An adaptive voltage regulation and power balancing optical storage cluster control method

Abstract

The application provides a kind of adaptive voltage regulation and power balance photovoltaic storage cluster control method, it is related to the field of distributed photovoltaic storage optimization operation in distribution network.The application proposes a comprehensive division index based on voltage regulation and regional power balance, establishes the objective function of voltage deviation and power balance degree under different voltage instructions for optimization control, solves the problems of power reverse sending, large-scale light abandonment and other problems caused by long line light load operation state of medium and low voltage distribution network.In high penetration rate distributed photovoltaic distribution network, excessive controllers lead to a straight-line increase in coordination calculation difficulty, the operation mode of cluster not only reduces the power flow between regions, but also reduces the communication and calculation burden existing in the traditional control mode, reduces the control means as much as possible within the cluster range, reduces the complexity of control, improves the operation speed and flexibility of distribution network, realizes the photovoltaic storage cluster division and voltage flexible regulation of distribution line containing high proportion of distributed photovoltaic.

Description

Technical Field

[0001] This invention relates to the field of optimized operation of distributed photovoltaic and energy storage in power distribution networks, and particularly to a photovoltaic and energy storage clustering control method with adaptive voltage regulation and power balance. Background Technology

[0002] With the large-scale integration of distributed photovoltaic (PV) power into the distribution network, it exhibits greater instability and uncertainty compared to traditional power generation methods. Power fluctuations caused by factors such as weather, time, and season have a significant impact on the dynamic stability of the power system, making it difficult to guarantee that they will not cause large-scale disturbances or even failures. Currently, the main methods of distribution network operation and control include centralized control, distributed control, and decentralized control. Too many controllers lead to a sharp increase in coordination and calculation difficulties. Cluster operation mode not only reduces power flow between different areas but also alleviates the communication and calculation burden existing in traditional control modes, minimizing the means of operation and control within the cluster scope and reducing the complexity of control.

[0003] One of the main purposes of cluster partitioning is to achieve better voltage control. Therefore, based on the coupling analysis of the voltage of each node with the active and reactive power of all other nodes, a voltage regulation capability index is established. Considering that balancing within the cluster area and minimizing the power exchange between clusters can also greatly contribute to voltage stability and reduce the power scheduling cost of the main grid, thus achieving local power balance, a regional power balance capability index is established from the perspective of operation and scheduling methods. The above two indicators are used as a comprehensive partitioning index as the basis for the number of clusters to be partitioned.

[0004] The cluster partitioning of distributed optical storage involves the selection of comprehensive partitioning index weights. Currently, in most cases, the weight coefficients are set without emphasis and are balanced, but such weight settings lack consideration for application scenarios. Summary of the Invention

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

[0006] The adaptive voltage regulation and power balance photovoltaic-storage clustering control method proposed in this invention comprises the following steps:

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

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

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

[0010] Based on the intra-cluster objective function under the different voltage indicators, and considering branch power flow constraints, operational 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 optimized control model is used to achieve adaptive voltage regulation and power balance in the photovoltaic-storage cluster control process.

[0012] In a further embodiment, the purpose of cluster partitioning is to implement different voltage control strategies within different clusters. The voltage coupling of nodes within a cluster is high, while 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, the internal performance weights are replaced with the voltage coupling levels between nodes, which not only ensures the stability of the cluster structure but also improves the voltage adjustability 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 The sum of the weights of all edges connected to node i; m = ∑ i ∑ j A ij δ(i,j) represents the sum of the weights of all nodes in the network. If node i and node 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 in the Newton-Layer power flow calculation correction equation. Δδ is the correction angle of the voltage phasor of each node. ΔU is the correction amount of the effective value of the voltage of each node.

[0017] In a further embodiment, when a large number of distributed energy sources and energy storage are connected, equipping each equivalent node with a controller would lead to a sharp increase in the difficulty of coordination calculations. The cluster operation mode can alleviate the communication and computational burden existing in the traditional control mode, and minimize the means of operation and control within the cluster as much as possible, reducing power flow between different areas and achieving local regional power balance. Therefore, regional power balance capability is considered as another dividing indicator. Analyzing the cross-section at t=12 in a day, based on historical operating data, the overall load power is set to be relatively stable and unadjustable, while photovoltaic power is adjustable, and the maximum and minimum output power values ​​are taken.

