Distribution network voltage distributed optimization control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of active power distribution network voltage regulation and control, in particular to a power distribution network voltage distributed optimization control method, device and equipment and a storage medium, and the method comprises the steps: firstly carrying out cluster division on a power distribution network to obtain a plurality of clusters, and determining a plurality of key nodes according to the plurality of clusters; then constructing a power distribution network voltage optimization model; taking the plurality of key points as reactive power injection and / or reactive power absorption points, and optimizing the power flow of the power distribution network by using a plurality of reactive power quantities determined by the power distribution network voltage optimization model; and finally, adjusting the voltage of the voltage out-of-limit node by adjusting the reactive power of the key power supply node in the cluster. The method is more reasonable in the aspects of cluster division scale and cluster capacity distribution, operation time is saved, key nodes of the cluster are selected, the number of control nodes can be reduced, voltage control efficiency is improved, power on-site balance is facilitated through cluster voltage coordination control and on-site real-time control, and the problem of voltage out-of-limit is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of active distribution network voltage regulation, and in particular to a distribution network voltage distributed optimization control method, device, equipment and storage medium. Background Art

[0002] Active distribution network refers to a distribution network that has a large number of distributed power sources and bidirectional power flow, also known as active distribution network. Active distribution network is an energy exchange and distribution network, where power flow and fault current flow in both directions. The power flow and fault analysis, voltage and reactive power control, relay protection methods and operation management measures of traditional distribution network are no longer suitable and need to be adjusted and improved accordingly.

[0003] Among them, the distribution network connected to large-scale distributed photovoltaics is prone to voltage exceeding the limit. Using only cluster control is likely to cause uneven distribution of voltage regulation tasks. Centralized control cannot respond quickly to the randomness of distributed photovoltaics, and local control makes it difficult to achieve overall coordination.

[0004] Based on this, it is necessary to develop and design a distributed optimization control method for distribution network voltage. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, equipment and storage medium for distributed optimization control of distribution network voltage, which are used to solve the problem of voltage over-limit in distribution network that is difficult to coordinate and control distributed photovoltaic access in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for distributed optimization control of distribution network voltage, comprising:

[0007] Clustering the distribution network to obtain a plurality of clusters, and determining a plurality of key nodes according to the plurality of clusters, wherein each key node corresponds to a cluster;

[0008] With the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network, and with the distribution network operating conditions as constraints, a distribution network voltage optimization model is constructed;

[0009] The multiple key points are used as reactive injection and / or reactive absorption points, a cluster reactive voltage control optimization strategy and the distribution network voltage optimization model are used to determine a plurality of reactive quantities, and the power flow of the distribution network is optimized according to the plurality of reactive quantities, wherein each reactive quantity corresponds to a key point;

[0010] Within the cluster, the voltage of the voltage-exceeding node is adjusted by adjusting the reactive power of the key power node, wherein the key power node is the power node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

[0011] In a possible implementation manner, the clustering of the distribution network to obtain multiple clusters includes:

[0012] Obtaining a comprehensive modularity index, a balance index, and a membership index, and constructing a comprehensive cluster division index according to the comprehensive modularity index, the balance index, and the membership index;

[0013] Each node in the distribution network is regarded as a first cluster, and the comprehensive cluster division index value of each node is used as the first index value;

[0014] For each node that is independently clustered, randomly select clusters to combine to obtain the second cluster;

[0015] The comprehensive cluster division index value of each second cluster is used as the second index value;

[0016] If the second index value is a positive increment relative to the first index value, the second cluster corresponding to the second index value is used as the first cluster, and the second index value is used as the first index value;

[0017] Otherwise, the nodes joining the second cluster are considered as the first cluster;

[0018] If there is a cluster in which nodes form an independent cluster among the multiple first clusters, the process jumps to the step of randomly selecting other nodes for each node that forms an independent cluster to combine to obtain a second cluster.

[0019] In a possible implementation manner, determining a plurality of key nodes according to the plurality of clusters includes:

[0020] A key node is selected for each cluster according to a node voltage observability index and a reactive power and / or active power controllability index, wherein the node voltage observability index is determined according to a first formula, which is:

[0021]

[0022] Where OB is the observability index of the key node e in cluster C, o n is the number of nodes in the current cluster, S QUie and S PUie They are the reactive and active voltage sensitivity relationships between node i and key node e in cluster C, respectively;

[0023] The reactive power and / or active power controllability index is determined according to a second formula, which is:

[0024]

[0025] In the formula, CO is the controllability index of the key node e in the cluster C, U enis the voltage of the key node e in cluster C, Q dn and P dn are the reactive and active power of the photovoltaic nodes in cluster C, respectively. dn is the total number of PV nodes in cluster C.

