Power distribution network-oriented distributed wind power plant self-configured energy storage system power distribution method

Abstract

The invention relates to a power distribution network-oriented distributed wind power plant self-configured energy storage system power distribution method, and belongs to the field of power distribution network operation and control. The method comprises the following steps: S1, acquiring data such as access position and capacity configuration of the distributed wind storage system according to a power distribution network; s2, taking a single distributed wind storage system as a cluster center, and adopting a K-means clustering algorithm to realize node voltage cluster division of the power distribution network according to a voltage sensitivity index; and S3, according to the comprehensive voltage sensitivity, realizing optimal power distribution of the self-configured energy storage system of the high-proportion distributed wind storage system. According to the method, effective voltage regulation control can be performed on the power distribution network under the high proportion of the distributed wind storage system, extra voltage fluctuation and network loss increase caused by insufficient control sensitivity between node clusters of the power distribution network can be avoided, the operation economy of the wind storage system can be improved, and the method has important engineering application value for guaranteeing safe and reliable operation of the power distribution network.

Description

Technical Field

[0001] The invention belongs to the field of distribution network operation and control, and relates to a power distribution method for a distributed wind farm self-distributed energy storage system facing the distribution network. Background Art

[0002] In the context of new power system construction, in order to solve the node voltage over-limit and wind power consumption problems caused by the high proportion of distributed wind storage systems connected to the distribution network, it is necessary to carry out in-depth research on the power allocation method of distributed wind farm self-distributed energy storage system for distribution network to ensure the safe and reliable operation of the distribution network. In terms of power allocation of high proportion of distributed wind storage systems connected to the distribution network, many studies in recent years have been carried out on the voltage regulation control of the distribution network based on the node cluster division. For example, the Chinese invention with publication number CN117595285A uses the community algorithm to obtain the optimal division results of reactive power clusters and active power clusters, and then selects the key node actions in the cluster through the clustering algorithm to improve the cluster voltage regulation capability; there are also methods that use the modularity index based on electrical distance as the basis for cluster division, and take the principle of giving priority to the severely over-limit cluster, and allocate power to each energy storage in the cluster according to the node voltage sensitivity. However, the above methods do not take into account that the location distribution of the distributed wind storage system may affect the voltage regulation sensitivity. If there are multiple distributed wind storage systems in a cluster, new problems such as how to reasonably allocate the output of each distributed wind storage system in the cluster will arise. The present invention uses a single distributed wind-storage system as the clustering center to determine the number of clusters, and does not consider inter-cluster control, which can avoid additional voltage fluctuations and increased network losses caused by insufficient inter-cluster control sensitivity. It will be a new method for power distribution of distributed wind farm self-distributed energy storage systems for distribution networks. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network. After obtaining the access location of the distributed wind storage system, the method first divides the distribution network cluster according to the voltage sensitivity, and then realizes the optimal power allocation of the high-proportion distributed wind storage system according to the comprehensive voltage sensitivity.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network includes the following steps:

[0006] Obtain the access location, capacity configuration, power factor, reactive power configuration and parameter data of the distributed wind storage system in the distribution network;

[0007] Taking a single distributed wind-storage system as the cluster center, the K-means clustering algorithm is used to realize the voltage clustering of distribution network nodes according to the voltage sensitivity index.

[0008] According to the comprehensive voltage sensitivity, the optimal power allocation of high-ratio distributed wind-storage system is achieved.

[0009] Furthermore, the voltage sensitivity index is the voltage sensitivity vector matrix M s , each node to be divided in the distribution network corresponds to M s Each row vector of , the distance between two row vectors represents the electrical distance between two nodes, which is used to characterize the coupling strength between nodes, and the cluster division of distribution network nodes is realized through K-means clustering algorithm.

[0010] Furthermore, the voltage sensitivity vector matrix M s The acquisition process is as follows:

[0011] The power flow equation of the known distribution network is:

[0012]

[0013] Among them, J is the Jacobian matrix; ΔP and ΔQ are the changes of active and reactive power injected into the node respectively; ΔU and Δδ are the changes of node voltage amplitude and node voltage phase angle respectively;

[0014] Perform matrix inverse transformation on the Jacobian matrix J in equation (1) to obtain the corresponding sensitivity matrix J -1 ;

[0015]

[0016] The active and reactive power injected into the node will affect the voltage and phase angle of each node, thus defining the voltage sensitivity matrix M i,j , and further obtain the voltage sensitivity vector matrix M s ;

[0017]

[0018] Among them, i=1,2,…,n; j=1,2,…,m.