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

[0019]

[0020] In the formula, P t max,i P represents the maximum excess power of the i-th cluster at this time; t min,i This indicates the power deficit of the i-th cluster at this time; It represents the sum of the minimum power that all photovoltaics in the i-th cluster can generate at time t; This represents the sum of the maximum power output of all photovoltaic cells within the group; This represents the sum of the active power loads of all nodes within the i-th cluster. This represents the sum of the rated charging power of all energy storage devices within the i-th cluster; This represents the sum of the rated discharge power of all energy storage devices within the i-th cluster; εi represents the power balancing capability of the i-th cluster; ε0 represents the regional power balancing capability of the system under this number of clusters.

[0021] The comprehensive classification indicators are as follows:

[0022] φ=w1ρ0+w2ε0

[0023] 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.

[0024] In a further embodiment, the process of establishing an adaptive dynamic partitioning method with voltage identifiers as partitioning instructions is as follows:

[0025] Because comprehensive performance indicators have their own characteristics, different weights can be assigned to each indicator during cluster partitioning based on specific application scenarios. This invention focuses on the safety and stability of distribution network operation, establishing a dynamic partitioning method based on whether the per-unit voltage value exceeds the limit. When the per-unit voltage value exceeds ±0.05, the value of w1 is increased, which is the weight of the voltage regulation indicator; conversely, when the per-unit voltage value fluctuates within ±0.05, the value of w2 is increased, which is the weight of the regional power balance indicator.

[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 U represents the maximum voltage at each node. * min This represents the minimum voltage at each node.

[0028] In a further embodiment, a high proportion of distributed photovoltaic (PV) grid integration may cause problems such as power backfeeding to the distribution network. Power backfeeding not only drastically increases line losses but can also lead to voltage overruns in severe cases. To improve the distributed PV absorption capacity within a cluster, the power balance within the cluster should be fully considered, reducing power transmission from the main grid to the cluster and power transmission between clusters. The power balance index measures the degree of supply and demand balance within each defined area and the power exchange demand between areas, thereby improving the overall stability and self-regulation capability of the cluster and achieving autonomous power balance within the cluster. If voltage overruns occur, such problems should be addressed first. To this end, objective functions for voltage offset and power balance need to be established under different voltage ratings to regulate the output values ​​of PV and energy storage, achieving voltage regulation and power balance within the cluster.

[0029] When the per-unit voltage value exceeds ±0.05, an objective function is established to minimize the voltage offset, and its expression is:

[0030]

[0031] In the formula, N is the total number of nodes in the cluster; U i U represents the voltage amplitude at the i-th node; r This is the system's rated voltage;

[0032] When the voltage per unit value fluctuates within ±0.05, an objective function is established to optimize the power balance within the cluster, and its expression is:

[0033]

[0034] In the formula, P clu (t) Ck P represents the inflow power to the Ck-th cluster at time t. load (t) i T represents the net active power of each node in cluster Ck; T is the duration; and C is the number of clusters.

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

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

[0037]

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

[0039]

[0040] In the formula, P is the square of the line current on branches i and j; ij and Qij V represents the active power and reactive power flowing from branch i to branch j, respectively; i r is the voltage amplitude at node i within the group; ij and x ij Let $v$ be the line resistance and reactance between nodes $i$ and $j$, respectively; $k \in v(j)$ indicates that node $k$ is the end node on the branch with $j$ as the starting point; $i \in u(j)$ indicates that node $i$ is the beginning node on the branch with $j$ as the ending point; P j,PV P j,EsS and P j,d These represent the active power from photovoltaics, the active power from energy storage, and the active power from the load at node j, respectively; Q j,PV Q j,ESS and Q j,d These represent the photovoltaic reactive power, energy storage reactive power, and load reactive power at node j, respectively.

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

[0042]

[0043] In the formula, and These are the upper and lower limits of the voltage amplitude at that node, respectively. This 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] In the formula, P ref S represents the maximum controllable active power of photovoltaic power. Pvj This is the rated apparent power of the photovoltaic system;

[0047] The energy storage power constraint is 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] In the formula, P essmax The maximum active power of the energy storage system; SOC max SOC min η represents the maximum and minimum energy storage capacity; t represents the charging and discharging time; η represents the maximum and minimum energy storage capacity. ch η dis For the charge and discharge efficiency of energy storage systems; SOC j It is expressed as the energy storage capacity.

