Radial power distribution network distributed power station configuration method based on line loss minimization

Abstract

The invention relates to the technical field of power grids, in particular to a line loss minimization-based radial power distribution network distributed power station configuration method, which comprises the following steps of: S1, accessing a distributed power station into a radial power distribution network, and determining the current of the access position of the radial power distribution network; s2, establishing a power loss model of the radial power distribution network to obtain the power loss amount of the radial power distribution network; s3, establishing an optimal configuration model of the radial power distribution network of the distributed power station; and S4, optimizing by using a particle swarm algorithm, and configuring the distributed power station of the radial power distribution network. In order to reduce the power loss of the radial power distribution network and improve the voltage level, the optimal configuration model of the radial power distribution network of the distributed power station is established, and the particle swarm optimization is adopted to carry out optimal configuration on the distributed power station of the radial power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular relates to a method for configuring distributed power stations in a radial distribution network based on minimizing line losses. Background Art

[0002] Distributed renewable energy generation technologies include biomass, wind, geothermal, photovoltaic, small hydropower, etc. As a type of power generation that has rapidly emerged in recent years, it has attracted widespread attention because it can effectively improve the voltage level of the distribution network, reduce losses, and enhance the stability of the distribution network. In particular, in recent years, distributed renewable energy generation technology has developed rapidly in the distribution network due to its clean and pollution-free characteristics.

[0003] At the same time, inappropriate access location or grid-connected capacity of distributed generation will also lead to increased fault current and voltage fluctuation in the power grid. Therefore, the reasonable configuration of distributed generation becomes an important task.

[0004] The distributed generation configuration problem is a complex combinatorial optimization problem that requires optimizing multiple objective functions at the same time to minimize power loss in the power grid, increase voltage levels, and improve system reliability. Previous optimization strategies can be divided into two categories: mathematical analytical methods and heuristic algorithms. Mathematical analytical methods are highly interpretable, but they require flow calculations for each scenario, which is time-consuming and inefficient. Especially when multiple distributed power stations are involved, the computational complexity increases exponentially. Heuristic algorithms include genetic algorithms, evolutionary programming algorithms, particle swarm optimization algorithms, etc. These methods are less sensitive to the complexity of the optimization problem and can be applied to complex optimization problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for configuring distributed power stations in a radial distribution network based on minimizing line losses. The present invention aims to reduce the power loss of the radial distribution network and improve the voltage level, establishes an optimal configuration model of the radial distribution network of distributed power stations, and uses a particle swarm algorithm to optimize the configuration of the distributed power stations in the radial distribution network, thereby solving the problems of increased fault current and increased voltage fluctuation in the distribution network caused by inappropriate access locations or grid-connected capacities of distributed power stations.

[0006] To achieve the above object, the present invention discloses the following technical solution:

[0007] A method for configuring distributed power stations in a radial distribution network based on line loss minimization, comprising:

[0008] S1: Connect the distributed power station to the radial distribution network and determine the current at the access location of the radial distribution network;

[0009] Calculate the current I injected by a single distributed power station into the radial distribution networkDG , when m distributed power stations are connected, the current of branch i of the radial distribution network is for:

[0010]

[0011] in, I is the current of radial distribution network branch i after the distributed power station is connected; i I is the current of radial distribution network branch i before the distributed power station is connected; a,i The current I of radial distribution network branch i before the distributed power station is connected i The real part of I r,i The current I of radial distribution network branch i before the distributed power station is connected i The imaginary part of The current injected into the radial distribution network node k is injected into the distributed power station; The current injected into the radial distribution network node k by the distributed power station The real part of tanφ k The current injected into the radial distribution network node k by the distributed power station phase angle; m is the distributed power station number; φ is the phase angle; j is the imaginary unit; i is the radial distribution network branch number; k is the radial distribution network node number; D i,k The current parameters of branch i injected into the radial distribution network node k for the distributed power station;

[0012] S2: Establish a radial distribution network power loss model and obtain the radial distribution network power loss ΔP L for:

[0013]

[0014] Among them, ΔP L is the power loss of the radial distribution network; P L is the power loss of the radial distribution network; R i is the resistance of branch i of the radial distribution network; N is the number of buses in the radial distribution network;

[0015] S3: Establish the optimal configuration model of the radial distribution network of distributed power stations:

[0016]

[0017] Among them, minΔP L To minimize the power loss in radial distribution network;

[0018] S4: Use the particle swarm algorithm to optimize the optimal configuration model of the radial distribution network in step S3. When the power loss of the radial distribution network ΔP LWhen it is minimum, the location configuration of the distributed power station in the radial distribution network is obtained.

