically:

[0089]

[0090] in, is the source-load matching index, , The closer it is to 1, the With curve The more similar, if the value is 1, it means that the two curves completely overlap. The closer it is to 0, the With curve The weaker the similarity; is the total number of clusters; Indicates the number of clusters; Indicates Moment The sum of the photovoltaic output values ​​within the cluster; Indicates Moment The total power consumption of the loads in the cluster.

[0091] The modularity index is used to measure the degree of electrical coupling between nodes in the distribution network system. The modularity index is specifically:

[0092]

[0093]

[0094]

[0095]

[0096] in, is the modularity index; Nodes in the power network and nodes The edge weight between them (edge ​​weight for short); is the sum of the weights of all edges in the power network; To connect nodes The sum of all edge weights of ; To connect nodes The sum of all edge weights of ; Representation Node and nodes Whether they belong to the same cluster, in the node and nodes When they belong to the same cluster, The value is 1, at the node and nodes When they do not belong to the same cluster, The value is 0;

[0097] It should be noted that in the calculation of the edge weights above, the edge weights of the power network are determined by the electrical distances of each node. The electrical distance is calculated using the sensitivity method, and the electrical distance matrix is ​​defined by the voltage sensitivity matrix to represent the degree of coupling between nodes.

[0098] The electrical distance between nodes can measure the degree of electrical coupling between two nodes in the network, and its formula is:

[0099]

[0100] in, is the active voltage sensitivity matrix; is the node voltage amplitude change, is the node active power change; matrix Middle Line Column Elements Representation Node Active power change unit value corresponding to the node The change in voltage;

[0101] The expression of voltage sensitivity is:

[0102]

[0103] in, is the voltage sensitivity, i.e., the node When the active power changes by unit value, the change in its own voltage amplitude and the node The ratio of the voltage amplitude change, The larger the node Voltage changes on nodes The smaller the impact of voltage changes, that is, the greater the electrical distance between the two nodes, the lower the degree of coupling; since the nodes in the area affect each other, the active power changes of the other nodes will also affect the nodes. and nodes The voltage amplitude.

[0104] node With Node The electrical distance between them is:

[0105]

[0106] in, For Node With Node The electrical distance between

[0107] The expression for determining the edge weight between nodes based on the electrical distance is:

[0108]

[0109] in, For Node With Node The edge weights between them; It is the maximum electrical distance between any two nodes in the system.

[0110] The active power matching index is based on the ratio of the sum of the rated capacity of distributed photovoltaics in the same cluster to the sum of the rated load of the nodes; the active power matching index is specifically:

[0111]

[0112] in, It is the active power matching index; is the number of clusters; For the The sum of the maximum active outputs of all photovoltaic power sources in a cluster; For the The sum of the maximum active power of each node load in the cluster;

[0113] The reactive power matching index is based on the ratio of the PV reactive power to the reactive power required by the load within the same cluster. The reactive power matching index is specifically:

[0114]

[0115]

[0116] in, It is the reactive power matching index; For cluster The reactive power matching amount within the group; is the number of clusters; is the reactive power supply value of PV in the cluster; is the reactive power demand value of the nodes in the cluster; define the division The sum of reactive matching of clusters It is the reactive power matching index.

[0117] In summary, the weighted combination of source-load matching index, modularity index, active matching index and reactive matching index can obtain the comprehensive index of cluster division for evaluating the advantages and disadvantages of regional distribution network cluster division, which is:

[0118]

[0119] in, Classify comprehensive indicators for clusters; , , and is the weight parameter corresponding to each indicator.

[0120] For step S3, in a preferred embodiment, it also includes: generating an optimization configuration model based on a particle swarm algorithm and a gray wolf algorithm; dividing the distribution network photovoltaic clusters according to the comprehensive cluster division index, determining the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster, including: dividing the distribution network photovoltaic clusters according to the optimization configuration model and the comprehensive cluster division index, determining the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster.

