Power distribution network feeder line cluster optimization method, system and device and storage medium

Abstract

The invention discloses a power distribution network feeder cluster optimization method, system and device and a storage medium, and the method comprises the steps: inputting model parameters, and collecting determined cluster parameters; 2, determining an optimization target of a distributed photovoltaic cluster according to the voltage level of the dominant node, and achieving inter-cluster coordination optimization facing the real-time regulation and control requirements of the power distribution network; the feed line cluster optimization aims at improving the operation economy of the power distribution network and ensuring the operation safety of the power distribution network, and the corresponding distributed photovoltaic clusters are divided into economic regulation and control clusters and emergency regulation and control clusters; and step 3, collecting real-time power of each distributed photovoltaic field station in the cluster, and distributing a power instruction obtained by coordination optimization between upper-layer clusters to each distributed photovoltaic field station in the cluster in real time according to a distribution principle. According to the invention, each distributed photovoltaic is coordinated to participate in the voltage control of the power distribution network in the form of a distributed photovoltaic cluster, and the regional voltage control of the power distribution network is realized through the multi-stage cooperation of global optimization scheduling and feed line cluster optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network cluster optimization, and in particular to a distribution network feeder cluster optimization method, system, device and storage medium. Background Art

[0002] For distribution networks containing large-scale distributed power sources and flexible loads, considering their radial structure and the various traditional reactive power regulation devices contained in the network, their optimal operation is essentially an optimal power flow problem that includes both continuous and discrete decision variables, and is a typical mixed integer non-convex nonlinear programming problem.

[0003] In order to solve the power quality problems such as voltage fluctuation and voltage over-limit and the "curse of dimensionality" of state control quantity caused by the large-scale access of distributed power sources to the distribution network, the present invention proposes a feeder cluster optimization model based on power stratification, which consists of optimization between clusters and power allocation within clusters. According to the operating voltage level of the dominant node in the region, an economic cluster optimization based on distributed photovoltaic reactive power optimization is proposed to improve the economic efficiency of distribution network operation; an emergency cluster optimization based on distributed photovoltaic active and reactive power coordinated optimization is proposed to quickly respond to voltage over-limit events of dominant nodes in the region and ensure the safe operation of the distribution network. Summary of the invention

[0004] Purpose of the invention: In view of the problems in the background technology, the present invention discloses a distribution network feeder cluster optimization method, system, device and storage medium to ensure the safe operation of the distribution network.

[0005] Technical solution: The present invention discloses a distribution network feeder cluster optimization method, comprising the following steps:

[0006] Step 1: Model parameter input, collect the determined cluster parameters, including: the inductive reactive adjustable capacity of the distributed photovoltaic cluster v and reactive power Active adjustable capacity and real active power Voltage reactive sensitivity and voltage active sensitivity Where C represents cluster, v represents distributed photovoltaic cluster, S represents capacity, P represents active power, J represents sensitivity, and Q represents reactive power;

[0007] Step 2: Inter-cluster coordination optimization: determine the optimization target of the distributed photovoltaic cluster according to the voltage level of the dominant node, and realize inter-cluster coordination optimization for the real-time control needs of the distribution network;

[0008] The inter-cluster coordination optimization objectives are divided into two categories: improving the economic operation of the distribution network and ensuring the safety of the distribution network operation. The distributed photovoltaic cluster that improves the economic operation of the distribution network is the economic regulation cluster, which is triggered by event 1; the distributed photovoltaic cluster that ensures the safety of the distribution network operation is the emergency regulation cluster, which is triggered by event 2.

[0009] Event 1 is when the voltage of the dominant node is in the voltage warning zone, triggering the economic cluster optimization, optimizing the reactive power of the cluster, and obtaining the reactive power instruction of cluster v

[0010] Event 2 is when the voltage of the dominant node is in the unqualified voltage zone, triggering the emergency cluster optimization to coordinate and optimize the active and reactive power of the cluster, and obtaining the active and reactive power instructions of cluster v.

