Power distribution network simulation method for high-permeability distributed photovoltaic cluster control

Abstract

The invention discloses a power distribution network simulation method for high-permeability distributed photovoltaic cluster control, and the method comprises the steps: S1, carrying out the clustering division of a cluster according to the electrical distance between impedances, calculating the sensitivity of each voltage in the divided cluster, and selecting a node with the highest comprehensive sensitivity as a dominant node of the cluster; s2, if the voltage and power data of each node do not meet the constraint condition, transferring inverters with adjustable reactive power capacity in other clusters, selecting a cluster with reactive power margin and the maximum sensitivity factor with a dominant node according to the voltage influence sensitivity factor of the dominant node between the clusters, and carrying out dominant node voltage control and voltage regulation on the cluster; s3, building data such as active power, reactive power, voltage amplitude and network loss in the power distribution network in simulation software for simulation, and returning to the step S2 if constraint conditions are not met; if the constraint condition is satisfied, the method can be applied to an entity power distribution system to carry out the optimization process of the power distribution network, thereby improving the operation efficiency of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of power distribution system operation optimization, and in particular to a power distribution network simulation method for high-penetration distributed photovoltaic cluster control. Background Art

[0002] As my country attaches great importance to the development of renewable energy and promotes the goal of building a new power system, due to the small capacity of distributed power sources such as photovoltaic, wind power, and hydropower stations, large-scale access to the distribution network will have a huge impact on the stability and economy of the distribution network. The output of distributed power sources is intermittent and highly random, and cannot be regarded as a simple load. When the power of large-scale high-penetration photovoltaic power generation is disorderly connected to the distribution network, it will change the flow direction of the system, causing problems such as flow reversal and voltage fluctuations, and ultimately lead to voltage over-limit. According to the overvoltage protection requirements of the distributed photovoltaic grid-connected technical specifications, when the grid-connected point voltage is not within the specified range, the distributed photovoltaic power input to the grid should be stopped. This not only has a great impact on the stable operation of the power grid and the network loss of the distribution network, but also has immeasurable damage to the user equipment and the economic benefits of investors.

[0003] At present, voltage regulation equipment such as shunt capacitors and on-load tap-changing transformers are the mainstream methods for voltage control in distribution networks. These traditional methods cannot solve the problems of voltage fluctuations and voltage over-limit. Another solution is to configure appropriate energy storage devices in photovoltaic power stations and treat batteries as supercapacitors to smooth out voltage fluctuations caused by distributed power sources, but this method is expensive and cannot be widely used in my country's medium and low voltage distribution networks at an economic level.

[0004] In order to better ensure the safe, stable and economical operation of the distributed power supply connected to the grid, the dominant node is first determined in each power supply cluster based on the comprehensive sensitivity, and then the capacity of the distributed power supply in the cluster and the voltage deviation between the dominant nodes are calculated. For the nodes with voltage exceeding the limit, some nodes with reactive power margin can be used to compensate, and finally the goal of node voltage control is achieved. This control method can effectively adapt to the grid voltage regulation, reduce voltage fluctuations, improve node voltage, and improve the stability of the grid. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a distribution network simulation method for high-penetration distributed photovoltaic cluster control, which improves the voltage of each node in the photovoltaic power station connected to the distribution network by mutual compensation of reactive power between each cluster, reduces voltage over-limit, and improves the stability of distribution system operation.

[0006] Technical solution: A distribution network simulation method for high-penetration distributed photovoltaic cluster control, including the following steps:

[0007] S1, clustering is performed based on the electrical distance between impedances, and the sensitivity of each voltage in the cluster is calculated, and the node with the highest comprehensive sensitivity is selected as the dominant node of the cluster;

[0008] S2, if the voltage and power data of each node do not meet the constraints, mobilize the inverters with adjustable reactive capacity in other clusters, and select the cluster with reactive margin and the largest sensitivity factor with the dominant node through the voltage influence sensitivity factor of the main node between the clusters, and control the dominant node voltage for voltage regulation;

[0009] S3, build the active power, reactive power, voltage amplitude, network loss and other data in the distribution network in the simulation software for simulation. If the constraints are not met, return to step S2; if the constraints are met, it can be applied to the physical distribution system to optimize the distribution network.