[0019] Preferably, in step S1, a single distributed power station injects current I into the radial distribution network. DG , specifically:

[0020] I DG =I a,DG +jI r,DG =I a,DG (1+j tanφ)

[0021] Among them, I DG Inject current into the radial distribution network; I a,DG Inject current I into the radial distribution network DG The real part of I r,DG Inject current I into the radial distribution network DG The imaginary part of .

[0022] Preferably, in step S1, the current parameter D of the branch i injected by the distributed power station into the radial distribution network node k is i,k The method to obtain is:

[0023]

[0024] Among them, L 1 is the branch of node 1; P k is the location of the distributed power station access node k.

[0025] Preferably, the radial distribution network power loss model in step S2 is specifically:

[0026] Power loss P of radial distribution network L for:

[0027]

[0028] When m distributed power stations are connected, the current in branch i of the radial distribution network Get the updated radial distribution network power loss P L for:

[0029]

[0030] Preferably, the first constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: active power and reactive power balance constraint condition, specifically:

[0031] B″δ-G′V+P G =P D

[0032] G″δ-B′V+QG =Q D

[0033] Wherein, B′ is the first block Jacobian matrix; B″ is the second block Jacobian matrix; G′ is the third block Jacobian matrix; G″ is the third block Jacobian matrix; δ is the radial distribution network node voltage phase angle matrix; V is the radial distribution network node voltage; P G is the first power generation matrix of the radial distribution network node; Q G is the second power generation matrix of the radial distribution network node; D is the first load matrix of radial distribution network nodes; Q D It is the second load matrix of radial distribution network nodes.

[0034] Preferably, the second constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: a node voltage limit constraint condition, specifically:

[0035]

[0036] in, is the voltage upper limit of the radial distribution network node k; is the voltage lower limit of the radial distribution network node k; V k is the voltage at node k of the radial distribution network.

[0037] Preferably, the third constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: a distribution capacity constraint condition, specifically:

[0038]

[0039] in, The maximum allowable carrying current of the wiring; I k Distribute current for radial distribution network.

[0040] Preferably, the fourth constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: a distributed generation capacity constraint condition, specifically:

[0041]

[0042] Among them, P DG The active power generation capacity of the distributed power station; Q DG Generate reactive capacity for distributed power stations; It is the maximum value of the active power capacity of the distributed power station; It is the maximum reactive capacity of distributed power station.

[0043] Preferably, in step S4, the optimal configuration model of the radial distribution network in step S3 is optimized by using a particle swarm algorithm, specifically:

[0044] S41: Calculate the power flow of the radial distribution network by using the forward-backward method according to the power generation and load measurement data of each node of the radial distribution network and the line impedance parameters;

[0045] S411: Set the initial voltage value of each node in the radial distribution network, and calculate the current of each node as follows:

[0046]

[0047] in, is the current vector after the radial distribution network node k is connected to the distributed power station; is the current vector before the radial distribution network node k is connected to the distributed power station; is the conjugate vector of the injected power at the node k of the radial distribution network; is the voltage conjugate vector of the radial distribution network node k;

[0048] S412: Starting from the root radial distribution network node, the voltage of each node is back-generated as:

[0049]

[0050] Among them, Z k is the branch impedance of the radial distribution network node k; is the voltage vector of node k in the radial distribution network;

[0051] S413: Determine the two iterations before and after Are the differences of all values ​​less than the set threshold ε? If not, proceed to the next iteration until it is satisfied;

[0052] S42: Based on the calculation result of the radial distribution network power flow in step S41, a particle swarm optimization algorithm is used to calculate the power loss ΔP of the radial distribution network when m distributed power stations are connected to each node in step S3. L ;

[0053] S43: When the radial distribution network power loss ΔP in step S42 L The smallest distributed power station access point combination is the optimal access point combination, and the current injected by the distributed power station into the radial distribution network node k is obtained. The real part of

[0054] S44: According to step S41, the power flow distribution of the radial distribution network after the distributed power station is connected is calculated, and the optimal capacity of the distributed power station at the node k of the radial distribution network is obtained as follows:

[0055]

[0056] Among them, S k is the optimal capacity of the distributed power station at node k in the radial distribution network.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) The present invention aims to establish a power loss variation model of the distribution network after the access of distributed renewable energy power stations based on the radial structure characteristics of the radial distribution network. At the same time, with the goal of reducing the power loss of the radial distribution network and improving the voltage level, an optimal configuration model of the radial distribution network that measures the effect of the access of distributed power stations is accurately established.