[0011] Furthermore, when optimizing the distribution network feeder cluster, the power in the cluster is also allocated, as follows:

[0012] Collect the real-time power of each distributed photovoltaic station in the cluster, grasp the real-time operating status of the station, distribute the power instructions obtained by the upper-level cluster coordination optimization to each distributed photovoltaic station in the cluster in real time, and obtain the setting instructions of each station, so that the distributed photovoltaic cluster v outputs the corresponding power to follow the instructions of the upper-level cluster coordination optimization, and realize the closed-loop control of the voltage of the dominant node of the distribution network.

[0013] Furthermore, when the power instructions obtained by the upper-level cluster coordination optimization are distributed in real time to each distributed photovoltaic station in the cluster and the setting instructions of each station are obtained, when the upper-level cluster coordination optimization belongs to economic cluster optimization, the reactive power setting instructions are obtained. When the upper-layer cluster coordination optimization belongs to emergency cluster optimization, active and reactive power setting instructions are obtained. Where m is the number of economic clusters, n is the number of emergency clusters, represents the reactive power setting instructions of m economic clusters, They respectively represent n emergency cluster active power setting instructions and n emergency cluster reactive power setting instructions.

[0014] Furthermore, the step 2 also includes economic cluster optimization: based on the principle of minimizing the voltage deviation of the dominant node and minimizing the change of cluster reactive power, the objective function is as follows:

[0015]

[0016] in, is the reactive power regulation amount due to cluster v The voltage change of the dominant node caused by is the voltage-reactive sensitivity of cluster v relative to the dominant node based on sensitivity analysis; is the deviation between the actual voltage of the dominant node and the optimal reference voltage, is the optimal reference value of the dominant node voltage obtained according to the global optimization scheduling model, is the real-time voltage value of the dominant node, is the reactive power command of cluster v, m is the number of economic clusters, and α1 and β1 are the weight coefficients of the sub-objective function.

[0017] Furthermore, the step 2 also includes emergency cluster optimization: with the goal of minimizing the voltage deviation of the dominant node, minimizing the active power reduction of the cluster, and minimizing the reactive power change of the cluster, the objective function is as follows:

[0018]

[0019] in, is the reactive power regulation amount due to cluster v The voltage change of the dominant node caused by is the voltage-reactive sensitivity of cluster v relative to the dominant node based on sensitivity analysis; is the deviation between the actual voltage of the dominant node and the optimal reference voltage, is the optimal reference value of the dominant node voltage obtained according to the global optimization scheduling model, is the real-time voltage value of the dominant node, is the reactive power command of cluster v, is the active power command of cluster v, n is the number of emergency clusters, α2, β2 and γ are the weight coefficients of each sub-objective function.

[0020] Furthermore, in step 3, reactive power is distributed in an equal power factor manner to ensure that the power factors of each station are equal and that the power factor of a single station does not exceed the limit. Specifically:

[0021] Assuming that there are x distributed photovoltaic stations in cluster v, the reactive power control instruction of the jth station is:

[0022]

[0023] Among them, P j,max is the maximum active power of the jth station in the distributed photovoltaic cluster v, represents the reactive power control instruction of the jth station;

[0024] According to the above allocation method, the reference power factors of all stations in cluster v are:

[0025]

[0026] The above allocation method can give full play to the reactive power regulation potential of each station, ensure that each station has the same reactive power margin, and prevent the reactive power exceeding the limit of a distributed photovoltaic station from causing a chain reaction in other stations.

[0027] The present invention also discloses a system based on the above-mentioned distribution network feeder cluster optimization method, including a data acquisition module, an inter-cluster coordination optimization module, and an intra-cluster power allocation module;

[0028] The data acquisition module is used to collect cluster parameters, including: the inductive reactive adjustable capacity of the distributed photovoltaic cluster v and reactive power Active adjustable capacity and real active power Voltage reactive sensitivity and voltage active sensitivity

[0029] The inter-cluster coordination optimization module is used to determine the optimization target of the distributed photovoltaic cluster according to the voltage level of the dominant node, and realize the inter-cluster coordination optimization for the real-time control demand of the distribution network; the optimization targets of the feeder cluster are divided into two categories: improving the economic efficiency of the distribution network operation and ensuring the safety of the distribution network operation, and the corresponding distributed photovoltaic clusters are divided into economic control clusters and emergency control clusters;

[0030] The intra-cluster power allocation module is used to allocate power instructions obtained by upper-layer inter-cluster coordination optimization to each distributed photovoltaic station in the cluster in real time according to the real-time power of each distributed photovoltaic station in the cluster and the allocation principle.