[0010] Furthermore, in step S1, the implementation steps of constructing a distribution network simulation method for high-penetration distributed photovoltaic cluster control for a physical distribution system are as follows:

[0011] S11, the modularity definition based on electrical distance weight is used to describe the coupling degree between nodes, and the optimal partitioning of the system is determined by measuring the overall modularity of the system;

[0012] S12, based on the characteristics of the optimally divided cluster, the comprehensive sensitivity of each node is calculated, and the node with the highest comprehensive sensitivity is the dominant node.

[0013] Furthermore, in step S1, clusters are divided according to electrical distances:

[0014]

[0015] Where: ρ is the system modularity; m is the sum of network edge weights; k i and k j are the sum of the edge weights of the edges connected to node i and node j, respectively, ij is the electrical distance between node i and node j.

[0016] Taking into account the power balance of the system, the overall module, the electrical distance and other factors, on the basis of satisfying the autonomy of each cluster area, the system division variables are used to establish the following cluster division model:

[0017] F=λ1f1+λ2f2

[0018] Among them: λ1 and λ2 are the weight coefficients of different indicators respectively, where λ1+λ2=1, and in this paper, λ1=λ2=0.5.

[0019] Further, in step S1, the sensitivity of all voltage nodes is calculated according to the observability and controllability of each node in the cluster. The dominant node is the node with the highest comprehensive sensitivity. The comprehensive sensitivity calculation equation is:

[0020]

[0021] in: represents the observability of node i, is the voltage sensitivity of node k to node i, S G It is the set of all controllable nodes in the area; represents the controllability of node i, is the reactive voltage sensitivity, S G is the set of all controllable nodes in the area; β is the weight coefficient, and its value is determined according to the proportion of controllability and observability of the dominant node.

[0022] The calculation formulas for reactive power-voltage sensitivity and active power-voltage sensitivity are:

[0023]

[0024] in: and are the active power-voltage sensitivity matrix and the reactive power-voltage sensitivity matrix respectively; G and B are the conductance and susceptance parts in the node admittance matrix respectively.

[0025] Further, in step S2, a distribution network optimization model for high penetration distributed photovoltaics is constructed as follows:

[0026] (1) Objective function

[0027] The optimization objective function is constructed with the lowest operating cost of the distribution network as the optimization goal, which includes the power purchase cost f from the upper power grid. pb and the network loss cost of the distribution network system loss , the optimization objective function of the distribution network can be expressed as follows:

[0028] F=minf

[0029] f=f ab +f loss

[0030]

[0031] Where: c pb 、c loss are the electricity purchase price from the distribution network to the main network and the network loss price respectively; T is the total dispatching time, which is 24 hours; Δt is the dispatching interval, which is 1 hour; P loss,t is the line active power loss; For the merits purchased from the main network;

[0032] (2) Constraints

[0033] Node voltage constraints:

[0034] U jmax ≤U j ≤U jmax

[0035] Among them: U jmax and U jmin are the upper and lower limits of the voltage amplitude at node j.

[0036] Active output constraints of photovoltaic power stations:

[0037] 0≤P i,PV ≤P i,PVpre

[0038] Where: P i,PVpre is the predicted active output value, P i,PV It is the actual active output value.

[0039] Reactive power output constraints of photovoltaic power stations:

[0040]

[0041] Where: P i,PV is the actual active output value, Q i,PV is the actual reactive power output value, S j,PCS,max is the maximum capacity of the adjustable PV power station PCS at node j.

[0042] Node power balance constraints:

[0043]

[0044] Where: P j,PV and Q j,PV They represent the active and reactive power injected by the PV power station to node j, P j and Q j They represent the active and reactive power injected into the grid by node j, P ij and Q ij are respectively the active and reactive power at the head end of branch ij, P j,d and Q j,d They represent the active and reactive power of the load at node j, respectively. The set u(j) is the set of the head nodes of the branch with node j as the terminal node. ij is the reactance of branch ij, U j is the output voltage amplitude of node j.

[0045] Further, in step S2, the cluster control voltage difference and reactive power adjustment amount are calculated, and the actual voltage value Upilot and the actual value of the boundary voltage U lim The difference between them can be expressed as ΔU pilot , the calculation formula is as follows:

[0046] ΔU pilot =U pilot -U lim

[0047] From the sensitivity analysis, we can know that:

[0048] ΔU pilot =[S c1 ,...,S cn ]·[ΔQ c1 ,...,ΔQ cn ] T

[0049] Where: ΔQ cn is the total reactive power adjustment of n clusters; S cn is the voltage sensitivity of cluster n to the dominant node. Based on the analysis of voltage sensitivity, the reactive power adjustment formula of each cluster can be obtained as follows:

[0050]

[0051] Among them: Q is the total reactive power adjustment.