[0026] In a possible implementation, the voltage optimization model of the distribution network is constructed with the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network and with the distribution network operation conditions as constraints, including:

[0027] The first objective function is constructed by minimizing the network loss of the distribution network;

[0028] The second objective function is constructed by minimizing the voltage deviation of the distribution network nodes;

[0029] Constructing a comprehensive objective function according to the first objective function and the second objective function;

[0030] Construct power flow constraints, reactive power constraints, and voltage constraints;

[0031] The comprehensive objective function, the power flow constraint, the reactive power constraint and the voltage constraint are combined to obtain the distribution network voltage optimization model.

[0032] In a possible implementation, the first objective function is:

[0033]

[0034] In the formula, f 1 is the first objective function, n is the total number of distribution network nodes, G ij is the conductance of the branch formed by nodes i and j, U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, θ ij is the voltage phase angle difference between node i and node j;

[0035] The second objective function is:

[0036]

[0037] In the formula, f 2 is the second objective function, U i is the voltage amplitude of node i, U p.u. is the reference voltage;

[0038] The comprehensive objective function is:

[0039] F=ε 1 f 1 +ε 2 f 2

[0040] Where: ε 1 and ε 2 They are the first weight coefficient and the second weight coefficient respectively.

[0041] In a possible implementation manner, the method of determining multiple reactive quantities by using the cluster reactive voltage control optimization strategy and the distribution network voltage optimization model includes:

[0042] Get multiple particles and initialize the position and velocity of each particle;

[0043] Selecting the plurality of key nodes as reactive power injection and / or absorption points;

[0044] Set the specific parameters of the objective function, the specific parameters of the constraints, and the particle search range in each cluster;

[0045] Calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model;

[0046] Determining whether the fitness of the iterations of the adjacent first predetermined number of times has changed;

[0047] If the iterative fitness changes, the individual fitness value and the group fitness value of each particle are determined according to the constraint conditions and fitness of the distribution network voltage optimization model, and the position and speed of each particle are updated according to the individual fitness value and the group fitness value, and the individual fitness value and the group fitness value are updated;

[0048] If the number of iterations has not reached the maximum number of iterations, jump to the step of calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model;

[0049] Select the particle with the highest group fitness value as the target particle;

[0050] The plurality of reactive quantities are determined based on the target particles.

[0051] In a possible implementation, adjusting the voltage of the voltage-exceeding node by adjusting the reactive power of the key power supply node within the cluster includes:

[0052] Divide the nodes in the cluster into an overvoltage node set and a normal voltage node set;

[0053] The node with the largest voltage amplitude in the overvoltage node set is regarded as a dangerous voltage node;

[0054] According to the reactive voltage sensitivity matrix, find the node with the largest reactive voltage sensitivity between the dangerous voltage node and the critical power supply node;

[0055] The reactive power adjustment amount is determined according to a third formula, the reactive voltage sensitivity value of the key power supply node, and the voltage over-limit value of the dangerous voltage node, wherein the third formula is:

[0056]

[0057] Where: α is the adjustment constant, ΔU max is the voltage over-limit value of the dangerous voltage node, S QV,max is the reactive voltage sensitivity value corresponding to the key power supply node.

[0058] In a second aspect, an embodiment of the present invention provides a distribution network voltage distributed optimization control device, which is used to implement the distribution network voltage distributed optimization control method as described in the first aspect or any possible implementation of the first aspect, and the distribution network voltage distributed optimization control device includes:

[0059] A cluster division module, used to perform cluster division on the distribution network to obtain multiple clusters, and determine multiple key nodes according to the multiple clusters, wherein each key node corresponds to a cluster;

[0060] A distribution network voltage optimization model construction module is used to construct a distribution network voltage optimization model with the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network and with the distribution network operation conditions as constraints;

[0061] A power flow adjustment module, used to use the multiple key points as reactive injection and / or reactive absorption points, determine multiple reactive quantities using a cluster reactive voltage control optimization strategy and the distribution network voltage optimization model, and optimize the power flow of the distribution network according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point;

[0062] as well as,

[0063] The node voltage adjustment module is used to adjust the voltage of the voltage-exceeding node within the cluster by adjusting the reactive power of the key power supply node, wherein the key power supply node is the power supply node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