[0031] The present invention also discloses a distribution network feeder cluster optimization device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned distribution network feeder cluster optimization method when loaded into the processor.

[0032] The present invention also discloses a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the steps of the above-mentioned distribution network feeder cluster optimization method.

[0033] Beneficial Effects

[0034] The distribution network voltage multi-level coordinated control strategy proposed in the present invention coordinates each distributed photovoltaic to participate in the distribution network voltage control in the form of a distributed photovoltaic cluster, and realizes the distribution network regional voltage control through multi-level coordination of global optimization scheduling and feeder cluster optimization.

[0035] The physical meaning of the economic cluster objective function set in the present invention is: minimizing the voltage deviation of the dominant node, which implies the goal of making the voltage of each node in the area close to the optimal reference value; avoiding repeated and drastic changes in the power of the distributed photovoltaic cluster, which implies that the actual operating status of each cluster needs to be considered when allocating control tasks, so that each cluster has a more uniform reactive power regulation margin, and the goal of improving control efficiency and extending the life of the photovoltaic inverter.

[0036] The physical meaning of the emergency cluster objective function set in the present invention is: taking voltage safety as the primary goal, eliminating voltage over-limit events in a short time through joint adjustment of active and reactive power; under the premise of ensuring voltage safety, reducing the active power of distributed photovoltaics as little as possible and improving the utilization rate of distributed photovoltaics; in addition, it also includes the physical meaning of the economic cluster objective function. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the cluster optimization model framework of the distribution network feeder cluster optimization method of the present invention.

[0038] Figure 2 Schematic diagram of node voltage status under different interactive mechanisms of the distribution network feeder cluster optimization method of the present invention.

[0039] Figure 3 A schematic diagram of the low-voltage distribution station area topology of the distribution network feeder cluster optimization method of the present invention.

[0040] Figure 4 Schematic diagram of PV node net load of the distribution network feeder cluster optimization method of the present invention.

[0041] Figure 5 It is a schematic diagram of the voltage of key nodes before voltage regulation of the distribution network feeder cluster optimization method of the present invention.

[0042] Figure 6 It is a schematic diagram of the voltage of key nodes after voltage regulation of the distribution network feeder cluster optimization method of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] Reference Figure 1 , provides a distribution network feeder cluster optimization method and system, comprising the following steps:

[0048] Step 1: Data acquisition module, model parameter input, acquisition of determined cluster parameters, including: inductive reactive adjustable capacity of distributed photovoltaic cluster v and reactive power Active adjustable capacity and real active power Voltage reactive sensitivity and voltage active sensitivity Where C represents cluster, v represents distributed photovoltaic cluster, S represents capacity, P represents active power, J represents sensitivity, and Q represents reactive power.

[0049] Step 2: The inter-cluster coordination optimization module determines the optimization target of the distributed photovoltaic cluster according to the voltage level of the dominant node, and realizes inter-cluster coordination optimization for the real-time control needs of the distribution network.