[0019] Furthermore, the single distributed wind-storage system is used as the cluster center to determine the number of clusters for the cluster center; effective cluster division meets the following three points: ① Each node in the cluster is strongly coupled with the access point of the distributed wind-storage system; ② The nodes in the cluster are interconnected; ③ With m distributed wind-storage systems as cluster centers, the division result is m clusters; The specific steps of the cluster division process using the hierarchical K-means clustering algorithm are as follows;

[0020] ① Initialization parameter calculation

[0021] According to the distribution network topology and distribution network line parameters, the Jacobian matrix of the distribution network power flow equation is calculated;

[0022] ②Matrix M s Preprocessing

[0023] According to the access node location of the distributed wind energy storage system, M is obtained according to equations (2) to (4): s , to describe the degree of coupling between active / reactive power and node voltage; in order to make the difference in the sensitivity of each node voltage more obvious, the sensitivity is preprocessed and x i,1 ,x i,2 ,…,x i,m To describe the sensitivity of node j to the partitioned node i, the voltage sensitivity component of the distributed wind energy storage system access point j is M i,j The logarithm of the determinant is expressed as follows:

[0024] x i,j =-lg(det(M i,j )) (5)

[0025] Substitute equation (5) into the matrix M s Get the preprocessed matrix M s 'for:

[0026]

[0027] The electrical distance between node i and node j is the Euclidean distance D ij It is expressed as:

[0028]

[0029] ③Get cluster division results

[0030] First, set the number of clusters m and calculate D ij , nodes with similar electrical distances to node j are divided into the same cluster, so that the number of clusters is equal to the number of distributed wind-storage systems m, until all nodes are clustered and a clustering binary tree is generated; to ensure the strong controllability of the control range of the clustered distributed wind-storage system, if there are two points with a distance greater than a certain threshold, the cluster with the most "similar" electrical distance to this part of the nodes is recalculated; finally, m clusters with each distributed wind-storage system as the clustering center are obtained.

[0031] Furthermore, the cluster division result is used to achieve the optimal power allocation of the high-proportion distributed wind energy storage system; the process of determining the output range of the energy storage system is as follows:

[0032] ①Identify the out-of-limit nodes

[0033] According to formula (4), M s Realize clustering of distribution network nodes and initialize the voltage regulation ratio λ of the distributed wind storage system in each cluster j,t=0; energy storage output P s =0; the number of energy storage outputs within the allowable voltage deviation range φ = 0, and then the power flow calculation is performed, the node i with the maximum voltage exceeding the limit in the cluster is selected as the reference object, the control signal is output to the corresponding cluster j, and λ is updated j,t =λ j,t +ω (ω is the update factor), calculate the comprehensive voltage sensitivity μ i,j = det(M i,j );

[0034] ②Calculate the energy storage charging and discharging power

[0035] First, calculate the upper voltage limit U corresponding to the current voltage regulation ratio of cluster j up,j and the lower limit U down,j ;

[0036] U up,j =U N (1+λ j V m ) (8)

[0037] U down,j =U N (1-λ j V m ) (9)

[0038] Among them, V m is the allowable voltage deviation range; U N is the rated voltage of the distribution network; j ∈(0,1];

[0039] Then, calculate the current charging and discharging power. i >U up,j When the energy storage is charged to reduce the voltage in the cluster, the charging power is P cj =(U up,j -U i ) / μ i,j ; when U i down,j When the wind power output cannot meet the voltage regulation requirements, the energy storage discharge will raise the voltage in the cluster, and the energy storage discharge power is P dj =(U down,j -U i ) / μ i,j ;

[0040] ③ Determine the energy storage output range

[0041] Calculate the energy storage output P s =P cj,t or P dj,t , determine whether there is a voltage over-limit after adding the current energy storage output; if there is a voltage over-limit, update λ​j,t , and recalculate P s Until the cluster has no voltage exceeding the limit, φ=φ+1 and output Then return to update λ j,t Step reenters the loop and ends at λ j,t >1 ends the loop and confirms P DEj,t To determine the energy storage output range, each energy storage charging and discharging power can meet the distribution network operation requirements.

