Optical storage clustering control method for adaptive voltage regulation and power balance

Abstract

The invention provides an optical storage clustering control method for adaptive voltage regulation and power balance, and relates to the field of distributed optical storage optimization operation in a power distribution network. According to the invention, a comprehensive division index based on voltage regulation and regional power balance is provided, objective functions of voltage offset and power balance degree are respectively established under different voltage instructions to carry out optimization control, and the problems of power reverse transmission, great light abandoning and the like caused by a long-line light-load operation state of the medium and low voltage power distribution network are solved. In a high-permeability distributed photovoltaic power distribution network, the coordination calculation difficulty is linearly increased due to excessive controllers, a cluster operation mode not only reduces power flow among regions, but also can reduce communication calculation burden existing in a traditional control mode, an operation control means is reduced to a cluster range as much as possible, and the reliability of the distributed photovoltaic power distribution network is improved. The complexity of control is reduced, the running speed and flexibility of the power distribution network are improved, and distribution line optical storage cluster division and voltage flexible adjustment containing high-proportion distributed photovoltaic are realized.

Description

Technical Field

[0001] The present invention relates to the field of distributed photovoltaic storage optimization operation in a distribution network, and in particular to a photovoltaic storage clustering control method for adaptive voltage regulation and power balance. Background Art

[0002] With the large-scale access of distributed photovoltaic power generation to the distribution network, it has greater instability and uncertainty compared to traditional power generation methods. The power fluctuations caused by factors such as weather, time, and season have a great impact on the dynamic stability of the power system, and it is difficult to ensure that it will not cause large-scale disturbances or even failures. At present, the main modes of distribution network operation control are centralized control, distributed control, and decentralized control. Too many controllers lead to a sharp increase in the difficulty of coordination calculation. The cluster operation mode not only reduces the power flow between regions, but also reduces the communication calculation burden in the traditional control mode, and reduces the means of operation control to the cluster as much as possible, reducing the complexity of control.

[0003] One of the main purposes of cluster division is to achieve better voltage control. Therefore, based on the coupling analysis of each node voltage to the active power and reactive power of all other nodes, a voltage regulation capability index is established. Considering the balance within the cluster area and minimizing the power exchange between clusters, it can also have a great effect on voltage stability, reduce the dispatch cost of the main grid power, and achieve local power balance. Therefore, from the perspective of operation and dispatching means, a regional power balance capability index is established, and the above two indicators are used as comprehensive division indicators as the basis for cluster division number.

[0004] The cluster division of distributed photovoltaic storage will involve the selection of weights for comprehensive division indicators. Currently, in most cases, the setting of weight coefficients is unfocused and balanced, but this setting of weights lacks consideration of application scenarios. Summary of the invention

[0005] Purpose of the invention: To propose a photovoltaic energy storage clustering control method with adaptive voltage regulation and power balance to solve the above problems existing in the prior art.

[0006] The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance proposed by the present invention has the following steps:

[0007] For the target distributed photovoltaic storage cluster, establish a comprehensive classification index based on voltage regulation capability and regional power balance;

[0008] Based on the comprehensive partitioning index, an adaptive dynamic partitioning method is established with the voltage identifier as the partitioning instruction;

[0009] Establish the intra-cluster objective function under different voltage labels;

[0010] Based on the intra-cluster objective function under the different voltage labels, considering the branch power flow constraints, operation safety constraints, photovoltaic power constraints, energy storage power constraints, and inter-cluster boundary variable constraints, an optimization control model based on photovoltaic storage clustering is established;

[0011] The optimization control model is used to achieve the adaptive voltage regulation and power balance goals in the photovoltaic energy storage clustering control process.

[0012] In a further embodiment, the purpose of cluster division is to implement different voltage control strategies in different clusters. The node voltage coupling degree within the cluster is high, and the power scheduling of nodes outside the cluster has a low impact on the voltage level within the cluster. Based on the Leuven modularity function, its internal performance weight is replaced by the voltage coupling level between each node, which can not only ensure the stability of the cluster structure but also improve the voltage adjustable capability within the cluster.