[0059] (2) The present invention uses a particle swarm algorithm to optimize the configuration of distributed power stations in a radial distribution network, thereby solving problems such as increased fault current and increased voltage fluctuation in the distribution network caused by inappropriate access locations or grid-connected capacity of distributed power stations.

[0060] (3) The present invention can simultaneously provide the optimal distributed power generation capacity and access location of the radial distribution network, and simultaneously optimize the voltage level and power loss of the distribution network system to achieve efficient operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the method for configuring distributed power stations in a radial distribution network based on line loss minimization of the present invention;

[0062] Figure 2 This is a typical radial distribution network structure diagram of the present invention;

[0063] Figure 3 A distribution network loss variation diagram of different access locations of a distributed renewable energy power station of the present invention;

[0064] Figure 4 A distribution network loss curve diagram of the distributed renewable energy power station before and after the connection of the present invention;

[0065] Figure 5 A system voltage curve diagram before and after the distributed renewable energy power station of the present invention is connected;

[0066] Figure 6 A diagram showing the variation of wiring losses at different access locations of a distributed renewable energy power station of the present invention;

[0067] Figure 7 This is a system voltage curve diagram before and after the distributed renewable energy power station of the present invention is connected. DETAILED DESCRIPTION

[0068] The exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0069] The embodiment of the present invention takes the access of renewable energy power stations to radial distribution networks as an example, and provides a method for configuring distributed power stations in radial distribution networks based on line loss minimization. Figure 1 As shown, the distributed power station is connected to the radial distribution network, and the current at the access position of the radial distribution network is determined; a radial distribution network power loss model is established to obtain the power loss of the radial distribution network; an optimal configuration model of the radial distribution network of the distributed power station is established; and a particle swarm algorithm is used for optimization to configure the distributed power station of the radial distribution network; which includes:

[0070] Step S1: Connect the distributed power station to the radial distribution network and determine the current at the connection location of the radial distribution network.

[0071] Taking a typical radial distribution network as an example, its primary structure diagram is as follows: Figure 2 As shown, I k is the branch current of the radial distribution network; I L,k is the load current of the radial distribution network node. When a distributed renewable energy power station is connected to any node such as node k, it will inject current I into the grid. DG , which leads to corresponding changes in the current distribution in the distribution network.

[0072] Calculate the current I injected by a single distributed power station into the radial distribution network DG for:

[0073] I DG =I a,DG +jI r,DG =I a,DG (1+j tanφ);

[0074] Among them, I DG Inject current into the radial distribution network; I a,DG Inject current I into the radial distribution network DG The real part of I r,DG Inject current I into the radial distribution network DG The imaginary part of φ; φ is the phase angle; j is the imaginary unit.

[0075] When a distributed power station is connected, the current in the radial distribution network branch i for:

[0076]

[0077] in, I is the current of radial distribution network branch i after the distributed power station is connected; i D is the current of radial distribution network branch i before the distributed power station is connected; i The current parameter of branch i of the radial distribution network injected into the distributed power station; I a,i The current I of radial distribution network branch i before the distributed power station is connected i The real part of I r,i The current I of radial distribution network branch i before the distributed power station is connected i The imaginary part of .

[0078] The current parameter D of the branch i injected by the distributed power station into the radial distribution network i for:

[0079]

[0080] Among them, L 1 is the branch of node 1 of the radial distribution network; L k is the branch of the radial distribution network node k; i is the distribution network branch number; k is the distribution network node number; i is the radial distribution network branch number.