[0052] The reactive power adjustment of the cluster control master station can be obtained by the above two formulas, and the formula is as follows.

[0053]

[0054] After determining the reactive power adjustment amount, the reactive power of each distributed photovoltaic power station is redistributed according to the comprehensive sensitivity relationship between each station controller and the dominant node. The sensitivity relationship is as follows:

[0055]

[0056] Where: Q k is the reactive power distribution of the kth distributed photovoltaic power station of the station controller; S j is the voltage sensitivity of cluster j to the dominant node.

[0057] Using the cluster-oriented dominant node voltage control method, after the reactive power of each cluster is redistributed, the node voltage simulation data is analyzed. If the voltage of some nodes still exceeds the limit, the optimization process is carried out again according to the optimization results of this time, and step S2 is repeated until the voltage requirements of each node are met. That is, the structural optimization of the distributed photovoltaic system of the distribution system is completed, and the stability of the distribution network is improved.

[0058] Compared with the prior art, the present invention has the following significant effects:

[0059] 1. The present invention can form a cluster by dispatching the surrounding distributed power sources according to the voltage of the node with voltage degradation through the cluster-oriented dominant node voltage control. The distributed power sources with closely related voltages bear more reactive power output, and the voltage selectivity advantage of the strategy is more obvious;

[0060] 2. The present invention can effectively improve the node voltage in the supply area through mutual reactive power compensation between clusters. The dominant node voltage control method has the best effect among the existing voltage control methods, which improves the stability of the distribution system operation and provides a new idea for solving the reactive power optimization problem of the new distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of the present invention;

[0062] Figure 2 A cluster division diagram of a distributed photovoltaic power station in the present invention;

[0063] Figure 3 A simulation topology diagram of a power distribution system used in the present invention;

[0064] Figure 4 It is a comparison diagram of voltage nodes under four control strategies in the present invention;

[0065] Figure 5 It is a network node voltage diagram under the control of the distributed power supply cluster in the present invention;

[0066] Figure 6 This is a parameter table of the photovoltaic power generation system in the present invention. DETAILED DESCRIPTION

[0067] In order to explain the purpose, technical solutions and advantages of the present invention more clearly, the present invention is described in detail below in conjunction with the accompanying drawings; it should be understood that the specific simulation cases described below are only used to explain the relevant contents of the present invention and are not used to limit the invention.

[0068] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0069] like Figure 1 As shown, a distribution network simulation method for high penetration distributed photovoltaic cluster control includes the following steps:

[0070] Step 1, clustering distributed photovoltaic power stations;

[0071] Step 11, the degree of electrical distance coupling of each node determines the optimal division. There are tens of thousands of nodes in the distribution system, and it is very complicated to conduct direct regulation. Therefore, cluster division must first be based on the line impedance between each node. Reasonable impedance division can reduce network loss and improve utilization; secondly, it must be divided according to the geographical location and the management scope of the power station to facilitate on-site compensation of reactive power, avoid long-distance transfer of reactive power, and reduce network loss; load characteristics must also be considered. The load distribution, curve and density in different places are not the same; finally, the user's electricity consumption must be considered. Users of different electricity levels have different priorities, as well as the nature and habits of users. The nature and habits of industrial and residential electricity use are different. The cluster division of distributed photovoltaic power stations is as follows: Figure 2 shown.

[0072] Step 12: Determine the dominant node. The dominant node voltage selection requirement for cluster control should first be able to accurately reflect the voltage level of all nodes in the partition and have convenient voltage regulation capabilities. Therefore, each partition should select the most controllable node as the dominant node to ensure that it can represent the voltage level of the area.

[0073] Step 2: After the cluster division is completed, the parameters of each node need to be judged. For nodes that do not meet the constraints, the cluster with reactive power margin and the largest sensitivity factor with the dominant node is selected, and the dominant node voltage is controlled to adjust the voltage;

[0074] In order to meet the requirements of multiple performance indicators of the distribution system operation, each node must meet certain constraints. The present invention takes into account the voltage, power and other parameters of the power grid and the economic investment of the distribution network. Under the condition of appropriate economic investment, the node voltage, power and other parameters of each cluster are adjusted to their constraints. In order to facilitate the solution of the problem, the parameters of the above conditions are fuzzy processed. The cluster-controlled distribution network simulation method proposed in the present invention has a significant effect on optimizing the stability of the distribution network voltage, and meets various conditions and requirements for the safe and stable operation of the new distribution network, so it has certain practical significance and application value.