[0064] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0065] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0066] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0067] The embodiment of the present invention discloses a distributed optimization control method for distribution network voltage, which firstly divides the distribution network into clusters to obtain multiple clusters, and determines multiple key nodes according to the multiple clusters, wherein each key node corresponds to a cluster; then, with the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network, and with the distribution network operating conditions as constraints, a distribution network voltage optimization model is constructed; then, the multiple key points are used as reactive injection and / or reactive absorption points, and a cluster reactive voltage control optimization strategy and the distribution network voltage optimization model are used to determine multiple reactive quantities, and the power flow of the distribution network is optimized according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point; finally, for the inside of the cluster, the voltage of the voltage-over-limit node is adjusted by adjusting the reactive power of the key power supply node, wherein the key power supply node is the power supply node with the greatest reactive power sensitivity to the voltage-over-limit node. The present invention is more reasonable in terms of cluster division scale and cluster capacity allocation, saves computing time, selects key nodes of the cluster, can reduce the number of control nodes, and improves the efficiency of voltage control. At the same time, cluster voltage coordination control and local real-time control are beneficial to local power balance, effectively solving the voltage limit problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0069] Figure 1 is a flow chart of a method for distributed optimization control of distribution network voltage provided by an embodiment of the present invention;

[0070] Figure 2 It is a flow chart of cluster reactive power voltage control optimization strategy provided by an embodiment of the present invention;

[0071] Figure 3 It is a functional block diagram of a distribution network voltage distributed optimization control device provided in an embodiment of the present invention;

[0072] Figure 4It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0075] The following is a detailed description of an embodiment of the present invention. This example is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0076] Figure 1 The present invention provides a flow chart of a method for distributed optimization control of distribution network voltage.

[0077] like Figure 1 As shown, it shows a flow chart of the implementation of the distribution network voltage distributed optimization control method provided by the embodiment of the present invention, which is described in detail as follows:

[0078] In step 101, a distribution network is clustered to obtain a plurality of clusters, and a plurality of key nodes are determined according to the plurality of clusters, wherein each key node corresponds to a cluster.

[0079] In some implementations, the step of dividing the distribution network into clusters to obtain a plurality of clusters includes:

[0080] Obtaining a comprehensive modularity index, a balance index, and a membership index, and constructing a comprehensive cluster division index according to the comprehensive modularity index, the balance index, and the membership index;

[0081] Each node in the distribution network is regarded as a first cluster, and the comprehensive cluster division index value of each node is used as the first index value;

[0082] For each node that is independently clustered, randomly select clusters to combine to obtain the second cluster;

[0083] The comprehensive cluster division index value of each second cluster is used as the second index value;

[0084] If the second index value is a positive increment relative to the first index value, the second cluster corresponding to the second index value is used as the first cluster, and the second index value is used as the first index value;

[0085] Otherwise, the nodes joining the second cluster are considered as the first cluster;

[0086] If there is a cluster in which nodes form an independent cluster among the multiple first clusters, the process jumps to the step of randomly selecting other nodes for each node that forms an independent cluster to combine to obtain a second cluster.

[0087] In some implementations, determining a plurality of key nodes according to the plurality of clusters includes:

[0088] A key node is selected for each cluster according to a node voltage observability index and a reactive power and / or active power controllability index, wherein the node voltage observability index is determined according to a first formula, which is:

[0089]

[0090] Where OB is the observability index of the key node e in cluster C, o n is the number of nodes in the current cluster, S QUie and S PUie They are the reactive and active voltage sensitivity relationships between node i and key node e in cluster C, respectively;

[0091] The reactive power and / or active power controllability index is determined according to a second formula, which is:

[0092]

[0093] In the formula, CO is the controllability index of the key node e in the cluster C, U en is the voltage of the key node e in cluster C, Q dn and P dn are the reactive and active power of the photovoltaic nodes in cluster C, respectively. dn is the total number of PV nodes in cluster C.

[0094] Exemplarily, when the embodiment of the present invention dynamically divides the distribution network into clusters based on the Louvain algorithm, it is necessary to determine the comprehensive index of cluster division, and the comprehensive index of cluster division includes:

[0095] (1) Comprehensive modularity index

[0096] In the selection of cluster partitioning indicators, the modularity index is used to determine the scale and structural strength of the partition network. The modularity index ρ is expressed as:

[0097]

[0098] Where: A ij is the adjacency matrix. If there is a connection between node i and node j in the distribution network, A ij =1, if there is no connection between node i and node j, A ij =0; is the network adjacency matrix A ij The sum of all edge weights in k i is the sum of the edge weights connected to node i; k j is the sum of the weights of all edges connected to node j; assuming that nodes i and j are in the same cluster, then δ(i,j) = 1, otherwise δ(i,j) = 0.

[0099] The reactive voltage sensitivity matrix can more accurately reflect the reactive coupling degree of different nodes, and the original edge weight matrix A is replaced by the mean sensitivity of different nodes. ij , the improved edge weight η QUij It can be expressed as

[0100]

[0101] Where: S QUij is the reactive voltage sensitivity factor of node i to node j; S QUji is the reactive voltage sensitivity factor of node j to node i.