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

[0053] Based on the original objective function, an equality constraint on the power exchange between clusters is added to constrain the power exchange value between clusters. The objective function with added inter-cluster constraints is expressed as follows:

[0054]

[0055] In the formula, 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 These represent the active and reactive power flowing from the previous cluster to the next cluster, respectively; ρ is the penalty coefficient used to ensure the convergence of boundary data. and The Lagrange multiplier is used to iterate the active and reactive power flow from the previous cluster to the next cluster.

[0056] Inter-cluster variable updates are as follows:

[0057]

[0058] In the formula, k is the number of iterations; and 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 clusters are separated.

[0059] Compared with the prior art, the beneficial effects of the present invention are:

[0060] This invention addresses the problem of photovoltaic (PV) and energy storage cluster partitioning and optimal control in medium- and low-voltage distribution networks. It establishes a comprehensive partitioning index based on voltage regulation capability and regional power balance, adaptively adjusts the weights of each index using voltage indicators as partitioning commands, and establishes objective functions for voltage offset and power balance under different voltage indicators to regulate the output values ​​of PV and energy storage within the cluster. This invention solves the problems of voltage exceeding limits and significant curtailment that occur in medium- and low-voltage distribution networks containing high-proportion distributed PV while achieving coordinated control of PV and energy storage clusters. Based on different application scenarios, it achieves dynamic partitioning and optimal control, realizing the partitioning and optimal power control of PV and energy storage clusters in medium- and low-voltage distribution networks containing high-proportion distributed PV. Attached Figure Description

[0061] Figure 1 This is a flowchart of an adaptive voltage regulation and power balance optical storage clustering equivalent control method according to the present invention.

[0062] Figure 2 This is the 33-node power distribution line topology in this embodiment of the invention.

[0063] Figure 3 This is a graph showing the photovoltaic and load data of each node over 24 hours in an embodiment of the present invention.

[0064] Figure 4 These are voltage regulation capability index curves obtained from different cluster division numbers in embodiments of the present invention.

[0065] Figure 5 These are the regional power balance capability index curves obtained from different cluster division numbers in the embodiments of the present invention.

[0066] Figure 6 This is the partitioning result under different partitioning weights based on the voltage identification instruction in the embodiments of the present invention.

[0067] Figure 7 and Figure 8 This is the specific division result in the embodiments of the present invention.

[0068] Figure 9 These are the active and reactive power outputs of photovoltaic and energy storage when the voltage offset is the objective function in this invention example.

[0069] Figure 10 These are the active and reactive power outputs of photovoltaic and energy storage when power balance is the objective function in this invention example.

[0070] Figure 11 It refers to the amplitude of the voltage at each node before and after the optimized control in this embodiment of the invention. Detailed Implementation

[0071] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0072] The applicant's research found that the cluster partitioning of distributed optical storage involves the selection of comprehensive partitioning index weights. Currently, in most cases, the weight coefficients are set without emphasis and are balanced, but such weight settings lack consideration for application scenarios.

[0073] To obtain the optimal weights for the objective function more objectively, this embodiment of the invention aims to use an adaptive dynamic partitioning method with voltage indicators as the partitioning instruction. When the per-unit voltage value exceeds ±0.05, the value of w1, i.e., the weight of the voltage regulation index, is increased. Nodes with high voltage coupling are distributed in a cluster, and both grid power dispatch and local photovoltaic inverter control should participate in regulation. The primary task is to reduce the distribution network voltage to ensure system safety. Conversely, when the per-unit voltage value fluctuates within ±0.05, the value of w2, i.e., the regional power balance index, can be increased. When the voltage fluctuation value is small, the main consideration is the autonomous balance of regional power, reducing grid power dispatch, and using local photovoltaic inverters and energy storage to cope with small voltage and power fluctuations.