[0121] Specifically, the present invention obtains an improved optimization configuration model based on the combination of the existing particle swarm algorithm and the grey wolf algorithm, and combines it with the comprehensive index of cluster division to realize the division of photovoltaic clusters in the distribution network.

[0122] In a preferred embodiment, the generation of the optimization configuration model according to the particle swarm algorithm and the gray wolf algorithm includes: constructing a mathematical model of a basic particle swarm according to the particle swarm algorithm; wherein the mathematical model of the basic particle swarm includes the position of the particles; constructing a mathematical model of a gray wolf surrounding its prey according to the gray wolf algorithm; wherein the mathematical model of the gray wolf surrounding its prey includes the current position of the gray wolf, the positions of several leader wolves related to the gray wolf, and the distance between the gray wolf and each leader wolves; using the position of the particle as the current position of the gray wolf in the gray wolf algorithm; and determining the final position of the particle according to the position of the particle, the position of each leader wolves, and the distance between each leader wolf and the position of the particle.

[0123] In a preferred embodiment, the photovoltaic clusters of the distribution network are divided according to the optimization configuration model and the comprehensive index of cluster division, and the number of photovoltaic clusters of the distribution network and the nodes contained in each photovoltaic cluster are determined, including: under the comprehensive index of cluster division with different weight combinations, the photovoltaic clusters of the distribution network are divided according to the optimization configuration model, and when the final position of the particle is determined, the number of photovoltaic clusters of the distribution network and the nodes contained in each photovoltaic cluster are obtained.

[0124] Specifically, the mathematical model of the basic particle swarm is constructed according to the particle swarm algorithm, which is expressed as follows:

[0125]

[0126]

[0127] in, is the velocity of the particle; is the position of the particle; is the current iteration number; is the inertia weight, generally 1; is the learning factor, generally ; To be evenly distributed Random numbers in the interval; For the The individual in The optimal position of an individual in the dimension; For the population in The optimal position of the group.

[0128] The above particle swarm algorithm is applied to the distributed photovoltaic cluster division. The position of the particle is the final division result, which includes the number of clusters and the nodes contained in each cluster. Assume that the number of nodes in the complex power grid to be divided is , then the position and velocity of the particle are dimensional particle. Particles passing through The position after iteration can be recorded as:

[0129]

[0130] in, It is At iteration The cluster ID of the node. , For complex power grids Any node among the nodes, then the node With Node are classified into the same cluster.

[0131] No. Particles passing through The speed after iterations can be recorded as:

[0132]

[0133] in, For Node In the The amount by which the cluster number changes from iteration to iteration.

[0134] The particle swarm algorithm is discretized, and the solution to the cluster partition problem is in the integer domain. In order to combine the particle swarm algorithm with cluster partitioning, it is necessary to ensure that the search range of the variable is in the integer space. Therefore, the velocity vector is rounded up when the velocity is updated. The model is:

[0135]

[0136] Since the position update of the particle swarm algorithm is relatively simple and it is easy to fall into the local optimal situation, in order to solve the problems of the particle swarm algorithm, the present invention introduces the gray wolf algorithm to optimize the particle swarm algorithm, thereby obtaining an optimal configuration model. The gray wolf algorithm and the particle swarm algorithm are both heuristic algorithms. The gray wolf algorithm uses the method of surrounding prey to find the best solution, and has a strong global optimization ability. The mathematical model of the gray wolf surrounding prey is:

[0137]

[0138]

[0139] in, Indicates the distance between the gray wolf and its prey; for the location of prey; The current location of the gray wolf;

[0140] , is the coefficient vector, and the expression is:

[0141]

[0142]

[0143] in, To control the parameters, it decreases linearly from 2 to 0 as the number of iterations increases; , is a random vector between 0 and 1.

[0144] When hunting, the gray wolf The position of is updated according to the positions of the other three leader wolves, which are , , , where the leader wolf The distance expression from the candidate gray wolf is:

[0145]

[0146] in, For the Gray Wolf location;

[0147] Leading the Wolf The distance expression from the candidate gray wolf is:

[0148]

[0149] in, For the Gray Wolf location;

[0150] Leading the Wolf The distance expression from the candidate gray wolf is

[0151]

[0152] in, For the Gray Wolf location;

[0153] Gray Wolf Gray Wolf , , The forward position is , , , the expression is:

[0154]

[0155]

[0156]

[0157]

[0158] Among them, the current candidate gray wolf and gray wolf , , The distance is , , , Gray Wolf The final position of .