[0075] (1) Objective function

[0076] The optimization objective function is constructed with the lowest operating cost of the distribution network as the optimization goal, which includes the power purchase cost f from the upper power grid. pb and the network loss cost of the distribution network system loss , the optimization objective function of the distribution network can be expressed as follows:

[0077] F=minf

[0078] f=f ab +f loss

[0079]

[0080] Where: c pb 、c loss are the electricity purchase price from the distribution network to the main network and the network loss price respectively; T is the total dispatching time, which is 24 hours; Δt is the dispatching interval, which is 1 hour; P loss,t is the line active power loss; For the merits purchased from the main network;

[0081] (2) Constraints

[0082] Node voltage constraints:

[0083] U jmax ≤U j ≤U jmax

[0084] Where: U jmax and U jmin are the upper and lower limits of the voltage amplitude at node j.

[0085] Active output constraints of photovoltaic power stations:

[0086] 0≤P i,PV ≤P i,PVpre

[0087] Where: P i,PVpre is the predicted active output value, P i,PV It is the actual active output value.

[0088] Reactive power output constraints of photovoltaic power stations:

[0089]

[0090] Where: P i,PV is the actual active output value, Q i,PV is the actual reactive power output value, S j,PCS,max is the maximum capacity of the adjustable PV power station PCS at node j.

[0091] Node power balance constraints:

[0092]

[0093]

[0094] Where: P j,PV and Q j,PV They represent the active and reactive power injected by the PV power station to node j, P j and Q j They represent the active and reactive power injected into the grid by node j, Pij and Q ij are respectively the active and reactive power at the head end of branch ij, P j,d and Q j,d They represent the active and reactive power of the load at node j, respectively. The set u(j) is the set of the head nodes of the branch with node j as the terminal node. ij is the reactance of branch ij, U j is the output voltage amplitude of node j.

[0095] Calculate the cluster control voltage difference and reactive power adjustment:

[0096] The actual voltage value of the dominant node is U pilot and the actual value of the boundary voltage U lim The difference between them can be expressed as ΔU pilot , the calculation formula is as follows:

[0097] ΔU pilot =U pilot -U lim

[0098] From the sensitivity analysis, we can know that:

[0099] ΔU pilot =[S c1 ,...,S cn ]·[ΔQ c1 ,...,ΔQ cn ] T

[0100] Where: ΔQ cn is the total reactive power adjustment of n clusters; S cn is the voltage sensitivity of cluster n to the dominant node.

[0101] Based on the analysis of voltage sensitivity, the reactive power adjustment formula of each cluster is as follows:

[0102]

[0103] Where: Q is the total reactive power adjustment.

[0104] The reactive power adjustment amount of the cluster control master station can be obtained by the above two formulas.

[0105]

[0106] After determining the reactive power adjustment amount, the reactive power of each distributed photovoltaic power station is redistributed according to the comprehensive sensitivity relationship between each station controller and the dominant node. The sensitivity relationship is as follows:

[0107]

[0108] Where: Q k is the reactive power distribution of the kth distributed photovoltaic power station of the station controller; S j is the voltage sensitivity of cluster j to the dominant node.

[0109] Step 3, using the cluster-oriented dominant node voltage control method, after the reactive power of each cluster is redistributed, the simulation data of the node voltage is analyzed. If the voltage of some nodes still exceeds the limit, the optimization process is carried out again according to the optimization results of this time, and step S2 is repeated until the voltage requirements of each node are met. That is, the structural optimization of the distributed photovoltaic system of the distribution system is completed, and the stability of the distribution network is improved.

[0110] The correctness of reducing voltage fluctuations is verified through simulation examples of typical scenarios.

[0111] In order to verify the effectiveness of the above-mentioned cluster-oriented dominant node voltage control method in solving the problem of voltage over-limit and fluctuation of distributed power supply, this embodiment carries out cluster control related analysis of dominant nodes on a distribution line in a certain area in a certain place in the west. The line structure topology of the area is shown in the figure below: Figure 3 As shown in the figure, the line has 33 nodes in total. The calculation example adopts the IEEE 33-node power distribution system, where the load is a constant power PQ type. The load level of the entire system is 3715kW of active power and 2300kvar of reactive power demand. Photovoltaic power generation systems are added to nodes 5, 7, 13, 17, 19, 24, 26, and 29 respectively. The specific parameters of the calculation example are as follows: Figure 6 shown in the table.