[0081] When m distributed power stations are connected, the current in branch i of the radial distribution network Updated to:

[0082]

[0083] in, The current injected into the radial distribution network node k is injected into the distributed power station; The current injected into the radial distribution network node k by the distributed power station The real part of tanφ k The current injected into the radial distribution network node k by the distributed power station The phase angle; m is the distributed power station number; k is the radial distribution network node number; D i,k The current parameters of branch i of radial distribution network node k are injected into the distributed power station.

[0084] The current parameter D of the branch i injected by the distributed power station into the radial distribution network node k i,k The method to obtain is:

[0085]

[0086] Among them, L 1 is the branch of node 1; P k is the location of the distributed power station access node k.

[0087] Step S2: Establish a radial distribution network power loss model, specifically:

[0088] Power loss P of radial distribution network L for:

[0089]

[0090] Among them, P L is the power loss of the radial distribution network; R i is the resistance of branch i of the radial distribution network; N is the number of buses in the radial distribution network.

[0091] When m distributed power stations are connected, the current in branch i of the radial distribution network Get the updated radial distribution network power loss P L for:

[0092]

[0093] Get the power loss of the radial distribution network ΔP L for:

[0094]

[0095] Among them, ΔP L is the power loss of the radial distribution network.

[0096] When a single distributed renewable energy power station is connected to the distribution network, the line loss caused by the connected distributed renewable energy power station at different locations is as follows: Figure 3 The figure shows the distribution network loss variation diagram of the distributed renewable energy power station at different access locations of the present invention. The node with the largest line loss variation is node 6, so the optimal access point of the distributed renewable energy power station is node 6. The line loss level under different access capacities is shown in FIG. Figure 4 The figure shows the distribution network loss curve before and after the distributed renewable energy power station of the present invention is connected, and it can be determined that the optimal access capacity of the distributed renewable energy power station is 2.49MW.

[0097] Step S3: Establish the optimal configuration model of the radial distribution network of the distributed power station as follows:

[0098]

[0099] Among them, minΔP L To minimize the power loss in the radial distribution network.

[0100] The first constraint of the optimal configuration model of the radial distribution network of distributed power stations is the active power and reactive power balance constraint, specifically:

[0101]

[0102] Wherein, B′ is the first block Jacobian matrix; B″ is the second block Jacobian matrix; G′ is the third block Jacobian matrix; G″ is the third block Jacobian matrix; δ is the radial distribution network node voltage phase angle matrix; V is the radial distribution network node voltage; P G is the first power generation matrix of the radial distribution network node; Q G is the second power generation matrix of the radial distribution network node; D is the first load matrix of radial distribution network nodes; Q D It is the second load matrix of radial distribution network nodes.

[0103] The second constraint condition of the optimal configuration model of the radial distribution network of distributed power stations is: node voltage limit constraint condition, specifically:

[0104]

[0105] in, is the voltage upper limit of the radial distribution network node k; is the voltage lower limit of the radial distribution network node k; V k is the voltage at node k of the radial distribution network.

[0106] The third constraint of the optimal configuration model of the radial distribution network of distributed power stations is: the distribution capacity constraint, specifically:

[0107]

[0108] in, The maximum allowable carrying current of the wiring; I k Distribute current for radial distribution network.

[0109] The fourth constraint of the optimal configuration model of the radial distribution network of distributed power stations is: the distributed generation capacity constraint, specifically:

[0110]

[0111] Among them, P DG The active power generation capacity of the distributed power station; Q DG Generate reactive capacity for distributed power stations; It is the maximum value of the active power capacity of the distributed power station; It is the maximum reactive capacity of distributed power station.

[0112] Step S4: Using the particle swarm algorithm to optimize the optimal configuration model of the radial distribution network in step S3, specifically:

[0113] Step S41: Calculate the radial distribution network power flow using the forward-backward method according to the power generation and load measurement data of each node in the radial distribution network and the line impedance parameters.

[0114] Step S411: Set the initial voltage value of each node in the radial distribution network, and calculate the current of each node as follows:

[0115]

[0116] in, is the current vector after the radial distribution network node k is connected to the distributed power station; is the current vector before the radial distribution network node k is connected to the distributed power station; is the conjugate vector of the injected power at the node k of the radial distribution network; is the voltage conjugate vector of node k in the radial distribution network.