[0074] Please see Figure 1 The present invention provides a specific implementation process for an equivalent control method for optical 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 Figure 2 In a 33-node distribution line topology, photovoltaic systems are connected to nodes 5, 7, 8, 10, 15, 18, 20, 23, 25, 29, and 31, and energy storage systems are connected to nodes 13 and 26. An analysis is conducted over a 24-hour period. First, the Jacobian matrix is ​​calculated using the output data at t=12 and system parameters. Then, the voltage regulation capability index is obtained based on the voltage coupling level between each node. The load power is set to be relatively stable and unadjustable overall, while the photovoltaic power is adjustable. The maximum and minimum output power values ​​are taken to obtain the regional power balance capability 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] In the formula: A ij =ΔU represents the weight of the edge between node i and node j, which can be assigned meaning based on the decision-maker's intention. In power networks, these mainly include reactance weights and power flow weights; this invention defines them as electrical coupling level weights. k i =∑ j A ij The sum of the weights of all edges connected to node i; m = ∑ i ∑ j A ij δ(i,j) represents the sum of the weights of all nodes in the network. If node i and node 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 in the Newton-Layer power flow calculation correction equation. Δδ is the correction angle of the voltage phasor of each node. ΔU is the correction amount of the effective value of the voltage of each node.

[0081] The regional power balance capability is defined as follows:

[0082]

[0083] In the formula, P t max,i P represents the maximum excess power of the i-th cluster at this time; t min,i This indicates the power deficit of the i-th cluster at this time; It represents the sum of the minimum power that all photovoltaics in the i-th cluster can generate at time t; This represents the sum of the maximum power output of all photovoltaic cells within the group; This represents the sum of the active power loads of all nodes within the i-th cluster. This represents the sum of the rated charging power of all energy storage devices within the i-th cluster; This represents the sum of the rated discharge power of all energy storage devices within the i-th cluster; εi represents the power balancing capability of the i-th cluster; ε0 represents the regional power balancing capability of the system under this number of clusters.

[0084] The comprehensive classification indicators are as follows:

[0085] φ=w1ρ0+w2ε0(8)

[0086] 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.

[0087] S2. Establish an adaptive dynamic partitioning method with voltage identifier as the partitioning instruction.

[0088] Because comprehensive performance indicators have their own characteristics, different weights can be assigned to each indicator during cluster partitioning based on specific application scenarios. This invention focuses on the safety and stability of distribution network operation, establishing a dynamic partitioning method based on whether the voltage per-unit value exceeds the limit. When the voltage per-unit value exceeds ±0.05, the value of w1, i.e., the weight of the voltage regulation indicator, is increased; conversely, when the voltage per-unit value fluctuates within ±0.05, the value of w2, i.e., the regional power balance indicator, can be increased.

[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 U represents the maximum voltage at each node. * min This represents the minimum voltage at each node.

[0091] See Figure 4 When the number of cluster partitions is 3, the cluster voltage regulation capability reaches its maximum value; see reference. Figure 5 When the number of clusters is 1, 2, or 5, the power balancing capability of the cluster area is equal and reaches its maximum value. However, when no clusters are divided, the nodes tend to be concentrated, the number of clusters is small, the scale is large, and the cluster structure performance is low, which does not meet the requirements of the purpose of cluster division. Only when the number of clusters is 2 or 5, it is considered as an alternative.

[0092] See Figure 6 For different weight combinations, increasing the weight of index ω1 increases the cluster voltage regulation capability but decreases the intra-cluster power balance capability; increasing the weight of w2 has the opposite effect. However, the magnitude of the change with the weights differs. When the per-unit voltage value exceeds ±0.05, the value of w1 (i.e., the weight of the voltage regulation index) is increased; conversely, when the per-unit voltage value fluctuates within ±0.05, the value of w2 (i.e., the regional power balance index) is increased. Under different weight combinations, the optimal overall performance is obtained when the number of cluster partitions is 3 and 5. (See reference...) Figure 7 and Figure 8 This is the system topology diagram under this partitioning result.

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

[0094] This invention takes voltage deviation and power balance into account whether the voltage exceeds the limit. Under different voltage ratings, it establishes objective functions to minimize voltage deviation and optimize power balance to regulate the output values ​​of photovoltaic and energy storage, thereby achieving rapid voltage regulation and power balance within the cluster.

[0095] When the per-unit voltage value exceeds ±0.05, in order to achieve fast voltage control, an objective function is established to minimize the voltage offset, and its expression is:

[0096]

[0097] In the formula: N is the total number of nodes in the cluster; U i U represents the voltage amplitude at the i-th node; r This is the system's rated voltage.

[0098] When the per-unit voltage value fluctuates within ±0.05, to achieve power balance within the cluster, an objective function is established to optimize the power balance within the cluster, and its expression is:

[0099]

[0100] In the formula, P clu (t) Ck P represents the inflow power to the Ck-th cluster at time t. load (t) i T represents the net active power of each node in cluster Ck; T is the duration; and C is the number of clusters.