[0042] Furthermore, the optimal power allocation of the high-proportion distributed wind-storage system is achieved based on the comprehensive voltage sensitivity by using an intelligent optimization algorithm. Find the optimal output result Get the best output The objective function and constraints are as follows;

[0043] 1) The comprehensive benefit C of the distributed wind-storage system is maximized

[0044] max C=C2-C1 (10)

[0045] ① Daily operation and maintenance cost C1

[0046]

[0047] Among them, C DWG is the unit operation and maintenance cost of the distributed wind farm; C DE P is the operation and maintenance cost of the energy storage unit; wdj,t is the output power of the jth distributed wind farm at time t; P cdj,t is the charging and discharging power of the jth energy storage at time t; Δt is the scheduling time interval;

[0048] ② Daily grid-connected income C2

[0049]

[0050] Among them, K DWSS,t is the unit grid-connected electricity price of the distributed wind-storage system at time t; wj,t and P ej,t are the grid-connected output values ​​of the jth distributed wind farm and energy storage system at time t respectively;

[0051] 2) The sum of the distribution network voltage deviation rate ΔU% is minimum

[0052]

[0053] Among them, U i,t is the voltage amplitude of node i at time t;

[0054] The constraints of the above objective function include distribution network flow constraints, node voltage constraints, energy storage charging and discharging power constraints, energy storage charge state constraints and distributed wind storage system power balance constraints.

[0055] The beneficial effects of the present invention are as follows: the power allocation method for the distributed wind farm self-distributed energy storage system for the distribution network proposed in this scheme first obtains the data such as the access location and capacity configuration of the distributed wind storage system in the distribution network; secondly, with a single distributed wind storage system as the cluster center, the K-means clustering algorithm is used to realize the voltage clustering of the distribution network nodes according to the voltage sensitivity index. Finally, according to the comprehensive voltage sensitivity, the optimal power allocation of the high-proportion distributed wind storage system is realized. The present invention divides the clusters with the distributed wind storage system as the cluster center, and avoids the additional voltage fluctuation and network loss increase caused by insufficient control sensitivity between clusters by distributing the power of the wind farm self-distributed energy storage system. It can effectively regulate the voltage of the distribution network under the high proportion of distributed wind storage system, and at the same time improve the economic efficiency of the wind storage system operation, which has important engineering application value for ensuring the safe and reliable operation of the distribution network.

[0056] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0058] Figure 1 A flow chart of voltage regulation control for distribution network clusters connected to a high-proportion distributed wind-storage system;

[0059] Figure 2 This is the structural diagram of the distributed wind storage system;

[0060] Figure 3 This is the IEEE33 distribution network topology diagram;

[0061] Figure 4 is the net load per unit value;

[0062] Figure 5 Flowchart for cluster partitioning;

[0063] Figure 6 Partition the results into clusters.

[0064] In the figure, 1, 2, 3, ... represent node numbers; ①, ②, ③, ... represent branch numbers. DETAILED DESCRIPTION

[0065] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0066] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0067] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0068] See also Figure 1 to Figure 6 , which is a power allocation method for distributed wind farm self-distributed energy storage systems for distribution networks, mainly includes three steps: obtaining distribution network data, distribution network cluster division and cluster voltage regulation control part.

[0069] Combination Figure 1 The proposed distributed wind farm self-distributed energy storage system power allocation method and implementation steps for the distribution network are described in detail, wherein the distributed wind storage system structure is as follows Figure 2 The effectiveness of the present invention is illustrated by taking the IEEE33 distribution network topology structure containing a distributed wind storage system as an example. Figure 3 As shown in the figure, 1, 2, 3, ... represent node numbers; ①, ②, ③, ... represent branch numbers. The voltage level of the distribution network is 10kV; the node voltage is allowed to offset V m Not more than ±7%U NNode 1 is used as a balancing node; reactive power compensators are installed at nodes 18 and 33, with capacities of 0.8 Mvar and 0.9 Mvar respectively; the base capacity of the distribution network is 10 MVA and the base voltage is 10 kV; the dispatching time is 1 day, the dispatching interval is Δt = 15 min, and T is 96; C DWG =C DE =0.1 yuan / (kW·h), K DWSS As shown in Table 1.

[0070] Table 1

[0071] category Time <![CDATA[Electricity price / [Yuan·(MW·h) -1 > valley 00:00-06:00，22:00-24:00 266 flat 06:00-08:00，12:00-15:00，19:00-22:00 510 peak 08:00-12:00，15:00-19:00 826

[0072] Step 1: Obtain distribution network data. The distributed wind storage system is installed at nodes 10, 18, 20, and 29; the installed capacity of the wind farm in the distributed wind storage system is 6MW, and the power factor is 0.9; the wind speeds at each grid connection point are different, and the confidence interval is [0.9, 1.1]; the capacity configuration of the self-distributed energy storage system is shown in Table 2, the energy storage SOC range is [0.1, 0.9], the initial SOC is 0.5, η = 0.9, and the maximum charge and discharge power is 1.2MW. According to the above parameters, the source and load data of the distribution network are collected and converted into net load per unit value data, such as Figure 4 shown.