[0013] The Leuven modularity function expression is as follows:

[0014]

[0015] ΔU=(N-HJ -1 L) -1 ΔP+(L-JH -1 N) -1 ΔQ

[0016] In the formula, A ij =ΔU represents the electrical coupling level weight of the edge between node i and node j; k i =∑ j A ij is the sum of the weights of all edges connected to node i; m = ∑ i ∑ j A ij It represents the sum of the weights of all nodes in the network. If nodes i and j are assigned to the same cluster, then δ(i,j) = 1, otherwise δ(i,j) = 0. H, N, J, L are the coefficients of each block matrix of the Newton-Rafael method power flow calculation correction equation. Δδ is the voltage phasor correction angle of each node. ΔU is the voltage effective value correction value of each node.

[0017] In a further embodiment, when a large number of distributed energy and energy storage are connected, if each equivalent node is equipped with a controller, the difficulty of coordination calculation will increase sharply. The cluster operation mode can reduce the communication calculation burden existing in the traditional control mode, reduce the means of operation control to the cluster as much as possible, reduce the power flow between regions, and achieve local regional power balance. Therefore, the regional power balance capacity is considered as another division indicator. Analyze the section at t = 12 in a day. Based on historical operation data, the load power is set to be relatively stable and non-adjustable, and the photovoltaic power is adjustable, and the maximum and minimum output power are taken.

[0018] The regional power balance is defined as follows:

[0019]

[0020] Where P t max,i represents the maximum excess power value of the ith cluster at this time; P t min,i represents the power shortage of the ith cluster at this time; It represents the minimum sum of the power that can be generated by all photovoltaics in the i-th cluster at time t; It represents the sum of the maximum power that can be generated by all photovoltaics in the group; is the sum of active loads of all nodes in the i-th cluster; represents the sum of the rated charging power of each energy storage in the i-th cluster; represents the sum of the rated discharge power of each energy storage in the i-th cluster; is the power balancing capability of the ith cluster; ε0 represents the regional power balancing capability of the system under the number of divided clusters.

[0021] The comprehensive classification indicators are:

[0022] φ=w1ρ0+w2ε0

[0023] Wherein, w1 and w2 are the weight values ​​of the voltage regulation index and the regional power balance index respectively, and w1+w2=1.

[0024] In a further embodiment, the process of establishing an adaptive dynamic partitioning method using a voltage identifier as a partitioning instruction is as follows:

[0025] Since the comprehensive performance index has its own characteristics, different weights can be assigned to each index according to the specific application scenario when clustering. The present invention focuses on the safety and stability of the distribution network operation, and establishes a dynamic division method based on whether the voltage per unit value exceeds the limit as the division instruction. When the voltage per unit value exceeds plus or minus 0.05, increase the value of w1, that is, the weight of the voltage regulation index; conversely, when the voltage per unit value fluctuates within plus or minus 0.05, increase the value of w2, that is, the weight of the regional power balance index.

[0026]

[0027] In the formula, w1 and w2 are the weight values ​​of the voltage regulation index and the regional power balance index respectively, and w1+w2=1. * max Indicates the maximum voltage in each node, U * min Indicates the minimum voltage among the nodes.

[0028] In a further embodiment, a high proportion of distributed photovoltaic access may cause problems such as power backflow in the distribution network. Power backflow will not only dramatically increase line losses, but in severe cases, there will be voltage over-limit problems. In order to improve the distributed photovoltaic absorption capacity within the cluster, the power balance problem within the cluster should be fully considered to reduce the power transmission from the main grid to the cluster and the power transmission between clusters. The power balance index is to measure the degree of supply and demand balance within each divided area and the power exchange demand between areas, so as to improve the overall stability and self-regulation ability of the cluster and achieve autonomous power balance within the group. If the voltage exceeds the limit, such problems need to be solved first. To this end, it is necessary to establish the objective functions of voltage offset and power balance under different voltage identifiers to regulate the output values ​​of photovoltaics and energy storage, so as to achieve voltage regulation and power balance within the group.

[0029] When the voltage per unit value exceeds plus or minus 0.05, the objective function with the minimum voltage offset is established, and its expression is:

[0030]

[0031] Where N is the total number of nodes in the cluster; U i is the voltage amplitude of the ith node; U r is the system rated voltage;

[0032] When the voltage per unit value fluctuates within plus or minus 0.05, the objective function of optimizing the power balance within the cluster is established, and its expression is:

[0033]

[0034] Where P clu (t) Ck is the power value flowing into the Ckth cluster at time t; P load (t) i is the net load active power of each node in cluster Ck; T is the duration; C is the number of clusters.