[0159] The particle swarm algorithm is combined with the gray wolf algorithm. The update speed equation of the direction is:

[0160]

[0161] in, and The particles are The speed and position of the approaching direction, is the inertia weight, generally 1; is the learning factor, generally ; To be evenly distributed Random numbers in the interval; For the The individual in The optimal position of an individual in the dimension; For the population in The optimal position of the group of dimensions; is the current iteration number.

[0162] Particles to the gray wolf The update speed equation of the direction is:

[0163]

[0164] in, and The particles are Speed ​​and position when approaching;

[0165] Particles to the gray wolf The update speed equation of the direction is:

[0166]

[0167] in, and The particles are Speed ​​and position when approaching;

[0168] The particle will be in different positions after moving in different directions. The position expression after the direction moves forward is:

[0169]

[0170] in, and Respectively and The second iteration particle moves to the gray wolf The speed when approaching, For the The second iteration particle moves to the gray wolf The position obtained after the direction is close;

[0171] Particles to the gray wolf The position expression after the direction moves forward is:

[0172]

[0173] in, and Respectively and The second iteration particle moves to the gray wolf The speed when approaching, For the The second iteration particle moves to the gray wolf The position obtained after the direction is close;

[0174] Particles to the gray wolf The position expression after the direction moves forward is:

[0175]

[0176] in, and Respectively and The second iteration particle moves to the gray wolf The speed when approaching, For the The second iteration particle moves to the gray wolf The position obtained after the direction is close;

[0177] The final position of the particle is the average of the positions obtained after the particle approaches in three directions, and the expression is:

[0178]

[0179] The photovoltaic clusters of the distribution network are divided through the optimization configuration model and cluster division comprehensive index that combines the above-mentioned particle swarm algorithm and gray wolf algorithm; different values ​​are assigned to the weight parameters corresponding to each indicator according to the comprehensive index of cluster division, and different weight combinations are performed. The above-mentioned optimization configuration model is used to cluster the distribution network system, and the cluster division results are compared according to the needs to finally obtain the best weight combination scheme and the cluster division results under the best weight scheme. Users can combine indicators with different weights according to different needs to obtain different cluster division results, and then make corresponding adjustments to the indicator weights based on the comparison of the results, and different results can be obtained according to specific scenarios and needs. For a weight combination, the final position of the particle is sought, and at the final position of the particle When determining, a division result including the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster is obtained; further, the division results under various weight combinations are compared, and the weight parameters of the weight combination are optimized.

[0180] For step S4, according to the basic data in step S1, an objective function is constructed with the goal of minimizing the abandonment rate; and the power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints of the objective function are constructed based on the basic data.

[0181] In a preferred embodiment, the objective function is specifically:

[0182]

[0183] in, is the objective function, which is used to represent the network loss; For Node The current value; node The resistance value of the branch; is the number of branches in the distribution network.

[0184] In a preferred embodiment, the power system flow constraint is specifically:

[0185]

[0186] in, Injection node Active power of Injection node Reactive power; For Node The voltage amplitude of Contains all nodes in the system connected nodes; is the real part of the node admittance matrix; is the imaginary part of the node admittance matrix; For Node and nodes The phase angle difference between

[0187] Node voltage constraints, specifically:

[0188]

[0189] in, For Node The upper limit of the voltage amplitude; For Node The lower limit of the voltage amplitude; For Node The voltage amplitude of

[0190] The branch power flow constraints are specifically:

[0191]

[0192] in, For Line Active power of For Line The maximum active power allowed to pass;

[0193] The DG injection power constraint is specifically:

[0194]

[0195] in, Distributed photovoltaic active power injected into the distribution network; It is the maximum active power of distributed photovoltaic that can be injected into the distribution network.