[0073] Table 2

[0074]

[0075]

[0076] Step 2: Distribution network cluster division. See the cluster division process for details. Figure 5 As shown in Figure 2, the number of clusters m is set to 4, and a single distributed wind-storage system is used as the cluster center, so it is divided into 4 clusters, as shown in Figure 2. Figure 6 As shown in the figure, 1, 2, 3, ..., represent node numbers; ①, ②, ③, ..., represent branch numbers.

[0077] Step 3: Cluster voltage regulation control part. Inter-cluster control is not considered, and voltage regulation is only performed within the cluster. Since the invention involves a multi-objective optimization problem, a multi-objective particle swarm optimization algorithm based on simulated annealing is used to solve it. The parameters set the population size to 100, the initial temperature to 100°C, the termination temperature to 1°C, the temperature cooling coefficient to 0.8, the maximum number of iterations to 500, and the weights of the sum of the comprehensive benefits of the wind storage system and the voltage deviation rate of the distribution network in the objective function are equal. The forward-backward method is used for the distribution network flow calculation. In the voltage regulation process, ω=0.001 is set. The optimal energy storage output curve is obtained through optimization iteration, and the daily node voltage of the distribution network after voltage regulation is obtained. The voltage fluctuation range can be controlled within [0.9511, 1.0521]. There is no voltage over-limit situation and the voltage fluctuation is small, that is, the 10kV distribution network voltage does not exceed ±7%U N Voltage quality requirements.

[0078] In summary, the power allocation method for the distributed wind farm self-distributed energy storage system for the distribution network proposed in the present invention can effectively control the voltage regulation of the distribution network, suppress voltage fluctuations and reduce network losses, and has important engineering application value for ensuring the safe and reliable operation of the distribution network.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network, characterized by: The following steps are involved: Obtain the access location, capacity configuration, power factor, reactive power configuration and parameter data of the distributed wind storage system in the distribution network; Taking a single distributed wind-storage system as the cluster center, the K-means clustering algorithm is used to realize the voltage clustering of distribution network nodes according to the voltage sensitivity index. According to the comprehensive voltage sensitivity, the optimal power allocation of high-ratio distributed wind-storage system is achieved.

2. The power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network according to claim 1 is characterized in that: The voltage sensitivity index is the voltage sensitivity vector matrix M s , each node to be divided in the distribution network corresponds to M s Each row vector of , the distance between two row vectors represents the electrical distance between two nodes, which is used to characterize the coupling strength between nodes, and the cluster division of distribution network nodes is realized through K-means clustering algorithm.

3. The power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network according to claim 2 is characterized in that: The voltage sensitivity vector matrix M s The acquisition process is as follows: The power flow equation of the known distribution network is: Among them, J is the Jacobian matrix; ΔP and ΔQ are the changes of active and reactive power injected into the node respectively; ΔU and Δδ are the changes of node voltage amplitude and node voltage phase angle respectively; Perform matrix inverse transformation on the Jacobian matrix J in equation (1) to obtain the corresponding sensitivity matrix J -1 ; The active and reactive power injected into the node will affect the voltage and phase angle of each node, thus defining the voltage sensitivity matrix M i,j , and further obtain the voltage sensitivity vector matrix M s ; Among them, i=1,2,…,n; j=1,2,…,m.