[0035] In a further embodiment, the branch flow constraints are as follows:

[0036] For the branch ij in the cluster, it is expressed as:

[0037]

[0038] For node j in the cluster, it is expressed as:

[0039]

[0040] In the formula, is the square of the line current on branches i and j; P ij and Qij are the active power and reactive power flowing from branch i to branch j respectively; V i is the voltage amplitude of node i in the group; r ij and x ij are the line resistance and reactance between nodes i and j, respectively; k∈v(j) means that node k is the end node of the branch with j as the head end; i∈u(j) means that node i is the head end node of the branch with j as the end end; P j,PV , P j,EsS and P j,d are the photovoltaic active power, energy storage active power and load active power at node j respectively; Q j,PV , Q j,ESS and Q j,d They are the photovoltaic reactive power, energy storage reactive power and load reactive power at node j respectively.

[0041] In a further embodiment, the operational safety constraints are as follows:

[0042]

[0043] In the formula, and are the upper and lower limits of the voltage amplitude of the node respectively; It is the safe current for overload operation of branches i and j.

[0044] In a further embodiment, the photovoltaic power constraint is as follows:

[0045]

[0046] Where P ref is the maximum active power that can be controlled by photovoltaics; S Pvj is the rated apparent power of the PV;

[0047] The energy storage power constraints are as follows:

[0048]

[0049] When P j,ess (t)>0, SOC j (t+1) = SOC j (t)-P j,ess (t)t·η dis ;

[0050] When P j,ess (t)≤0, SOC j (t+1) = SOC j (t)+P j,ess (t)t·η ch .

[0051] Where P essmax is the maximum active power of the energy storage system; SOC max , SOC min is the maximum and minimum value of the energy storage charge capacity; t is the charge and discharge time; η ch , η dis SOC is the charging and discharging efficiency of the energy storage system; j Expressed as the capacity of energy storage.

[0052] In a further embodiment, the inter-cluster boundary variable constraints are as follows:

[0053] On the basis of the original objective function, the equality constraint of the line power between clusters is added to constrain the power exchange value between clusters. The objective function with the inter-cluster constraint is expressed as:

[0054]

[0055] Where a and b represent the iterative values ​​of active and reactive power flowing from the previous cluster to the next cluster respectively; P fg and Q fg They represent the active and reactive power flowing from the previous cluster to the next cluster respectively; ρ is the penalty coefficient, which is used to ensure the convergence of the boundary data; and Lagrange multipliers for the iteration of active and reactive power from the previous cluster to the next cluster;

[0056] Inter-cluster variables are updated as follows:

[0057]

[0058] In the formula, k is the number of iterations; and The active and reactive power flowing out of the upstream cluster respectively; P down and Q down The active and reactive power flowing into the downstream cluster respectively.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The method of the present invention is aimed at the problem of division and optimization control of photovoltaic and energy storage clusters in medium and low voltage distribution networks. It establishes a comprehensive division index based on voltage regulation capability and regional power balance, adaptively adjusts the weights of each index with voltage identification as the division instruction, and establishes the objective functions of voltage offset and power balance respectively under different voltage identifications to regulate the output values ​​of photovoltaic and energy storage in the cluster. The present invention solves the problem of voltage over-limit and large-scale abandonment of light in medium and low voltage distribution networks containing a high proportion of distributed photovoltaics while realizing coordinated control of photovoltaic and energy storage clusters. It realizes dynamic division and optimization control based on different application scenarios, and realizes the division of photovoltaic and energy storage clusters and optimal power control in medium and low voltage distribution networks containing a high proportion of distributed photovoltaics. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a photovoltaic energy storage clustering equivalent control method for adaptive voltage regulation and power balance of the present invention.

[0062] Figure 2 This is a 33-node distribution line topology structure in an embodiment of the present invention.

[0063] Figure 3 It is a 24-hour photovoltaic and load data diagram of each node in the embodiment of the present invention.

[0064] Figure 4 It is a voltage regulation capability index curve under different cluster division numbers obtained by solving in the embodiment of the present invention.

[0065] Figure 5 It is a regional power balancing capability index curve under different numbers of cluster divisions obtained by solving in an embodiment of the present invention.

[0066] Figure 6 It is the division result under different division weights set based on the voltage identification instruction in the embodiment of the present invention.

[0067] Figure 7 and Figure 8 It is a specific division result in the embodiment of the present invention.