[0196] For step S5 and step S6, according to the established objective function and its corresponding constraints, the objective function is solved with and without considering cluster division to obtain the photovoltaic capacity with the minimum abandonment rate, and each photovoltaic connected to the distribution network is regulated according to the photovoltaic capacity.

[0197] The following beneficial effects are achieved by implementing the present invention:

[0198] 1. Considering the volatility of photovoltaic access and the variability of load power consumption at different times, the source-load matching index is considered when dividing clusters to make up for the deficiencies of active matching and reactive matching when dividing clusters. It can reflect the matching of photovoltaic output and load power consumption in the distribution network, and then improve the photovoltaic cluster division and photovoltaic regulation effect.

[0199] 2. The present invention improves the particle swarm algorithm and optimizes the particle swarm algorithm by combining it with the gray wolf algorithm, so that there are multiple possibilities for particle position changes, which can reduce the situation where the result falls into the local optimum and make the final calculation result more accurate.

[0200] 3. The present invention designs multiple cluster division indicators. Users can combine the indicators with different weights and optimize them according to their own needs to obtain cluster division results by comprehensively considering the functions of each cluster preference, so that the cluster division results are flexible and can be applied to cluster division in various distribution network topologies.

[0201] In order to verify the solution proposed by the present invention, a case analysis is carried out on a 10kV feeder system in a certain county. The node feeder topology diagram is shown in the figure below: Figure 2 As shown in the figure. The topology diagram includes a total of 57 nodes, of which bus 0 is used as the reference node, there are 56 load nodes, and the total system load demand is 1874+j896kVA. In this power grid, the system is divided into 12 load areas, including 15 photovoltaic power sources, with a total photovoltaic installed capacity of 1.2MW. The system area division and the corresponding photovoltaic output curve are shown in the figure. Figure 3 As shown, the load curve corresponding to each area is as follows Figure 4 As shown. Among them, Figure 4 (a) is the load curve diagram of area 1 and area 4; Figure 4 (b) is the load curve diagram of area 2 and area 8; Figure 4 (c) is the load curve diagram of area 3 and area 11; Figure 4 (d) is the load curve diagram of area 5 and area 12; Figure 4 (e) is the load curve diagram of area 6 and area 9; Figure 4 (f) is the load curve diagram of area 7 and area 10.

[0202] The installed capacities of the six photovoltaic power sources in this power grid are shown in the following table. The corresponding photovoltaic output curves and the corresponding load curves for each area are shown in the following table. Figure 4 shown.

[0203]

[0204] The optimization configuration model is used for optimization calculation. Different cluster division schemes are obtained according to different weight combinations of indicators. The most suitable weight combination and its corresponding cluster division scheme are selected according to actual needs. The cluster division results under comprehensive performance and when considering only the modularity indicator are obtained through calculation as shown in the following table:

[0205]

[0206] In order to make a more intuitive comparison of the data, the data of each indicator obtained from the four schemes are used as follows: Figure 5 By comparing with the bar chart shown in the figure, it can be seen that the use of this comprehensive index for division results in poor performance only in modularity compared to a single modularity index, but its source-load matching and active and reactive matching performance within the cluster are both good. The use of this comprehensive index can adjust the weights of each index according to actual needs. The division results can not only take into account the electrical coupling between system nodes and the source-load matching characteristics when the photovoltaic output changes within the cluster and the load power consumption fluctuate due to external factors, but can also maximize the effective use of photovoltaics within the cluster while considering the fluctuations in distributed photovoltaic output and load, thereby facilitating the operation and regulation of the distribution network to a great extent. Figure 6 As shown in, this is the cluster division result obtained by using traditional indicators, such as Figure 7 The figure shows the result of clustering according to the index proposed in the present invention. It can be clearly seen that the result of the clustering according to the present invention is better than the result obtained by the traditional index.