[0102] The cluster can change the reactive power output by regulating distributed photovoltaics, thereby supporting the system voltage. Distributed photovoltaics of different capacities have different voltage support capabilities for other nodes, which not only affects the reactive power balance of the cluster, but also affects the results of cluster division. The reactive power support capacity of node i for node j is α QUij It can be expressed as:

[0103]

[0104] Where: S QUij is the reactive voltage sensitivity factor of node i to node j, S QUjj is the reactive voltage sensitivity factor of node j to node j, Q QUi is the photovoltaic adjustable reactive capacity of node i.

[0105] The final improved weight matrix A QUij It can be expressed as:

[0106] A QUij =η QUij +α QUij

[0107] Use the final improved weight matrix A QUij Replace the original edge weight matrix Aij , then the improved modularity ρ QUa It can be expressed as:

[0108]

[0109] Considering the internal structural characteristics of the cluster, the aggregation index ρ is constructed QUb It can be expressed as:

[0110]

[0111] In the formula, o n is the number of nodes in the current cluster, S QUij is the reactive voltage sensitivity factor of node i to node j, c is the number of the current cluster, N c is the total number of clusters.

[0112] Based on the above derivation process, the comprehensive modularity index ρ within the cluster QU It can be expressed as:

[0113] ρ QU =ρ QUa +ρ QUb

[0114] (2) Power balance index

[0115] Distributed photovoltaic access may cause problems such as power reverse voltage exceeding the limit of the distribution network. Power reverse will not only increase line losses sharply, but also pose the risk of equipment overload and burning. Taking full account of the power balance problem within the cluster, a power balance index is constructed, which can be expressed as.

[0116]

[0117] In the formula, β is the power balance index; β P is the total active power balance index within the cluster; β Q It is the total reactive power balance index within the cluster.

[0118]

[0119] Where: P clu,c (t) is the net power characteristic of cluster c at time t, max(P clu,c (t)) is the maximum net power characteristic of cluster c in the typical time-varying scenario duration, T is the typical time-varying scenario duration; T = 24, N c is the number of clusters.

[0120] The reactive power balance index is used to describe the balance relationship between reactive power demand and supply within the cluster.

[0121]

[0122] In the formula, β Q is the total reactive power balance index within the cluster, Q c is the reactive balance degree of cluster c, Q sup,c , Q need,c are the maximum reactive power output and reactive power demand value of the cth cluster respectively.

[0123] (3) Node membership index

[0124] In order to improve the balance of cluster scale and reduce the complexity of cluster optimization model, the node membership index is defined as:

[0125]

[0126] In the formula, λ is the node membership index. The larger its value, the more balanced the sizes of clusters. D(i,c) is the membership degree of nodes in cluster c that are connected to node i in the same cluster. - is the sum of the number of edges between nodes in clusters other than cluster c, D(i,c - ) is not in the same cluster as node i, but the connected node is in cluster n - degree of affiliation.

[0127] (4) Comprehensive cluster division indicators

[0128] Under the premise of balanced cluster size, in order to ensure the structure and functionality of the cluster, a comprehensive cluster partitioning index is proposed by combining the comprehensive modularity index, power balance index and node membership index as the objective function of cluster partitioning. The expression of the comprehensive cluster partitioning index τ is as follows:

[0129] τ=ω 1 ρ QU +ω 2 β+ω 3 λ

[0130] Where: 1 ,ω 2 ,ω 3 are the weight coefficients of each indicator, and ω 1 +ω 2 +ω 3 = 1. Different weight coefficients are selected according to different clustering objectives to obtain corresponding clustering results.

[0131] Based on the comprehensive cluster division index, the Louvain algorithm is used to dynamically divide the distribution network into clusters. The specific steps are as follows:

[0132] Step 1: Obtain distribution network related data, regard each node in the network as a cluster, and calculate the comprehensive cluster division index value τ of each node 0 .

[0133] Step 2: Initial node i starts to randomly select node j to combine to obtain a new cluster, and calculate its comprehensive cluster division index value τ 1 , calculate the comprehensive cluster partitioning index increment τ 1 -τ 0 , if the increment is positive, nodes i and j are considered to be in the same cluster.

[0134] Step 3: Treat the current cluster as a new cluster and continue to combine it with other clusters, repeating step 2. After traversing all nodes in the entire distribution network, the first cluster division is completed.

[0135] Step 4: Determine whether there is a cluster with 1 node in the entire system. If so, repeat Step 2 and Step 3 for this cluster. If not, the cluster division phase ends and the result of the current cluster division is output.