[0068] Fig. 9 It is the active and reactive power outputted by photovoltaic and energy storage when the voltage offset is used as the objective function in the example of the present invention.

[0069] Fig.10 It is the active and reactive power outputted by photovoltaic and energy storage when the power balance is used as the objective function in the example of the present invention.

[0070] Fig.11 It is the amplitude of the voltage of each node before and after the optimization control in the example of the present invention. DETAILED DESCRIPTION

[0071] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0072] The applicant's research found that the cluster division of distributed photovoltaic storage will involve the selection of weights for comprehensive division indicators. Currently, in most cases, the setting of weight coefficients is unfocused and balanced, but this setting of weights lacks consideration of application scenarios.

[0073] In order to obtain the optimal weight of the objective function more objectively, the embodiment of the present invention aims to select by using an adaptive dynamic partitioning method with voltage identification as the partitioning instruction. When the voltage per unit value exceeds plus or minus 0.05, the value of w1, that is, the weight of the voltage regulation index, is increased, and the nodes with high voltage coupling are distributed in a cluster. The main grid power dispatching and local photovoltaic inverter control should participate in the regulation. The primary task is to reduce the voltage of the distribution network to ensure the safety of the system; on the contrary, when the voltage per unit value is within plus or minus 0.05 of the fluctuation, the value of w2, that is, the regional power balance index, can be increased. When the voltage fluctuation value is not large, the regional power balance autonomy is mainly considered, the power dispatching of the main grid is reduced, and the access of photovoltaic inverters and energy storage is used locally to deal with small voltage and power fluctuations.

[0074] See also Figure 1 The embodiment of the present invention provides a specific implementation process of an equivalent control method for photovoltaic storage clustering with adaptive voltage regulation and power balance:

[0075] S1. Establish a comprehensive classification index based on voltage regulation capability and regional power balance.

[0076] See also Figure 2 In the 33-node distribution line topology, nodes 5, 7, 8, 10, 15, 18, 20, 23, 25, 29, and 31 are connected to the photovoltaic system, and nodes 13 and 26 are connected to the energy storage system. A 24-hour period is selected for analysis. First, the Jacobian matrix is ​​calculated using the output data at t=12 and the system parameters, and the voltage regulation capability index is obtained according to the voltage coupling level between the nodes. The load power is set to be relatively stable and non-adjustable as a whole, and the photovoltaic power is adjustable. The maximum and minimum output power values ​​are taken to obtain the regional power balancing capacity under different cluster division numbers.

[0077] The Leuven modularity function is defined as follows:

[0078]

[0079] ΔU=(N-HJ -1 L)-1 ΔP+(L-JH -1 N) -1 ΔQ(3)

[0080] Where: A ij =ΔU represents the weight of the edge between node i and node j, which can be given meaning based on the decision maker's willingness to divide. In the power network, there are mainly: reactance weight, flow weight, etc., which are defined as electrical coupling level weight in this invention. k i =∑ j A ij is the sum of the weights of all edges connected to node i; m = ∑ i ∑ j A ij It represents the sum of the weights of all nodes in the network. If nodes i and j are assigned to the same cluster, then δ(i,j) = 1, otherwise δ(i,j) = 0. H, N, J, L are the coefficients of each block matrix of the Newton-Rafael method power flow calculation correction equation. Δδ is the voltage phasor correction angle of each node. ΔU is the voltage effective value correction value of each node.

[0081] The regional power balancing capability is defined as follows

[0082]

[0083] Where P t max,i represents the maximum excess power value of the ith cluster at this time; P t min,i represents the power shortage of the ith cluster at this time; It represents the minimum sum of the power that can be generated by all photovoltaics in the i-th cluster at time t; It represents the sum of the maximum power that can be generated by all photovoltaics in the group; is the sum of active loads of all nodes in the i-th cluster; represents the sum of the rated charging power of each energy storage in the i-th cluster; represents the sum of the rated discharge power of each energy storage in the i-th cluster; is the power balancing capability of the ith cluster; ε0 represents the regional power balancing capability of the system under the number of divided clusters.

[0084] The comprehensive classification indicators are:

[0085] φ=w1ρ0+w2ε0(8)

[0086] Wherein, w1 and w2 are the weight values ​​of the voltage regulation index and the regional power balance index respectively, and w1+w2=1.