[0112] In order to ensure the feasibility and priority of cluster control methods for high-penetration distributed photovoltaic power stations, this simulation also compared and studied the four strategies of "constant power factor control (fix_cosФ)" based on local autonomous control of photovoltaic clusters, "reactive voltage droop control (reactive voltage droop)", "common connection point power factor correction control (PCC-PF-adjust)" and "cluster-oriented dominant node voltage control" based on the cluster monitoring system master station in terms of power consumption and network loss.

[0113] When the photovoltaic penetration rate reaches 100%, the base point is photovoltaic PF = 1 when comparing the photovoltaic power station "reactive voltage droop", "PPC-PFC" and "cluster-oriented dominant node voltage control". It can be seen that the effect based on "cluster-oriented dominant node voltage control" is the best. Figure 4 shown.

[0114] The distributed power generation cluster autonomous control strategy "reactive voltage droop" can adjust the feeder node voltage range to 1.019~1.047 based on local operation information collection and grid reactive support; "cluster-oriented dominant node voltage control" adjusts the node voltage range to 1.018~1.047, the voltage distribution is significantly improved, and overvoltage is avoided.

[0115] After actual verification, the voltage levels of each node in the cluster are controlled within the allowable range, and the voltage fluctuation is also reduced accordingly compared with before cluster control, which proves that the optimization method is practical and effective and can solve the problem of voltage over-limit and fluctuation in the distributed photovoltaic power station connected to the distribution system.

[0116] The distribution network simulation method for high-penetration distributed photovoltaic cluster control can form a cluster by dispatching the surrounding distributed power sources according to the voltage of the node with voltage deterioration through the cluster-oriented dominant node voltage control. The distributed power sources with closely related voltages undertake more reactive power output. The voltage selectivity advantage of the strategy is obvious, and it can effectively improve the node voltage in the supply area. The dominant node voltage control method has the best effect among the existing voltage control methods, which improves the stability of the distribution system operation and provides a new idea for solving the reactive power optimization problem of the new distribution system.

[0117] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. A distribution network simulation method for high penetration distributed photovoltaic cluster control, characterized in that: The steps include: S1, clustering is performed based on the electrical distance between impedances, and the sensitivity of each voltage in the cluster is calculated, and the node with the highest comprehensive sensitivity is selected as the dominant node of the cluster; S2, if the voltage and power data of each node do not meet the constraints, mobilize the inverters with adjustable reactive capacity in other clusters, and select the cluster with reactive margin and the largest sensitivity factor with the dominant node through the voltage influence sensitivity factor of the main node between the clusters, and control the dominant node voltage for voltage regulation; S3, build the active power, reactive power, voltage amplitude, network loss and other data in the distribution network in the simulation software for simulation. If the constraints are not met, return to step S2; if the constraints are met, it can be applied to the physical distribution system to optimize the distribution network.

2. The distribution network simulation method for high penetration distributed photovoltaic cluster control according to claim 1 is characterized in that: In step S1, it is also necessary to construct a cluster control method for a high-penetration distributed photovoltaic power station for a physical distribution system. The specific implementation steps are as follows: S11, the modularity definition based on electrical distance weight is used to describe the coupling degree between nodes, and the optimal partitioning of the system is determined by measuring the overall modularity of the system; S12, based on the characteristics of the optimally divided cluster, the comprehensive sensitivity of each node is calculated, and the node with the highest comprehensive sensitivity is the dominant node.

3. The distribution network simulation method for high penetration distributed photovoltaic cluster control according to any one of claims 1 or 2, characterized in that: In step S1, clusters are divided according to the electrical distance between impedances: Where: ρ is the system modularity; m is the sum of network edge weights; k i and k j are the sum of the edge weights of the edges connected to node i and node j, respectively, ij is the electrical distance between node i and node j; Taking into account the power balance, overall modules, and electrical distance factors of the system, on the basis of satisfying the autonomy of each cluster area, the system partition variables are used to establish the following cluster partition model: F=λ1f1+λ2f2 Where: λ1 and λ2 are the weight coefficients of different indicators, where λ1+λ2=1. In this paper, λ1=λ2=0.5 is taken.