[0136] In the step 1, the distribution network is dynamically divided into clusters based on the Louvain algorithm, and the optimal cluster division result is output. The key nodes in the cluster are selected by calculating the observability index and controllability index of the nodes of each cluster.

[0137] The selection of key nodes of the present invention takes into account the observability of node voltage and reactive / active power controllability, which respectively characterize the sensitivity of key nodes to voltage changes and reactive / active power changes of other load nodes, and are specifically defined as:

[0138]

[0139] Where OB is the observability index of the key node e in cluster C, o n is the number of nodes in the current cluster, S QUie and S PUie are the reactive and active voltage sensitivity relationships between node i and key node e in cluster C, CO is the controllability index of key node e in cluster C, and U en is the voltage of the key node e in cluster C, Q dn and P dn are the reactive and active power of the photovoltaic nodes in cluster C, respectively. dn is the total number of PV nodes in cluster C.

[0140] In step 102, a distribution network voltage optimization model is constructed with the goal of minimizing voltage deviations of multiple nodes in the distribution network and minimizing network losses of multiple nodes in the distribution network and with the distribution network operating conditions as constraints.

[0141] In some implementations, step 102 includes:

[0142] The first objective function is constructed by minimizing the network loss of the distribution network;

[0143] The second objective function is constructed by minimizing the voltage deviation of the distribution network nodes;

[0144] Constructing a comprehensive objective function according to the first objective function and the second objective function;

[0145] Construct power flow constraints, reactive power constraints, and voltage constraints;

[0146] The comprehensive objective function, the power flow constraint, the reactive power constraint and the voltage constraint are combined to obtain the distribution network voltage optimization model.

[0147] In some embodiments, the first objective function is:

[0148]

[0149] In the formula, f 1 is the first objective function, n is the total number of distribution network nodes, G ij is the conductance of the branch formed by nodes i and j, U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, θ ij is the voltage phase angle difference between node i and node j;

[0150] The second objective function is:

[0151]

[0152] In the formula, f 2 is the second objective function, U i is the voltage amplitude of node i, U p.u. is the reference voltage;

[0153] The comprehensive objective function is:

[0154] F=ε 1 f 1 +ε 2 f 2

[0155] Where: ε 1 and ε 2 They are the first weight coefficient and the second weight coefficient respectively.

[0156] Exemplarily, the embodiment of the present invention takes the minimum voltage deviation and network loss of each node in the distribution network as the goal, and combines the constraints to construct a distribution network voltage optimization model:

[0157] (1) With the minimum network loss of the distribution network f 1 As the objective function, it can be expressed as:

[0158]

[0159] Where n is the total number of distribution network nodes, G ij is the conductance of the branch ij formed by nodes i and j, U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, θ ij is the voltage phase angle difference between node i and node j.

[0160] (2) Taking the minimum voltage deviation of the distribution network node as f 2 It can be expressed as:

[0161]

[0162] Where U i is the voltage amplitude of node i, U p.u. is the reference voltage.

[0163] (3) Comprehensive objective function

[0164] The comprehensive objective function of the distribution network voltage optimization model can be expressed as:

[0165] F=ε 1 f 1 +ε 2 f 2

[0166] Where: ε 1 and ε 2 They are the first weight coefficient and the second weight coefficient respectively, which can be selected according to the demand for distribution network optimization.

[0167] With the goal of minimizing the voltage deviation and network loss of each node in the distribution network, a distribution network voltage optimization model is constructed, which includes the following constraints:

[0168] (1) Power flow constraints

[0169]

[0170] Where P i and Q i Respectively represent the active and reactive loads at node i; G ij is the conductance between node i and node j, B ijis the susceptance between node i and node j, θ ij is the voltage phase angle difference between node i and node j.

[0171] (2) Reactive power and voltage upper and lower limit constraints

[0172]

[0173] Where: Q min With Q max are the maximum and minimum reactive power injected into the node respectively. To avoid excessive injection of the node, this paper takes the upper and lower limits of the photovoltaic capacity of each node as max with U min They are the maximum and minimum node voltages allowed by the system respectively.

[0174] In step 103, the multiple key points are used as reactive injection and / or reactive absorption points, and the cluster reactive voltage control optimization strategy and the distribution network voltage optimization model are used to determine multiple reactive quantities, and the power flow of the distribution network is optimized according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point.