4. The power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network according to claim 1, characterized in that: The single distributed wind-storage system is used as the cluster center to determine the number of clusters for the cluster center; effective cluster division meets the following three points: ① Each node in the cluster is strongly coupled with the access point of the distributed wind-storage system; ② The nodes in the cluster are interconnected; ③ With m distributed wind-storage systems as cluster centers, the division result is m clusters; The specific steps of the cluster division process using the hierarchical K-means clustering algorithm are as follows; ① Initialization parameter calculation According to the distribution network topology and distribution network line parameters, the Jacobian matrix of the distribution network power flow equation is calculated; ②Matrix M s Preprocessing According to the access node location of the distributed wind energy storage system, M is obtained according to equations (2) to (4): s , to describe the degree of coupling between active / reactive power and node voltage; in order to make the difference in the sensitivity of each node voltage more obvious, the sensitivity is preprocessed and x i,1 ,x i,2 ,…,x i,m To describe the sensitivity of node j to the partitioned node i, the voltage sensitivity component of the distributed wind energy storage system access point j is M i,j The logarithm of the determinant is expressed as follows: x i,j =-lg(the(M i,j )) (5) Substitute equation (5) into the matrix M s Get the preprocessed matrix M s 'for: The electrical distance between node i and node j is the Euclidean distance D ij It is expressed as: ③Get cluster division results First, set the number of clusters m and calculate D ij , nodes with similar electrical distances to node j are divided into the same cluster, so that the number of clusters is equal to the number of distributed wind-storage systems m, until all nodes are clustered and a clustering binary tree is generated; to ensure the strong controllability of the control range of the clustered distributed wind-storage system, if there are two points with a distance greater than a certain threshold, the cluster with the most "similar" electrical distance to this part of nodes is recalculated; finally, m clusters with each distributed wind-storage system as the clustering center are obtained.

5. The power allocation method for a distributed wind farm self-distributed energy storage system for a distribution network according to claim 4 is characterized in that: The cluster division result is used to achieve the optimal power allocation of the high-proportion distributed wind energy storage system; the process of determining the output range of the energy storage system is as follows: ①Identify the out-of-limit nodes According to formula (4), M s Realize clustering of distribution network nodes and initialize the voltage regulation ratio λ of the distributed wind storage system in each cluster j,t =0; energy storage output P s =0; the number of energy storage outputs within the allowable voltage deviation range φ = 0, and then the power flow calculation is performed, the node i with the maximum voltage exceeding the limit in the cluster is selected as the reference object, the control signal is output to the corresponding cluster j, and λ is updated j,t =λ j,t +ω (ω is the update factor), calculate the comprehensive voltage sensitivity μ i,j = det(M i,j ); ②Calculate the energy storage charging and discharging power First, calculate the upper voltage limit U corresponding to the current voltage regulation ratio of cluster j up,j and the lower limit U down,j ; U up,j =U N (1+λ j V m ) (8) U down,j =U N (1-λ j V m ) (9) Among them, V m is the allowable voltage deviation range; U N is the rated voltage of the distribution network; j ∈(0,1]; Then, calculate the current charging and discharging power. i >U up,j When the energy storage is charged to reduce the voltage in the cluster, the charging power is P cj =(U up,j -U i ) / μ i,j ; when U i down,j When the wind power output cannot meet the voltage regulation requirements, the energy storage discharge will raise the voltage in the cluster, and the energy storage discharge power is P dj =(U down,j -U i ) / μ i,j ;​ ③ Determine the energy storage output range Calculate the energy storage output P s =P cj,t or P dj,t , determine whether there is a voltage over-limit after adding the current energy storage output; if there is a voltage over-limit, update λ j,t , and recalculate P s Until the cluster has no voltage exceeding the limit, φ=φ+1 and output Then return to update λ j,t Step reenters the loop and ends at λ j,t >1 ends the loop and confirms P DEj,t To determine the energy storage output range, each energy storage charging and discharging power can meet the distribution network operation requirements.

6. The power allocation method of the distributed wind farm self-distributed energy storage system for the distribution network according to claim 5 is characterized by: The optimal power allocation of the high-proportion distributed wind-storage system is achieved by intelligent optimization algorithm based on the comprehensive voltage sensitivity. Find the optimal output result Get the best output The objective function and constraints are as follows; 1) The comprehensive benefit C of the distributed wind-storage system is maximized max C=C2-C1 (10) ① Daily operation and maintenance cost C1 Among them, C DWG is the unit operation and maintenance cost of the distributed wind farm; C DE P is the operation and maintenance cost of the energy storage unit; wdj,t is the output power of the jth distributed wind farm at time t; P cdj,t is the charging and discharging power of the jth energy storage at time t; Δt is the scheduling time interval; ② Daily grid-connected income C2 Among them, K DWSS,t is the unit grid-connected electricity price of the distributed wind-storage system at time t; wj,t and P ej,t are the grid-connected output values ​​of the jth distributed wind farm and energy storage system at time t respectively; 2) The sum of the distribution network voltage deviation rate ΔU% is minimum Among them, U i,t is the voltage amplitude of node i at time t; The constraints of the above objective function include distribution network flow constraints, node voltage constraints, energy storage charging and discharging power constraints, energy storage charge state constraints and distributed wind storage system power balance constraints.

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