[0207] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0208] like Figure 8 As shown, an embodiment of the present invention provides a distributed photovoltaic control device for a distribution network, including: a data acquisition module, an indicator establishment module, a cluster division module, an objective function construction module, a function solving module and a control module;

[0209] The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network;

[0210] The index establishment module is used to establish a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index and a reactive matching index;

[0211] The cluster division module is used to divide the distribution network photovoltaic clusters according to the cluster division comprehensive index, and determine the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster;

[0212] The objective function construction module is used to construct an objective function based on the basic data with the goal of minimizing the abandonment rate; and construct the power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints of the objective function based on the basic data;

[0213] The function solving module is used to solve the objective function under the constraints of the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint to generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized;

[0214] The control module is used to control each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster.

[0215] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0216] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0217] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0218] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distributed photovoltaic control method for a distribution network described in any one of the present invention is implemented.

[0219] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0220] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0221] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0222] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0223] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distributed photovoltaic control method for a distribution network as described in any one of the present inventions.

[0224] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0225] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distributed photovoltaic control method for a distribution network, characterized in that: include: Obtain basic data of the distribution network; wherein, the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network; Establishing a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index and a reactive matching index; A mathematical model of a basic particle swarm is constructed according to the particle swarm algorithm; wherein the mathematical model of the basic particle swarm includes the positions of the particles; According to the gray wolf algorithm, a mathematical model of the gray wolf surrounding its prey is constructed; wherein the mathematical model of the gray wolf surrounding its prey includes the current position of the gray wolf, the positions of several leader wolves related to the gray wolf, and the distances between the gray wolf and the leader wolves; The position of the particle is used as the current position of the gray wolf in the gray wolf algorithm; An optimization configuration model for solving the final position of the particle is determined according to the position of the particle, the position of each leader wolf and the distance between each leader wolf and the position of the particle; Divide the distribution network photovoltaic clusters according to the optimization configuration model and the comprehensive index of cluster division, and determine the number of distribution network photovoltaic clusters and the nodes contained in each photovoltaic cluster; According to the basic data, an objective function is constructed with the goal of minimizing the abandoned light rate; and based on the basic data, a power system flow constraint, a node voltage constraint, a branch flow constraint and a DG injection power constraint of the objective function are constructed; Under the constraints of the power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints, the objective function is solved in the case of considering the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster and in the case of not considering the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster, and the photovoltaic capacity of each photovoltaic cluster is generated when the abandonment rate is minimized; Each photovoltaic cell connected to the distribution network is regulated according to the photovoltaic capacity of each photovoltaic cluster.

2. A distributed photovoltaic control method for a distribution network as claimed in claim 1, characterized in that: The source-load matching index is specifically: in, It is the source-load matching index; is the total number of clusters; Indicates the number of clusters; Indicates Moment The sum of the photovoltaic output values ​​within the cluster; Indicates Moment The total power consumption of the loads in the cluster; The modularity index is specifically: in, is the modularity index; Nodes in the power network and nodes The edge weights between them; is the sum of the weights of all edges in the power network; To connect nodes The sum of all edge weights of ; To connect nodes The sum of all edge weights of ; Representation Node and nodes Whether they belong to the same cluster, in the node and nodes When they belong to the same cluster, The value is 1, at the node and nodes When they do not belong to the same cluster, The value is 0; The active power matching index is specifically: in, It is the active power matching index; is the number of clusters; For the The sum of the maximum active outputs of all photovoltaic power sources in a cluster; For the The sum of the maximum active power of each node load in the cluster; The reactive power matching index is specifically: in, It is the reactive power matching index; For cluster The reactive power matching amount within the group; is the number of clusters; is the reactive power supply value of PV in the cluster; is the reactive power demand value of the nodes in the cluster; The cluster division comprehensive indicators are specifically: in, Classify comprehensive indicators for clusters; , , and is the weight parameter.

3. A distributed photovoltaic control method for a distribution network as claimed in claim 1, characterized in that: The objective function is specifically: in, is the objective function; For Node The current value; node The resistance value of the branch; is the number of branches in the distribution network.