[0175] In some implementations, step 103 includes:

[0176] Get multiple particles and initialize the position and velocity of each particle;

[0177] Selecting the plurality of key nodes as reactive power injection and / or absorption points;

[0178] Set the specific parameters of the objective function, the specific parameters of the constraints, and the particle search range in each cluster;

[0179] Calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model;

[0180] Determining whether the fitness of the iterations of the adjacent first predetermined number of times has changed;

[0181] If the iterative fitness changes, the individual fitness value and the group fitness value of each particle are determined according to the constraint conditions and fitness of the distribution network voltage optimization model, and the position and speed of each particle are updated according to the individual fitness value and the group fitness value, and the individual fitness value and the group fitness value are updated;

[0182] If the number of iterations has not reached the maximum number of iterations, jump to the step of calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model;

[0183] Select the particle with the highest group fitness value as the target particle;

[0184] The plurality of reactive quantities are determined based on the target particles.

[0185] By way of example, the embodiment of the present invention utilizes the key nodes within each cluster to call reactive resources, utilizes the cluster reactive voltage control optimization strategy, optimizes the reactive voltage of the distribution network, improves the power flow distribution of the entire network, and uses POS to optimize the specific process of reactive voltage control as follows: Figure 2 As shown, the steps are as follows:

[0186] Step 1: Initialize the position and velocity of the particle;

[0187] Step 2: Select key nodes in each cluster as reactive power injection and / or absorption points based on the cluster division results, and set the algorithm parameters in POS;

[0188] Step 3: Set the distribution network objective function and constraints. Set the constraints as U min =0.95 and U max =1.05, Q min With Q max The upper and lower limits of the photovoltaic capacity of each node; set the particle search range in each cluster in the distribution network;

[0189] Step 4: Calculate the fitness function F = ε 1 f 1 +ε 2 f 2 , determine whether the fitness of the adjacent 20 iterations has changed. If not, jump to Step 7;

[0190] Step 5: Find individual fitness value and group fitness value according to constraints and fitness; update particle position and speed, update individual fitness value and group fitness value;

[0191] Step 6: Determine the number of iterations. If it has not reached the maximum number of iterations, return to Step 4.

[0192] Step 7: Get the optimal solution and output the reactive power required by each key node, thereby improving the power distribution of the entire network.

[0193] In step 104, within the cluster, the voltage of the voltage-exceeding node is adjusted by adjusting the reactive power of the key power node, wherein the key power node is the power node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

[0194] In some implementations, step 104 includes:

[0195] Divide the nodes in the cluster into an overvoltage node set and a normal voltage node set;

[0196] The node with the largest voltage amplitude in the overvoltage node set is regarded as a dangerous voltage node;

[0197] According to the reactive voltage sensitivity matrix, find the node with the largest reactive voltage sensitivity between the dangerous voltage node and the critical power supply node;

[0198] The reactive power adjustment amount is determined according to a third formula, the reactive voltage sensitivity value of the key power supply node, and the voltage over-limit value of the dangerous voltage node, wherein the third formula is:

[0199]

[0200] Where: α is the adjustment constant, ΔU max is the voltage over-limit value of the dangerous voltage node, S QV,max is the reactive voltage sensitivity value corresponding to the key power supply node.

[0201] Exemplarily, an embodiment of the present invention monitors the voltage level of the distribution network nodes in real time. When the voltage of the nodes in the group exceeds the limit, an event triggering method is used to perform local real-time control, so as to realize rapid voltage management under the random fluctuation of source and load power by using an event triggering method.

[0202] The local real-time control is the real-time control of the local voltage within the group based on the voltage sensitivity coefficient.

[0203] The local voltage in the cluster is controlled in real time based on the voltage sensitivity coefficient, using an event trigger method. When the voltage of a node in the cluster exceeds the limit, the reactive power of the distributed photovoltaic system is first adjusted to restore the voltage level. The nodes in the cluster are divided into an overvoltage node set and a normal voltage node set. The node with the largest voltage amplitude in the overvoltage node set is recorded as a dangerous voltage node, and the corresponding voltage over-limit amplitude is recorded as ΔU max According to the reactive voltage sensitivity matrix S QV , find the node with the largest reactive voltage sensitivity between the dangerous voltage node and the critical power node, and the corresponding reactive voltage sensitivity value is calculated as S QV,max According to S QV , calculate the reactive power adjustment ΔQ required to correct the dangerous voltage node voltage.

[0204]

[0205] Where: α is the adjustment constant, ΔU max For voltage exceeding the limit value, S QV,max is the reactive voltage sensitivity value corresponding to the key power supply node.