[0050] The inter-cluster coordination optimization objectives are mainly divided into two categories: improving the economic operation of the distribution network and ensuring the safety of the distribution network operation. The corresponding distributed photovoltaic clusters are divided into economic control clusters and emergency control clusters, which are triggered by events 1 and 2 respectively. That is, the inter-cluster coordination optimization objectives are divided into two categories: improving the economic operation of the distribution network and ensuring the safety of the distribution network operation. The distributed photovoltaic cluster that improves the economic operation of the distribution network is the economic control cluster, which is triggered by event 1; the distributed photovoltaic cluster that ensures the safety of the distribution network operation is the emergency control cluster, which is triggered by event 2. Event 1 is that the dominant node voltage is in the voltage warning zone, which triggers the economic cluster optimization and optimizes the reactive power of the cluster; event 2 is that the dominant node voltage is in the voltage unqualified zone, which triggers the emergency cluster optimization to coordinate the optimization of the active and reactive power of the cluster and obtain the reactive power instruction. Or active and reactive power instructions

[0051] Step 3: The power distribution module within the cluster collects the real-time power of each distributed photovoltaic station in the cluster, grasps the real-time operation status of the station, and distributes the power instructions obtained by the upper-level cluster coordination optimization to each distributed photovoltaic station in the cluster in real time according to the distribution principle, and obtains the reactive power setting instructions of each station. Or active and reactive power setting instructions As a result, the distributed photovoltaic cluster v outputs the corresponding power to follow the coordinated optimization instructions of the upper cluster, realizing closed-loop control of the voltage of the dominant node in the distribution network.

[0052] Step 4: Case analysis, by analyzing the correlation and difference of different control levels in the voltage control effect of distribution network, in the scenario of triggering economic cluster optimization, analyze the effectiveness and superiority of introducing feeder cluster optimization.

[0053] Among them, step 2 includes economic cluster optimization: based on the principle of minimizing the voltage deviation of the dominant node and minimizing the change of cluster reactive power, the objective function is as follows:

[0054]

[0055] in, is the reactive power regulation amount due to cluster v The voltage change of the dominant node caused by is the voltage-reactive sensitivity of cluster v relative to the dominant node based on sensitivity analysis; is the optimal reference value of the dominant node voltage obtained according to the global optimization scheduling model, is the real-time voltage value of the dominant node, is the deviation between the actual voltage of the dominant node and the optimal reference voltage; is the reactive power command of cluster v, m is the number of economic clusters, and α1 and β1 are the weight coefficients of the sub-objective function.

[0056] Specifically, the physical meaning of the economic cluster objective function is: minimizing the voltage deviation of the dominant node, which implies the goal of making the voltage of each node in the region close to the optimal reference value; avoiding repeated and drastic changes in the power of the distributed photovoltaic cluster, which implies that the actual operating status of each cluster must be considered when allocating control tasks, so that each cluster has a more uniform reactive regulation margin, improving the control efficiency and extending the life of the photovoltaic inverter.

[0057] Among them, step 2 includes emergency cluster optimization: the goal is to minimize the voltage deviation of the dominant node, minimize the active power reduction of the cluster, and minimize the change of the reactive power of the cluster. The objective function is as follows:

[0058]

[0059] in, is the real-time value of cluster v active power, is the active optimization instruction of cluster v, n is the number of emergency clusters, α2, β2 and γ are the weight coefficients of each sub-objective function.

[0060] Specifically, the physical meaning of the emergency cluster objective function is: taking voltage safety as the primary goal, eliminating voltage over-limit events in a short time through active and reactive power co-regulation; under the premise of ensuring voltage safety, reducing the active power of distributed photovoltaics as little as possible and improving the utilization rate of distributed photovoltaics; in addition, it also includes the physical meaning of the economic cluster objective function.

[0061] Among them, in step 3, in order to avoid the situation that some stations have uncoordinated active and reactive output or even reactive power exceeds the limit, the reactive power is distributed in an equal power factor manner to ensure that the power factors of each station are equal and that the power factor of a single station does not exceed the limit. Specifically:

[0062] Assuming that there are x distributed photovoltaic stations in cluster v, the reactive power control instruction of the jth station is:

[0063]

[0064] Among them, P j,max is the maximum active power of the jth station in the distributed photovoltaic cluster v, Represents the reactive power control instruction of the jth station.

[0065] According to this allocation method, the reference power factors of all stations in cluster v are

[0066]

[0067] The above allocation method can give full play to the reactive power regulation potential of each station, ensure that each station has the same reactive power margin, and prevent the reactive power exceeding the limit of a distributed photovoltaic station from causing a chain reaction in other stations.