[0117] Step S412: Starting from the root radial distribution network node, the voltage of each node is back-converted to:

[0118]

[0119] Among them, Z k is the branch impedance of the radial distribution network node k; is the voltage vector of node k in the radial distribution network.

[0120] Step S413: Determine the If the difference is not satisfied, the next iteration is performed until it is satisfied.

[0121] Step S42: Based on the radial distribution network power flow calculation result in step S41, the particle swarm optimization algorithm is used to calculate the radial distribution network power loss ΔP when m distributed power stations are connected to each node in step S3. L .

[0122] Step S43: When the radial distribution network power loss ΔP in step S42 L The smallest distributed power station access point combination is the optimal access point combination, and the current injected by the distributed power station into the radial distribution network node k is obtained. The real part of

[0123] Step S44: According to step S41, the power flow distribution of the radial distribution network after the distributed power station is connected is calculated, and the optimal capacity of the distributed power station at the node k of the radial distribution network is obtained as:

[0124]

[0125] Among them, Sk is the optimal capacity of the distributed power station at node k in the radial distribution network.

[0126] According to the above steps, determine the power loss ΔP of the radial distribution network L When it is minimum, the location configuration of the distributed power station in the radial distribution network is obtained.

[0127] Analyze the voltage changes after the distributed renewable energy power station is connected, such as Figure 5 The figure shows the system voltage curve before and after the distributed renewable energy power station of the present invention is connected. After the distributed renewable energy power station is connected, the bus voltage level of each node is also significantly improved.

[0128] When two distributed renewable energy power stations are connected, the change in line loss caused by the connected distributed renewable energy power stations at different locations is as follows: Figure 6 The figure shows the distribution loss variation diagram of the distributed renewable energy power station of the present invention at different access locations. When the distributed renewable energy power station is connected to the bus (13, 30), the distribution network loss changes the most. Therefore, the optimal access point is the bus (13, 30), and the optimal access capacity is 0.82 and 1.13MW.

[0129] Analyze the voltage changes after the distributed renewable energy power station is connected, such as Figure 7 The figure shows the system voltage curve before and after the distributed renewable energy power station of the present invention is connected. After the distributed renewable energy power station is connected, the bus voltage level of each node is also significantly improved, and compared with only connecting one distributed renewable energy power station, the system voltage level is more significantly improved.

[0130] The beneficial effects of the present invention are as follows: the present invention provides a method for configuring distributed power stations in a radial distribution network based on minimizing line losses, and according to the radial structural characteristics of the radial distribution network, provides the optimal power generation capacity and access position of the distributed renewable energy power station, and aims to reduce the power loss of the radial distribution network and improve the voltage level, and accurately establishes an optimal configuration model of the radial distribution network for measuring the access effect of the distributed power station; and adopts a particle swarm algorithm to optimize the configuration of the distributed power stations of the radial distribution network, and solves the problems of increased fault current and increased voltage fluctuation in the distribution network caused by inappropriate access position or grid-connected capacity of the distributed power station.

[0131] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for configuring distributed power stations in a radial distribution network based on line loss minimization, characterized in that: It includes: S1: Connect the distributed power station to the radial distribution network and determine the current at the access location of the radial distribution network; Calculate the current I injected by a single distributed power station into the radial distribution network DG , when m distributed power stations are connected, the current of branch i of the radial distribution network is for: in, I is the current of radial distribution network branch i after the distributed power station is connected; i I is the current of radial distribution network branch i before the distributed power station is connected; a,i The current I of radial distribution network branch i before the distributed power station is connected i The real part of I r,i The current I of radial distribution network branch i before the distributed power station is connected i The imaginary part of The current injected into the radial distribution network node k is injected into the distributed power station; The current injected into the radial distribution network node k by the distributed power station The real part of tanφ k The current injected into the radial distribution network node k by the distributed power station phase angle; m is the distributed power station number; φ is the phase angle; j is the imaginary unit; i is the radial distribution network branch number; k is the radial distribution network node number; D i,k The current parameters of branch i injected into the radial distribution network node k for the distributed power station; S2: Establish a radial distribution network power loss model and obtain the radial distribution network power loss ΔP L for: Among them, ΔP L is the power loss of the radial distribution network; P L is the power loss of the radial distribution network; R i is the resistance of branch i of the radial distribution network; N is the number of buses in the radial distribution network; S3: Establish the optimal configuration model of the radial distribution network of distributed power stations: Among them, minΔP L To minimize the power loss in radial distribution network; S4: Use the particle swarm algorithm to optimize the optimal configuration model of the radial distribution network in step S3. When the power loss of the radial distribution network ΔP L When it is minimum, the location configuration of the distributed power station in the radial distribution network is obtained.

2. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1 is characterized in that: In step S1, a single distributed power station injects current I into the radial distribution network. DG , specifically: I DG =I a,DG +jI r,DG =I a,DG (1+jtanφ) Among them, I DG Inject current into the radial distribution network; I a,DG Inject current I into the radial distribution network DG The real part of I r,DG Inject current I into the radial distribution network DG The imaginary part of .

3. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1 is characterized in that: In step S1, the distributed power station injects the branch i current parameter D into the radial distribution network node k. i,k The method to obtain is: Among them, L1 is the branch of node 1; P k is the location of the distributed power station access node k.

4. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: The power loss model of the radial distribution network in step S2 is specifically: Power loss P of radial distribution network L for: When m distributed power stations are connected, the current in branch i of the radial distribution network Get the updated radial distribution network power loss P L for:

5. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: The first constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: active power and reactive power balance constraint condition, specifically: B′′δ-G′V+P G JP D G″δ-B′V+Q G =Q D Wherein, B′ is the first block Jacobian matrix; B″ is the second block Jacobian matrix; G′ is the third block Jacobian matrix; G″ is the third block Jacobian matrix; δ is the radial distribution network node voltage phase angle matrix; V is the radial distribution network node voltage; P G is the first power generation matrix of the radial distribution network node; Q G is the second power generation matrix of the radial distribution network node; D is the first load matrix of radial distribution network nodes; Q D It is the second load matrix of radial distribution network nodes.

6. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: The second constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: node voltage limit constraint condition, specifically: in, is the voltage upper limit of the radial distribution network node k; is the voltage lower limit of the radial distribution network node k; V k is the voltage at node k of the radial distribution network.

7. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: The third constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: the distribution capacity constraint condition, specifically: in, The maximum allowable carrying current of the wiring; I k Distribute current for radial distribution network.

8. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: The fourth constraint condition of the optimal configuration model of the radial distribution network of the distributed power station in step S3 is: the distributed generation capacity constraint condition, specifically: Among them, P DG The active power generation capacity of the distributed power station; Q DG Generate reactive capacity for distributed power stations; It is the maximum value of the active power capacity of the distributed power station; It is the maximum reactive capacity of distributed power station.

9. The method for configuring distributed power stations in a radial distribution network based on line loss minimization according to claim 1, characterized in that: In step S4, the particle swarm algorithm is used to optimize the optimal configuration model of the radial distribution network in step S3, specifically: S41: Calculate the power flow of the radial distribution network by using the forward-backward method according to the power generation and load measurement data of each node of the radial distribution network and the line impedance parameters; S411: Set the initial voltage value of each node in the radial distribution network, and calculate the current of each node as follows: in, is the current vector after the radial distribution network node k is connected to the distributed power station; is the current vector before the radial distribution network node k is connected to the distributed power station; is the conjugate vector of the injected power at the node k of the radial distribution network; is the voltage conjugate vector of the radial distribution network node k; S412: Starting from the root radial distribution network node, the voltage of each node is back-generated as: Among them, Z k is the branch impedance of the radial distribution network node k; is the voltage vector of node k in the radial distribution network; S413: Determine the two iterations before and after Are the differences of all values ​​less than the set threshold ε? If not, proceed to the next iteration until it is satisfied; S42: Based on the calculation result of the radial distribution network power flow in step S41, a particle swarm optimization algorithm is used to calculate the power loss ΔP of the radial distribution network when m distributed power stations are connected to each node in step S3. L ; S43: When the radial distribution network power loss ΔP in step S42 L The smallest distributed power station access point combination is the optimal access point combination, and the current injected by the distributed power station into the radial distribution network node k is obtained. The real part of S44: According to step S41, the power flow distribution of the radial distribution network after the distributed power station is connected is calculated, and the optimal capacity of the distributed power station at the node k of the radial distribution network is obtained as follows: Among them, S k is the optimal capacity of the distributed power station at node k in the radial distribution network.

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