[0087] S2. Establish an adaptive dynamic partitioning method using voltage identification as partitioning instruction.

[0088] Since the comprehensive performance index has its own characteristics, different weights can be assigned to each index according to the specific application scenario when clustering. The present invention focuses on the safety and stability of the distribution network operation, and establishes a dynamic division method based on whether the voltage per unit value exceeds the limit as the division instruction. When the voltage per unit value exceeds plus or minus 0.05, increase the value of w1, that is, the weight of the voltage regulation index; conversely, when the voltage per unit value fluctuates within plus or minus 0.05, increase the value of w2, that is, the regional power balance index.

[0089]

[0090] In the formula, w1 and w2 are the weight values ​​of the voltage regulation index and the regional power balance index respectively, and w1+w2=1. * max Indicates the maximum voltage in each node, U * min Indicates the minimum voltage among the nodes.

[0091] See also Figure 4 , when the number of cluster divisions is 3, the cluster voltage regulation capability reaches the maximum value; see Figure 5 ,When the number of cluster divisions is 1, 2, and 5, the cluster regional power balancing capability is equal and reaches the maximum value, but when the cluster is not divided, the nodes tend to be concentrated, the number of clusters is small and the scale is large, the cluster structure performance is low, which does not meet the requirements of cluster division purpose, and only when the number of clusters is 2 and 5 are used as alternative options.

[0092] See also Figure 6 For different weight combinations, increasing the weight of the indicator ω1 increases the cluster voltage regulation capability and decreases the power balance capability within the cluster; increasing the weight of w2 is the opposite, but the magnitude of the two changes with the weight is different. When the voltage per unit value exceeds plus or minus 0.05, increase the value of w1, that is, the weight of the voltage regulation indicator; conversely, when the voltage per unit value fluctuates within plus or minus 0.05, increase the value of w2, that is, the regional power balance indicator. Under different weight combinations, the best comprehensive performance is obtained when the number of cluster divisions is 3 or 5. Figure 7 and Figure 8 The following is the system topology diagram for this division result.

[0093] S3. Establish the intra-cluster objective function under different voltage labels.

[0094] The present invention considers voltage offset and power balance based on whether the voltage exceeds the limit, and establishes objective functions of minimum voltage offset and optimal power balance under different voltage marks to regulate the output values ​​of photovoltaic and energy storage, thereby realizing rapid voltage regulation and power balance within the cluster.

[0095] When the voltage per unit value exceeds plus or minus 0.05, in order to achieve fast voltage control, an objective function with the minimum voltage offset is established, and its expression is:

[0096]

[0097] Where: N is the total number of nodes in the cluster; U i is the voltage amplitude of the ith node; U r is the rated voltage of the system.

[0098] When the voltage per unit value fluctuates within plus or minus 0.05, in order to achieve power balance within the cluster, an objective function with the optimal power balance within the cluster is established, and its expression is:

[0099]

[0100] Where P clu (t) Ck is the power value flowing into the Ckth cluster at time t; P load (t) i is the net load active power of each node in cluster Ck; T is the duration; C is the number of clusters.

[0101] S4. Establish an optimization control model based on photovoltaic clustering.

[0102] The branch power flow constraints are as follows:

[0103] For the branch ij in the cluster, it is expressed as:

[0104]

[0105] For node j in the cluster, it is expressed as:

[0106]

[0107] In the formula, is the square of the line current on branches i and j; P ij and Q ij are the active power and reactive power flowing from branch i to branch j respectively; V i is the voltage amplitude of node i in the group; r ij and x ij are the line resistance and reactance between nodes i and j, respectively; k∈v(j) means that node k is the end node of the branch with j as the head end; i∈u(j) means that node i is the head end node of the branch with j as the end end; P j,PV , P j,ESS and P j,d are the photovoltaic active power, energy storage active power and load active power at node j respectively; Q j,PV , Qj,ESS and Q j,d They are the photovoltaic reactive power, energy storage reactive power and load reactive power at node j respectively.

[0108] The operational safety constraints are as follows:

[0109]

[0110] In the formula, and are the upper and lower limits of the voltage amplitude of the node respectively; It is the safe current for overload operation of branches i and j.

[0111] The power constraints of photovoltaic and energy storage are as follows:

[0112]

[0113] Where P ref is the maximum active power that can be controlled by photovoltaics; S PVj is the rated apparent power of the PV.