[0068] The above-mentioned distribution network feeder cluster optimization method is applied to a distribution network feeder cluster optimization device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, a process according to the above-mentioned distribution network feeder cluster optimization method is implemented.

[0069] With respect to the above-mentioned distribution network feeder cluster optimization method, the present invention further discloses a computer storage medium storing computer executable instructions, and the computer executable instructions are used to execute the steps of the above-mentioned distribution network feeder cluster optimization method.

[0070] Furthermore, the example analysis in step 4 is as follows:

[0071] When the weather changes from cloudy to sunny, the active power of each distributed photovoltaic system increases rapidly. If the line load is light at this time, it may cause a large-scale voltage rise or even exceed the limit. In this case, the traditional distribution network adjusts the feeder voltage by investing in parallel reactors or adjusting the OLTC gear. The power uncertainty of distributed photovoltaics may cause equipment switching oscillation. Considering the participation of distributed photovoltaics in the distribution network voltage control, its large-scale decentralized access increases the difficulty of coordination between devices. The multi-level coordinated control strategy for the distribution network voltage proposed in this scheme coordinates the participation of each distributed photovoltaic system in the distribution network voltage control in the form of distributed photovoltaic clusters, and realizes the regional voltage control of the distribution network through global optimization scheduling and multi-level coordination of feeder cluster optimization.

[0072] By analyzing the correlation and difference of different control levels in the voltage control effect of the distribution network, the effectiveness and superiority of introducing feeder cluster optimization are analyzed in the scenario of triggering economic cluster optimization. The voltage status of the distribution network is compared in three cases: only global optimization scheduling (S1), voltage coordinated control by global optimization scheduling and local consumption (S2), and cluster-based multi-level coordinated optimization (S3), including the voltage change of the dominant node and the voltage distribution of each node in the region at time t4. Figure 2 shown.

[0073] Furthermore, due to the sudden increase in daytime light intensity and low load level, the voltage of the dominant node rose rapidly, approaching the upper limit of 1.07 at t1, triggering the local consumption control with dynamic correction link, and using its own reactive adjustable capacity for closed-loop control. At t3, the voltage of the dominant node operated in the warning zone, and the three economic clusters coordinated the photovoltaic reactive power to make the voltage of the dominant node close to the high-quality zone, improving the economic efficiency of the distribution network operation.

[0074] Depend on Figure 2 (a) It can be seen intuitively that due to the introduction of the dynamic correction link in the local consumption stage, the voltage over-limit event that may occur again at time t2 is effectively avoided. Comparing the control results of S1 and S2, S2 and S3, it can be concluded that on the basis of global optimization, local consumption and feeder cluster optimization stages have the effect of suppressing the trend of distribution network voltage over-limit, but the degree of improvement on distribution network voltage is different. Local consumption can only use its limited reactive power regulation ability to suppress local voltage over-limit, while feeder cluster optimization can coordinate the distributed photovoltaic power to jointly suppress voltage over-limit, and the effect is more significant.

[0075] superior Figure 2 At time t4 shown in (a), the overall voltage distribution of each node in the region under the action of S1, S2 and S3 is as shown above Figure 2(b) As shown. It can be seen intuitively that under the action of S1 and S2, there is a large deviation between the voltage of each node and the optimal reference value, while under the action of S3, the voltage of each node in the region is closer to the optimal reference value, indicating that the use of S3 can improve the economy of the overall operation of the distribution network to a certain extent. The network loss, voltage deviation and photovoltaic reactive power (negative sign indicates reactive power) under each control strategy are shown in the following table:

[0076] Table 1 Distribution network operation status under different participation strategies of distributed photovoltaics

[0077]

[0078] From the analysis of the table data, it can be seen that compared with S1, if S2 is adopted, the network loss will increase by 0.314%, and the node average voltage deviation will decrease by 18.14%; if S3 is adopted, the network loss will increase by 14.32%, and the node average voltage deviation will decrease by 86.94%. From the data analysis, it can be seen that the local consumption and feeder cluster optimization stages mainly adjust the system node voltage quickly by changing the distributed photovoltaic reactive power, which may cause an increase in system network loss, but both can play a relatively positive role in eliminating voltage deviations, and can achieve a better effect of the global optimization goal. Further analysis shows that compared with the impact of local consumption on the distribution network, cluster optimization has a slightly greater impact on the increase of network loss, but its effect on eliminating voltage deviations is also more significant, and it can better adapt to the increasingly high voltage control needs of the distribution network under the development situation of large-scale access of distributed photovoltaics.