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

[0102] Branch flow constraints are as follows:

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

[0104]

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

[0106]

[0107] In the formula, P is the square of the line current on branches i and j; ij and Q ij V represents the active power and reactive power flowing from branch i to branch j, respectively; i r is the voltage amplitude at node i within the group; ij and x ij Let $v$ be the line resistance and reactance between nodes $i$ and $j$, respectively; $k \in v(j)$ indicates that node $k$ is the end node on the branch with $j$ as the starting point; $i \in u(j)$ indicates that node $i$ is the beginning node on the branch with $j$ as the ending point; P j,PV P j,ESS and P j,d These represent the active power from photovoltaics, the active power from energy storage, and the active power from the load at node j, respectively; Q j,PV Qj,ESS and Q j,d These represent 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 These are the upper and lower limits of the voltage amplitude at that node, respectively. This is the safe current for overload operation of branches i and j.

[0111] The power constraints for photovoltaics and energy storage are as follows:

[0112]

[0113] In the formula, P ref S represents the maximum controllable active power of photovoltaic power. PVj This is the rated apparent power of the photovoltaic system.

[0114]

[0115] In the formula, P essmax The maximum active power of the energy storage system; SOC max SOC min η represents the maximum and minimum energy storage capacity; t represents the charging and discharging time; η represents the maximum and minimum energy storage capacity. ch η dis For the charge and discharge efficiency of energy storage systems; SOC j It is expressed as the energy storage capacity.

[0116] The boundary variable constraints between clusters are as follows:

[0117] Based on the original objective function, an equality constraint on the power exchange between clusters is added to constrain the power exchange value between clusters. The objective function with added inter-cluster constraints is expressed as follows:

[0118]

[0119] In the formula, 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 These represent the active and reactive power flowing from the previous cluster to the next cluster, respectively; ρ is the penalty coefficient used to ensure the convergence of boundary data. and The Lagrange multiplier is used to iterate the active and reactive power flow from the previous cluster to the next cluster.

[0120] Inter-cluster variable updates are as follows:

[0121]

[0122] In the formula, k is the number of iterations; and 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 clusters are separated.

[0123] by Figure 7 The system structure after cluster partitioning is the control object, and data from time 12 is selected for analysis, at which point the line voltage exceedance is most severe. (See reference...) Figure 9 When the line experienced a voltage exceedance, the optimization objective was to minimize the voltage deviation. The output power of the photovoltaic and energy storage systems was adjusted to quickly control the voltage, reducing the voltage deviation from 1.367 to 1.081. Once the voltage stabilized, the power balance of the energy cluster needed to be considered. (See [reference needed]). Figure 10 When power balance is used as the optimization target, the output power of photovoltaics and energy storage is adjusted, and the power balance is improved from 0.547 to 0.626. (See also...) Figure 11 To optimize the voltage amplitude of each node before and after control, the voltage returns to the safe operating range after optimization.

[0124] The entire process of the adaptive voltage regulation and power balance photovoltaic-storage clustering control method disclosed in the above embodiments can be embedded in an electronic device. This electronic device includes a processor, a memory, a communication interface, and a communication bus. The processor, memory, and communication interface communicate with each other via the communication bus. The memory stores at least one executable instruction, which causes the processor to execute all the steps of the plate heat exchanger temperature prediction method disclosed in the above embodiments, which will not be elaborated here.

[0125] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can 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.