[0206] The distributed optimization control method for distribution network voltage of the present invention is implemented as follows: first, the distribution network is clustered to obtain multiple clusters, and multiple key nodes are determined according to the multiple clusters, wherein each key node corresponds to a cluster; then, the voltage deviation of multiple nodes in the distribution network is minimized and the network loss of multiple nodes in the distribution network is minimized, and the distribution network operation conditions are constrained to construct a distribution network voltage optimization model; then, the multiple key points are used as reactive injection and / or reactive absorption points, and multiple reactive quantities are determined by using the cluster reactive voltage control optimization strategy and the distribution network voltage optimization model, and the flow of the distribution network is optimized according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point; finally, for the inside of the cluster, the voltage of the voltage over-limit node is adjusted by adjusting the reactive power of the key power supply node, wherein the key power supply node is the power supply node with the greatest reactive power sensitivity to the voltage over-limit node. The present invention is more reasonable in terms of cluster division scale and cluster capacity allocation, saves computing time, selects key nodes of the cluster, can reduce the number of control nodes, improves the efficiency of voltage control, and is conducive to local power balance through cluster voltage coordinated control and local real-time control, effectively solving the voltage over-limit problem.

[0207] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0208] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0209] Figure 3 301, the distributed optimization control device for distribution network voltage includes: a cluster division module 301, a distribution network voltage optimization model construction module 302, a power flow adjustment module 303 and a node voltage adjustment module 304, wherein:

[0210] The cluster division module 301 is used to divide the distribution network into clusters to obtain multiple clusters, and determine multiple key nodes according to the multiple clusters, wherein each key node corresponds to a cluster;

[0211] A distribution network voltage optimization model construction module 302 is used to construct a distribution network voltage optimization model with the goal of minimizing voltage deviations of multiple nodes in the distribution network and minimizing network losses of multiple nodes in the distribution network and with distribution network operation conditions as constraints;

[0212] The power flow adjustment module 303 is used to use the multiple key points as reactive injection and / or reactive absorption points, determine multiple reactive quantities by using the cluster reactive voltage control optimization strategy and the distribution network voltage optimization model, and optimize the power flow of the distribution network according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point;

[0213] The node voltage adjustment module 304 is used to adjust the voltage of the voltage-exceeding node within the cluster by adjusting the reactive power of the key power supply node, wherein the key power supply node is the power supply node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

[0214] Figure 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the steps in the above-mentioned distribution network voltage distributed optimization control method and the embodiment are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0215] Exemplarily, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0216] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0217] The processor 400 may be a central processing unit (CPU), or 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. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0218] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0219] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0220] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0221] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0222] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0223] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0224] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0225] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. 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. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0226] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distributed optimization control method for distribution network voltage, characterized in that: include: Clustering the distribution network to obtain a plurality of clusters, and determining a plurality of key nodes according to the plurality of clusters, wherein each key node corresponds to a cluster; With the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network, and with the distribution network operating conditions as constraints, a distribution network voltage optimization model is constructed; The multiple key points are used as reactive injection and / or reactive absorption points, a cluster reactive voltage control optimization strategy and the distribution network voltage optimization model are used to determine a plurality of reactive quantities, and the power flow of the distribution network is optimized according to the plurality of reactive quantities, wherein each reactive quantity corresponds to a key point; Within the cluster, the voltage of the voltage-exceeding node is adjusted by adjusting the reactive power of the key power node, wherein the key power node is the power node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

2. The method for distributed optimization control of distribution network voltage according to claim 1, characterized in that: The clustering of the distribution network to obtain a plurality of clusters includes: Obtaining a comprehensive modularity index, a balance index, and a membership index, and constructing a comprehensive cluster division index according to the comprehensive modularity index, the balance index, and the membership index; Each node in the distribution network is regarded as a first cluster, and the comprehensive cluster division index value of each node is used as the first index value; For each node that is independently clustered, randomly select clusters to combine to obtain the second cluster; The comprehensive cluster division index value of each second cluster is used as the second index value; If the second index value is a positive increment relative to the first index value, the second cluster corresponding to the second index value is used as the first cluster, and the second index value is used as the first index value; Otherwise, the nodes joining the second cluster are considered as the first cluster; If there is a cluster in which nodes form an independent cluster among the multiple first clusters, the process jumps to the step of randomly selecting other nodes for each node that forms an independent cluster to combine to obtain a second cluster.

3. The method for distributed optimization control of distribution network voltage according to claim 1, characterized in that: The determining a plurality of key nodes according to the plurality of clusters comprises: A key node is selected for each cluster according to a node voltage observability index and a reactive power and / or active power controllability index, wherein the node voltage observability index is determined according to a first formula, which is: Where OB is the observability index of the key node e in cluster C, o n is the number of nodes in the current cluster, S QUie and S PUie They are the reactive and active voltage sensitivity relationships between node i and key node e in cluster C, respectively; The reactive power and / or active power controllability index is determined according to a second formula, which is: In the formula, CO is the controllability index of the key node e in the cluster C, U en is the voltage of the key node e in cluster C, Q dn and P dn are the reactive and active power of the photovoltaic nodes in cluster C, respectively. dn is the total number of PV nodes in cluster C.