[0079] In order to verify the real-time autonomy mechanism within the cluster, the cluster area autonomy mechanism uses an actual 15-node low-voltage distribution area in Jiangsu as an example system. The topology diagram is as follows: Figure 3 As shown, the PV access points are 6, 7, 9, 12, 14, and 15.

[0080] Specifically, the net load is Figure 4 As shown in the figure, due to the high proportion of PV access, the PV power is greater than the load power around 09:00-12:30 and 14:00-16:30, that is, the net load is less than zero, causing the active power to be reversed upward.

[0081] The voltage of key nodes before voltage regulation is as follows Figure 5 As shown, at 14:45, 15:00, and 15:15, the voltage at node 7 exceeded the limit to varying degrees.

[0082] When the voltage exceeds the limit, the photovoltaic inverter is controlled by voltage sensitivity fitting, and nodes with high sensitivity are selected first according to the voltage-power sensitivity, which minimizes the voltage regulation cost while ensuring the voltage regulation effect. Figure 6It can be seen that optimizing the control of the substation voltage according to the voltage sensitivity fitting method can effectively suppress the voltage exceeding the limit in the distribution station area and realize the substation voltage autonomy.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network feeder cluster optimization method, characterized in that: The following steps are involved: Step 1: Model parameter input, collect the determined cluster parameters, including: the inductive reactive adjustable capacity of the distributed photovoltaic cluster v and reactive power Active adjustable capacity and real active power Voltage reactive sensitivity and voltage active sensitivity Where C represents cluster, v represents distributed photovoltaic cluster, S represents capacity, P represents active power, J represents sensitivity, and Q represents reactive power; Step 2: Inter-cluster coordination optimization: determine the optimization target of the distributed photovoltaic cluster according to the voltage level of the dominant node, and realize inter-cluster coordination optimization for the real-time control needs of the distribution network; The goals of inter-cluster coordination optimization are divided into two categories: improving the economic operation of the distribution network and ensuring the safety of the distribution network operation. The distributed photovoltaic cluster that improves the economic operation of the distribution network is the economic regulation cluster, which is triggered by event 1; the distributed photovoltaic cluster that ensures the safety of the distribution network operation is the emergency regulation cluster, which is triggered by event 2. Event 1 is when the voltage of the dominant node is in the voltage warning zone, triggering the economic cluster optimization, optimizing the reactive power of the cluster, and obtaining the reactive power instruction of cluster v Event 2 is when the voltage of the dominant node is in the unqualified voltage zone, triggering the emergency cluster optimization to coordinate and optimize the active and reactive power of the cluster, and obtaining the active and reactive power instructions of cluster v.

2. A distribution network feeder cluster optimization method according to claim 1, characterized in that: When optimizing the distribution network feeder cluster, the power distribution within the cluster is also included, as follows: Collect the real-time power of each distributed photovoltaic station in the cluster, grasp the real-time operating status of the station, distribute the power instructions obtained by the upper-level cluster coordination optimization to each distributed photovoltaic station in the cluster in real time, and obtain the setting instructions of each station, so that the distributed photovoltaic cluster v outputs the corresponding power to follow the instructions of the upper-level cluster coordination optimization, and realize the closed-loop control of the voltage of the dominant node of the distribution network.