[0114]

[0115] Where P essmax is the maximum active power of the energy storage system; SOC max , SOC min is the maximum and minimum value of the energy storage charge capacity; t is the charge and discharge time; η ch , η dis SOC is the charging and discharging efficiency of the energy storage system; j Expressed as the capacity of energy storage.

[0116] The inter-cluster boundary variable constraints are as follows:

[0117] On the basis of the original objective function, the equality constraint of the line power between clusters is added to constrain the power exchange value between clusters. The objective function with the inter-cluster constraint is expressed as:

[0118]

[0119] Where a and b represent the iterative values ​​of active and reactive power flowing from the previous cluster to the next cluster respectively; P fg and Q fg They represent the active and reactive power flowing from the previous cluster to the next cluster respectively; ρ is the penalty coefficient, which is used to ensure the convergence of the boundary data; and Iterative Lagrange multipliers for active and reactive power flowing from the previous cluster to the next cluster.

[0120] Inter-cluster variables are updated as follows:

[0121]

[0122] In the formula, k is the number of iterations; and The active and reactive power flowing out of the upstream cluster respectively; P down and Q down The active and reactive power flowing into the downstream cluster respectively.

[0123] by Figure 7 The system structure after cluster division is the control object, and the data at 12 moments are selected for analysis, when the line voltage is most seriously over-limited. Fig. 9 , the line voltage exceeds the limit, with the minimum voltage offset as the optimization goal, the output power of photovoltaic and energy storage is adjusted, and the voltage is quickly controlled to reduce the voltage offset from the original 1.367 to the current 1.081. When the voltage is stable, the power balance of the cluster needs to be considered, see Fig.10 When the power balance is used as the optimization target, the output power of photovoltaic and energy storage is adjusted. At this time, the power balance is improved from the original 0.547 to 0.626. Fig.11 To optimize the amplitude of the voltage at each node before and after control, the voltage returns to the safe operating range after optimization.

[0124] The entire process of the photovoltaic clustering control method for adaptive voltage regulation and power balance disclosed in the above embodiment can be embedded in an electronic device for operation. The electronic device includes a processor, a memory, a communication interface and a communication bus. The processor, the memory and the communication interface communicate with each other through the communication bus. The memory is used to store at least one executable instruction, which enables the processor to execute all the process steps of the plate heat exchanger temperature prediction method disclosed in the above embodiment, which will not be repeated here.

[0125] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0126] In addition, the photovoltaic storage clustering control method for adaptive voltage regulation and power balancing disclosed in the above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0127] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A photovoltaic energy storage clustering control method with adaptive voltage regulation and power balance, characterized in that: The steps include: For the target distributed photovoltaic storage cluster, establish a comprehensive classification index based on voltage regulation capability and regional power balance; Based on the comprehensive partitioning index, an adaptive dynamic partitioning method is established with the voltage identifier as the partitioning instruction; Establish the intra-cluster objective function under different voltage labels; Based on the intra-cluster objective function under the different voltage labels, considering the branch power flow constraints, operation safety constraints, photovoltaic power constraints, energy storage power constraints, and inter-cluster boundary variable constraints, an optimization control model based on photovoltaic storage clustering is established; The optimization control model is used to achieve the adaptive voltage regulation and power balance goals in the photovoltaic energy storage clustering control process.

2. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 1 is characterized in that: The comprehensive division index based on regional power balance is established for the target distributed photovoltaic storage cluster, specifically including: The Leuven modularity function is used to replace the performance weight within the target distributed photovoltaic storage cluster with the voltage coupling level between nodes. The Leuven modularity function expression is as follows: ΔU=(N-HJ -1 L) -1 ΔP+(L-JH -1 N) -1 ΔQ In the formula, A ij =ΔU represents the electrical coupling level weight of the edge between node i and node j; k i =∑ j A ij is the sum of the weights of all edges connected to node i; m = ∑ i ∑ j A ij It represents the sum of the weights of all nodes in the network. If nodes i and j are assigned to the same cluster, δ(i, j) = 1, otherwise δ(i, j) = 0. H, N, J, L are the coefficients of each block matrix of the Newton-Rafael method power flow calculation correction equation. Δδ is the voltage phasor correction angle of each node. ΔU is the voltage effective value correction value of each node.

3. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 2 is characterized in that: The regional power balance is defined as follows: Where P t max,i represents the maximum excess power value of the ith cluster at this time; P t min,i represents the power shortage of the ith cluster at this time; It represents the minimum sum of the power that can be generated by all photovoltaics in the i-th cluster at time t; It represents the sum of the maximum power that can be generated by all photovoltaics in the group; is the sum of active loads of all nodes in the i-th cluster; represents the sum of the rated charging power of each energy storage in the i-th cluster; represents the sum of the rated discharge power of each energy storage in the i-th cluster; is the power balancing capability of the i-th cluster; ε0 represents the regional power balancing capability of the system under the number of partitioned clusters.

4. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 3 is characterized in that: Establish a comprehensive division index φ based on regional power balance: φ=w1ρ0+W2ε0 Wherein, w1 and w2 are the weight values ​​of the voltage regulation index and the regional power balance index respectively, and w1+w2=1.

5. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 4 is characterized in that: The method for establishing an adaptive dynamic partitioning method using a voltage identifier as a partitioning instruction specifically includes: Establish a dynamic division method based on whether the voltage per unit value exceeds the limit as the division instruction: When the voltage per unit value exceeds plus or minus 0.05, increase the value of w1. When the voltage per unit value fluctuates within plus or minus 0.05, increase the value of w2.

6. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 5 is characterized in that: The establishing of the intra-cluster objective function under different voltage identifiers specifically includes: When the voltage per unit value exceeds plus or minus 0.05, the objective function with the minimum voltage offset is established, and its expression is: Where N is the total number of nodes in the cluster; U i is the voltage amplitude of the ith node; U r is the system rated voltage; When the voltage per unit value fluctuates within plus or minus 0.05, the objective function of optimizing the power balance within the cluster is established, and its expression is: Where P clu (t) Ck is the power value flowing into the Ck-th cluster at time t; is the net load active power of each node in cluster Ck; T is the duration; C is the number of clusters.

7. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 6, characterized in that: The branch power flow constraints are as follows: For the branch ij in the cluster, it is expressed as: For node j in the cluster, it is expressed as: In the formula, is the square of the line current on branches i and j; P ij and Q ij are the active power and reactive power flowing from branch i to branch j respectively; V i is the voltage amplitude of node i in the group; r ij and x ij are the line resistance and reactance between nodes i and j, respectively; k∈v(j) means that node k is the end node on the branch with node j as the head end; i∈u(j) means that node i is the head node of the branch with node j as the end; P j，PV , P j，ESS and P j，d are the photovoltaic active power, energy storage active power and load active power at node j respectively; Q j，PV , Q j，ESS and Q j，d They are the photovoltaic reactive power, energy storage reactive power and load reactive power at node j respectively.

8. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 6, characterized in that: The operational safety constraints are as follows: In the formula, and are the upper and lower limits of the voltage amplitude of the node respectively; It is the safe current for overload operation of branches i and j.

9. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 6, characterized in that: The photovoltaic power constraints are as follows: Where P ref is the maximum active power that can be controlled by photovoltaics; S PVj is the rated apparent power of the PV; The energy storage power constraints are as follows: When P j，ess (t) > 0, SOC j (t + 1) = SOC j (t) - P j，ess (t)t·η dis ; When P j，ess (t) ≤ 0, SOC j (t + 1) = SOC j (t) + P j，ess (t) t·η ch ; Where P essmax is the maximum active power of the energy storage system; SOC max , SOC min are the maximum and minimum values ​​of energy storage charge capacity; t is the charge and discharge time; η ch , η dis SOC is the charging and discharging efficiency of the energy storage system; j Expressed as the capacity of energy storage.

10. The photovoltaic energy storage clustering control method for adaptive voltage regulation and power balance according to claim 6, characterized in that: The inter-cluster boundary variable constraints are as follows: Where a and b represent the iterative values ​​of active and reactive power flowing from the previous cluster to the next cluster respectively; P fg and Q fg They represent the active and reactive power flowing from the previous cluster to the next cluster respectively; ρ is the penalty coefficient, which is used to ensure the convergence of the boundary data; and Lagrange multipliers for the iteration of active and reactive power from the previous cluster to the next cluster; Inter-cluster variables are updated as follows: In the formula, k is the number of iterations; and The active and reactive power flowing out of the upstream cluster respectively; P down and Q down The active and reactive power flowing into the downstream cluster respectively.

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