[0126] Furthermore, the adaptive voltage regulation and power balance optical storage clustering control method disclosed in the above embodiments can be implemented entirely or partially through software, hardware, firmware, or other arbitrary combinations. When implemented using software, the above embodiments can be implemented entirely or partially 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 processes or functions described in the embodiments of this application are generated wholly or partially. 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0127] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the 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, Includes the following steps: For the target distributed photovoltaic-storage cluster, a comprehensive classification index based on voltage regulation capability and regional power balance is established, specifically including: The Leuven modularity function is used to replace the performance weights within the target distributed photovoltaic-storage cluster with the voltage coupling level between each node. The Leuven modularity function expression is as follows: In the formula, The electrical coupling level weight of the edge between node i and node j; This is the sum of the weights of all edges connected to node i; This represents the sum of the weights of all nodes in the network. If node i and node j are assigned to the same cluster, then... ,otherwise ; Calculate the coefficients of each block matrix in the modified equation for the Newton-Layer power flow method; Correct the angle for the voltage phasors at each node; This is the correction amount for the effective value of the voltage at each node; The regional power balance is defined as follows: In the formula, This represents the maximum excess power of the i-th cluster at this point; This indicates the power deficit of the i-th cluster at this time; It represents the sum of the minimum power that all photovoltaics in the i-th cluster can generate at time t; This represents the sum of the maximum power output of all photovoltaic cells within the group; This represents the sum of the active power loads of all nodes within the i-th cluster. This represents the sum of the rated charging power of all energy storage devices within the i-th cluster; This represents the sum of the rated discharge power of all energy storage devices within the i-th cluster; The power balancing capability of the i-th cluster; This indicates the regional power balance capability of the system under this number of clusters. Based on the comprehensive classification index, an adaptive dynamic classification method with voltage identifier as the classification instruction is established; Establish the intra-cluster objective function under different voltage indicators; Based on the intra-cluster objective function under the different voltage indicators, and considering branch power flow constraints, operational 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 optimized control model is used to achieve adaptive voltage regulation and power balance in the photovoltaic-storage cluster control process.

2. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 1, characterized in that, Establish a comprehensive classification index based on regional power balance. : In the formula, These are the weight values ​​for the voltage regulation index and the regional power balance index, respectively. .

3. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 2, characterized in that, The adaptive dynamic partitioning method based on voltage identifiers as partitioning instructions specifically includes: Establish a dynamic partitioning method based on whether the per-unit voltage value exceeds the limit as the partitioning instruction: When the per-unit voltage value exceeds ±0.05, increase. The value increases when the per-unit voltage value fluctuates within ±0.

05. The value of .

4. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 3, characterized in that, The establishment of the intra-cluster objective function under different voltage identifiers specifically includes: When the per-unit voltage value exceeds ±0.05, an objective function is established to minimize the voltage offset, and its expression is: In the formula, N is the total number of nodes in the cluster; Let be the voltage amplitude of the i-th node; This is the system's rated voltage; When the voltage per unit value fluctuates within ±0.05, an objective function is established to optimize the power balance within the cluster, and its expression is: In the formula, Let be the power value of the inflow to the Ck-th cluster at time t; T represents the net active power of each node in cluster Ck; T is the duration; and C is the number of clusters.

5. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 4, characterized in that, The branch power flow constraints are as follows: For branch ij in the cluster, it is represented as: For node j in the cluster, it is represented as: In the formula, Let be the square of the line current on branches i and j; and These represent the active power and reactive power flowing from branch i to branch j, respectively. Let be the voltage amplitude of node i within the group; and These are the line resistance and reactance between node i and node j, respectively; This indicates that node k is the end node on the branch that starts with j; Node i represents the first node on the branch that ends at j; , and These represent the photovoltaic active power, energy storage active power, and load active power at node j, respectively. , and These represent the photovoltaic reactive power, energy storage reactive power, and load reactive power at node j, respectively.

6. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 4, characterized in that, The operational safety constraints are as follows: In the formula, and These are the upper and lower limits of the voltage amplitude at that node, respectively. This is the safe current for overload operation of branches i and j.

7. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 4, characterized in that, The photovoltaic power constraints are as follows: In the formula, This represents the maximum controllable active power of photovoltaic power. This is the rated apparent power of the photovoltaic system; The energy storage power constraint is as follows: when , ; when , ; In the formula, This represents the maximum active power of the energy storage system. , These represent the maximum and minimum values ​​of the energy storage charge capacity. t represents the charging / discharging time; , For the charging and discharging efficiency of the energy storage system; It is expressed as the energy storage capacity.

8. The adaptive voltage regulation and power balance photovoltaic-storage clustering control method according to claim 4, characterized in that, The inter-cluster boundary variable constraints are as follows: In the formula, a and b represent the iterative values ​​of active and reactive power flowing from the previous cluster to the next cluster, respectively; and These represent the active and reactive power flowing from the previous cluster to the next cluster, respectively. This is a penalty coefficient used to ensure the convergence of boundary data; and The Lagrange multiplier is used to iterate the active and reactive power flow from the previous cluster to the next cluster. Inter-cluster variable updates are as follows: In the formula, k is the number of iterations; and The active and reactive power flowing out of the upstream cluster, respectively; and The active and reactive power flowing into the downstream clusters are separated.

Images