4. A distributed photovoltaic control method for a distribution network as claimed in claim 1, characterized in that: The power system flow constraints are specifically: in, Injection node Active power of Injection node Reactive power; For Node The voltage amplitude of Contains all nodes in the system connected nodes; is the real part of the node admittance matrix; is the imaginary part of the node admittance matrix; For Node and nodes The phase angle difference between The node voltage constraint is specifically: in, For Node The upper limit of the voltage amplitude; For Node The lower limit of the voltage amplitude; For Node The voltage amplitude of The branch power flow constraints are specifically: in, For Line Active power of For Line The maximum active power allowed to pass; The DG injection power constraint is specifically: in, Distributed photovoltaic active power injected into the distribution network; It is the maximum active power of distributed photovoltaic that can be injected into the distribution network.

5. A distributed photovoltaic control method for a distribution network as claimed in claim 1, characterized in that: The method of dividing the distribution network photovoltaic clusters according to the optimization configuration model and the comprehensive index of cluster division, and determining the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster, includes: Under the comprehensive index of cluster division with different weight combinations, the photovoltaic clusters of the distribution network are divided according to the optimal configuration model. When the final position of the particles is determined, the number of photovoltaic clusters in the distribution network and the nodes contained in each photovoltaic cluster are obtained.

6. A distributed photovoltaic control device for a distribution network, characterized in that: include: Data acquisition module, indicator establishment module, cluster division module, objective function construction module, function solution module and control module; The data acquisition module is used to acquire basic data of the distribution network; wherein the basic data includes: photovoltaic output value in the cluster at each moment, load power consumption at each moment, edge weight between nodes, maximum active power of load at each node, photovoltaic reactive power supply value in each cluster, reactive power demand value of nodes in each cluster, current value of each node, resistance value of the branch where each node is located, active power injected into each node, reactive power injected into each node, voltage amplitude of each node, admittance matrix of each node, phase angle difference between nodes, upper limit of voltage amplitude of each node, lower limit of voltage amplitude of each node, number of branches in the distribution network, connection data between nodes, active power of each line, maximum active power allowed to pass through each line, distributed photovoltaic active power and maximum distributed photovoltaic active power that can be injected into the distribution network; The index establishment module is used to establish a comprehensive index for cluster division according to the basic data; wherein the comprehensive index for cluster division includes: a source-load matching index, a modularity index, an active matching index and a reactive matching index; The cluster division module is used to construct a mathematical model of a basic particle group according to a particle swarm algorithm; wherein the mathematical model of the basic particle group includes the position of the particles; construct a mathematical model of a gray wolf surrounding its prey according to a gray wolf algorithm; wherein the mathematical model of a gray wolf surrounding its prey includes the current position of the gray wolf, the positions of several leader wolves related to the gray wolf, and the distance between the gray wolf and each leader wolves; the position of the particle is used as the current position of the gray wolf in the gray wolf algorithm; the optimization configuration model for solving the final position of the particle is determined according to the position of the particle, the position of each leader wolf, and the distance between each leader wolf and the position of the particle; the distribution network photovoltaic clusters are divided according to the optimization configuration model and the comprehensive index of cluster division, and the number of distribution network photovoltaic clusters and the nodes included in each photovoltaic cluster are determined; The objective function construction module is used to construct an objective function based on the basic data with the goal of minimizing the abandoned light rate; and construct the power system flow constraints, node voltage constraints, branch flow constraints and DG injection power constraints of the objective function based on the basic data; The function solving module is used to solve the objective function under the constraints of the power system flow constraint, node voltage constraint, branch flow constraint and DG injection power constraint, taking into account the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster and not taking into account the number of photovoltaic clusters in the distribution network and the nodes included in each photovoltaic cluster, and generate the photovoltaic capacity of each photovoltaic cluster when the abandonment rate is minimized; The control module is used to control each photovoltaic connected to the distribution network according to the photovoltaic capacity of each photovoltaic cluster.

7. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distributed photovoltaic control method for a distribution network as claimed in any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distributed photovoltaic control method for a distribution network as described in any one of claims 1 to 5.

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