4. The distribution network simulation method for high penetration distributed photovoltaic cluster control according to claim 1 is characterized in that: In step S1, the sensitivity of all voltage nodes is calculated according to the observability and controllability of each node in the cluster. The dominant node is the node with the highest comprehensive sensitivity. The comprehensive sensitivity calculation equation is: Where: represents the observability of node i, which is the voltage sensitivity of node k to node i, S G It is the set of all controllable nodes in the area; represents the controllability of node i, is the reactive voltage sensitivity, S G It is the set of all controllable nodes in the area; β is the weight coefficient, and its value is determined according to the proportion of controllability and observability of the dominant node; The calculation formulas for reactive power-voltage sensitivity and active power-voltage sensitivity are: Where: and are the active power-voltage sensitivity matrix and the reactive power-voltage sensitivity matrix respectively; G and B are the conductance and susceptance parts in the node admittance matrix respectively.

5. The distribution network simulation method for high penetration distributed photovoltaic cluster control according to claim 1 is characterized in that: In step S2, a distribution network optimization model for high penetration distributed generation is constructed as follows: (1) Objective function The optimization objective function is constructed with the lowest operating cost of the distribution network as the optimization goal, which includes the power purchase cost f from the upper power grid. pb and the network loss cost of the distribution network system loss , the optimization objective function of the distribution network can be expressed as follows: F=minf f=f ab +f loss Where: c pb 、c loss are the electricity purchase price from the distribution network to the main network and the network loss price respectively; T is the total dispatching time, which is 24 hours; Δt is the dispatching interval, which is 1 hour; P loss,t is the line active power loss; For the merits purchased from the main network; (2) Constraints Node voltage constraints: IN jmax ≤U j ≤U jmax Where: U jmax and U jmin are the upper and lower limits of the voltage amplitude at node j; Active output constraints of photovoltaic power stations: 0≤P i,PV ≤P i,PVpre Where: P i,PVpre is the predicted active output value, P i,PV is the actual active output value; Reactive power output constraints of photovoltaic power stations: Where: P i,PV is the actual active output value, Q i,PV is the actual reactive power output value, S j,PCS,max is the maximum capacity of the adjustable PV power station PCS at node j; Node power balance constraints: Where: P j,PV and Q j,PV They represent the active and reactive power injected by the PV power station to node j, P j and Q j They represent the active and reactive power injected into the grid by node j, P ij and Q ij are respectively the active and reactive power at the head end of branch ij, P j,d and Q j,d They represent the active and reactive power of the load at node j, respectively. The set u(j) is the set of the head nodes of the branch with node j as the terminal node. ij is the reactance of branch ij, U j is the output voltage amplitude of node j.

6. The distribution network simulation method for high penetration distributed photovoltaic cluster control according to claim 1 is characterized in that: In step S2, the cluster control voltage difference and reactive power adjustment amount are calculated: The actual voltage value of the dominant node is U pilot and the actual value of the boundary voltage U lim The difference between them can be expressed as ΔU pilot , the calculation formula is as follows: ΔU pilot =U pilot -U lim From the sensitivity analysis, we can know that: ΔU pilot =[S c1 ,...,S cn ]·[ΔQ c1 ,...,ΔQ cn ] T Where: ΔQ cn is the total reactive power adjustment of n clusters; S cn is the voltage sensitivity of cluster n to the dominant node; Based on the analysis of voltage sensitivity, the reactive power adjustment formula of each cluster is as follows: Where: Q is the total reactive power adjustment; The reactive power adjustment of the cluster control master station can be obtained by the above two formulas; After determining the reactive power adjustment amount, the reactive power of each distributed photovoltaic power station is redistributed according to the comprehensive sensitivity relationship between each station controller and the dominant node. The sensitivity relationship is as follows: Where: Q k is the reactive power distribution of the kth distributed photovoltaic power station of the station controller; S j is the voltage sensitivity of cluster j to the dominant node; Using the cluster-oriented dominant node voltage control method, after the reactive power of each cluster is redistributed, the simulation data of the node voltage is analyzed. If the voltage of some nodes still exceeds the limit, the optimization process is carried out again according to the optimization result of this time, and step S2 is repeated until the voltage requirements of each node are met. That is, the structural optimization of the distributed photovoltaic of the distribution system is completed, and the stability of the distribution network is improved.

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