3. A distribution network feeder cluster optimization method according to claim 2, characterized in that: When the power instructions obtained by the upper-level cluster coordination optimization are distributed to each distributed photovoltaic station in the cluster in real time, and the setting instructions of each station are obtained, when the upper-level cluster coordination optimization belongs to economic cluster optimization, the reactive power setting instructions are obtained. When the upper-layer cluster coordination optimization belongs to emergency cluster optimization, active and reactive power setting instructions are obtained. Where m is the number of economic clusters, n is the number of emergency clusters, represents the reactive power setting instructions of m economic clusters, They respectively represent n emergency cluster active power setting instructions and n emergency cluster reactive power setting instructions.

4. The method for optimizing distribution network feeder clusters according to claim 1, characterized in that: The step 2 also includes economic cluster optimization: the principle is to minimize the voltage deviation of the dominant node and the change of cluster reactive power. The objective function is as follows: in, is the reactive power regulation amount due to cluster v The voltage change of the dominant node caused by is the voltage-reactive sensitivity of cluster v relative to the dominant node based on sensitivity analysis; is the deviation between the actual voltage of the dominant node and the optimal reference voltage, is the optimal reference value of the dominant node voltage obtained according to the global optimization scheduling model, is the real-time voltage value of the dominant node, is the reactive power command of cluster v, m is the number of economic clusters, and α1 and β1 are the weight coefficients of the sub-objective function.

5. The method for optimizing distribution network feeder clusters according to claim 1, characterized in that: The step 2 also includes emergency cluster optimization: the goal is to minimize the voltage deviation of the dominant node, minimize the active power reduction of the cluster, and minimize the change of the reactive power of the cluster. The objective function is as follows: in, is the reactive power regulation amount due to cluster v The voltage change of the dominant node caused by is the voltage-reactive sensitivity of cluster v relative to the dominant node based on sensitivity analysis; is the deviation between the actual voltage of the dominant node and the optimal reference voltage, is the optimal reference value of the dominant node voltage obtained according to the global optimization scheduling model, is the real-time voltage value of the dominant node, is the reactive power command of cluster v, is the active power command of cluster v, n is the number of emergency clusters, α2, β2 and γ are the weight coefficients of each sub-objective function.

6. The method for optimizing distribution network feeder clusters according to claim 2, characterized in that: In step 3, reactive power is distributed in an equal power factor manner to ensure that the power factors of each station are equal and that the power factor of a single station does not exceed the limit. Specifically: Assuming that there are x distributed photovoltaic stations in cluster v, the reactive power control instruction of the jth station is: Among them, P j,max is the maximum active power of the jth station in the distributed photovoltaic cluster v, represents the reactive power control instruction of the jth station; According to the above allocation method, the reference power factors of all stations in cluster v are: The above allocation method can give full play to the reactive power regulation potential of each station, ensure that each station has the same reactive power margin, and prevent the reactive power exceeding the limit of a distributed photovoltaic station from causing a chain reaction in other stations.

7. A system based on the distribution network feeder cluster optimization method according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, inter-cluster coordination and optimization module, and intra-cluster power allocation module; The data acquisition module is used to collect cluster parameters, including: the inductive reactive adjustable capacity of the distributed photovoltaic cluster v and reactive power Active adjustable capacity and real active power Voltage reactive sensitivity and voltage active sensitivity The inter-cluster coordination optimization module is used to determine the optimization target of the distributed photovoltaic cluster according to the voltage level of the dominant node, and realize the inter-cluster coordination optimization for the real-time control demand of the distribution network; the optimization targets of the feeder cluster are divided into two categories: improving the economic efficiency of the distribution network operation and ensuring the safety of the distribution network operation, and the corresponding distributed photovoltaic clusters are divided into economic control clusters and emergency control clusters; The intra-cluster power allocation module is used to allocate power instructions obtained by upper-layer inter-cluster coordination optimization to each distributed photovoltaic station in the cluster in real time according to the real-time power of each distributed photovoltaic station in the cluster and the allocation principle.

8. A distribution network feeder cluster optimization device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, the steps of the distribution network feeder cluster optimization method according to any one of claims 1 to 6 are implemented.

9. A computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are used to execute the steps of the distribution network feeder cluster optimization method according to any one of claims 1 to 6.

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