4. The method for distributed optimization control of distribution network voltage according to claim 1, characterized in that: The voltage optimization model of the distribution network is constructed with the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network and with the distribution network operation conditions as constraints, including: The first objective function is constructed by minimizing the network loss of the distribution network; The second objective function is constructed by minimizing the voltage deviation of the distribution network nodes; Constructing a comprehensive objective function according to the first objective function and the second objective function; Construct power flow constraints, reactive power constraints, and voltage constraints; The comprehensive objective function, the power flow constraint, the reactive power constraint and the voltage constraint are combined to obtain the distribution network voltage optimization model.

5. The method for distributed optimization control of distribution network voltage according to claim 4, characterized in that: The first objective function is: Where f1 is the first objective function, n is the total number of distribution network nodes, G ij is the conductance of the branch formed by nodes i and j, U i is the voltage amplitude of node i, U j is the voltage amplitude at node j, θ ij is the voltage phase angle difference between node i and node j; The second objective function is: Where f2 is the second objective function, U i is the voltage amplitude of node i, U p.u. is the reference voltage; The comprehensive objective function is: F=ε1f1+ε2f2 Wherein, ε1 and ε2 are the first weight coefficient and the second weight coefficient respectively.

6. The method for distributed optimization control of distribution network voltage according to any one of claims 1 to 5, characterized in that: The method of determining a plurality of reactive quantities by using the cluster reactive voltage control optimization strategy and the distribution network voltage optimization model includes: Get multiple particles and initialize the position and velocity of each particle; Selecting the plurality of key nodes as reactive power injection and / or absorption points; Set the specific parameters of the objective function, the specific parameters of the constraints, and the particle search range in each cluster; Calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model; Determining whether the fitness of the iterations of the adjacent first predetermined number of times has changed; If the iterative fitness changes, the individual fitness value and the group fitness value of each particle are determined according to the constraint conditions and fitness of the distribution network voltage optimization model, and the position and speed of each particle are updated according to the individual fitness value and the group fitness value, and the individual fitness value and the group fitness value are updated; If the number of iterations has not reached the maximum number of iterations, jump to the step of calculating the fitness of each particle according to the objective function of the distribution network voltage optimization model; Select the particle with the highest group fitness value as the target particle; The plurality of reactive quantities are determined based on the target particles.

7. The method for distributed optimization control of distribution network voltage according to any one of claims 1 to 5, characterized in that: The method of adjusting the voltage of the voltage-exceeding node by adjusting the reactive power of the key power supply node within the cluster includes: Divide the nodes in the cluster into an overvoltage node set and a normal voltage node set; The node with the largest voltage amplitude in the overvoltage node set is regarded as a dangerous voltage node; According to the reactive voltage sensitivity matrix, find the node with the largest reactive voltage sensitivity between the dangerous voltage node and the critical power supply node; The reactive power adjustment amount is determined according to a third formula, the reactive voltage sensitivity value of the key power supply node, and the voltage over-limit value of the dangerous voltage node, wherein the third formula is: Where: α is the adjustment constant, ΔU max is the voltage over-limit value of the dangerous voltage node, S QV,max is the reactive voltage sensitivity value corresponding to the key power supply node.

8. A distributed optimization control device for distribution network voltage, characterized in that: Used to implement the distribution network voltage distributed optimization control method according to any one of claims 1 to 7, the distribution network voltage distributed optimization control device comprises: A cluster division module, used to perform cluster division on the distribution network to obtain multiple clusters, and determine multiple key nodes according to the multiple clusters, wherein each key node corresponds to a cluster; A distribution network voltage optimization model construction module is used to construct a distribution network voltage optimization model with the goal of minimizing the voltage deviation of multiple nodes in the distribution network and minimizing the network loss of multiple nodes in the distribution network and with the distribution network operation conditions as constraints; A power flow adjustment module, used to use the multiple key points as reactive injection and / or reactive absorption points, determine multiple reactive quantities using a cluster reactive voltage control optimization strategy and the distribution network voltage optimization model, and optimize the power flow of the distribution network according to the multiple reactive quantities, wherein each reactive quantity corresponds to a key point; as well as, The node voltage adjustment module is used to adjust the voltage of the voltage-exceeding node within the cluster by adjusting the reactive power of the key power supply node, wherein the key power supply node is the power supply node with the greatest sensitivity to the reactive power of the voltage-exceeding node.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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