A method for optimizing the configuration of distributed power sources in a distribution network and a computer-readable medium

Abstract

The present invention proposes a method for optimizing the configuration of distributed power sources in a distribution network and a computer-readable medium to reduce the energy consumption of a distribution network containing distributed power sources and improve voltage quality. First, a distribution network model containing multiple distributed power sources is initialized and constructed; secondly, a multi-objective optimization model of the distribution network is constructed by minimizing active power loss, minimizing voltage deviation, and maximizing voltage stability, and corresponding equality constraints and inequality constraints are designed; then, an improved transient search optimization algorithm is used to solve the multi-objective optimization model of the distribution network, and the global search and local optimization process of the balancing algorithm are performed by setting the adjustment coefficient and step coefficient; finally, the global optimal solution is obtained to achieve the optimal configuration of the distributed power sources in the distribution network model. The advantages of the present invention are its flexible optimization mechanism and simple control method. The improved transient search optimization algorithm can take into account both search accuracy and search speed, and has advantages in the application of optimal configuration of distributed power sources in distribution networks.

Description

Technical Field

[0001] The present invention belongs to the field of optimized configuration of distributed power sources in a distribution network, and in particular relates to a method for optimized configuration of distributed power sources in a distribution network and a computer-readable medium. Technical Background

[0002] With the advancement of industrialization, electricity demand is increasing worldwide, and a large number of power plants have been put into operation. However, the transmission of electricity generated by power plants to the user end requires a very long transportation process, and a large amount of electricity is lost in this process, resulting in significant economic losses. Compared with the transmission network, the distribution network suffers from more severe energy losses, which also leads to voltage deviations and reduced stability in the distribution network. Integrating distributed generation (DG) into the distribution network system can effectively reduce energy losses and improve the voltage distribution of the distribution network. However, irrational DG allocation can lead to greater energy losses, voltage deviations, and reduced grid stability. Therefore, it is necessary to optimize the configuration of DG to reduce energy losses, improve the voltage distribution of the distribution network, and enhance the stability of the distribution network.

[0003] Experts and scholars have proposed a variety of optimization methods for DG configuration, but few meta-heuristic optimization algorithms based on physical phenomena have been applied to DG configuration optimization. This paper, inspired by the transient response of power storage components, proposes an improved search optimization algorithm. Compared with other optimization methods, this method requires fewer parameters to be tuned and has faster convergence and higher search accuracy. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method for optimizing the configuration of distributed power sources in a distribution network and a computer-readable medium.

[0005] The technical solution adopted by the present invention is a remote sensing image fusion method taking into account image distortion, comprising the following steps:

[0006] Step 1: Input the line load data of the distribution network, build a distribution network model, and select multiple nodes in the distribution network model to incorporate distributed power sources;

[0007] Step 2: Calculate the active power loss, voltage deviation, and voltage stability coefficient of the distribution network model, and construct a multi-objective optimization model for the distribution network by minimizing the active power loss, voltage deviation, and voltage stability of the distribution network model.

[0008] Step 3: Construct the power balance constraint of the distribution network model, the node voltage constraint of the distribution network model, the branch current constraint of the distribution network model, and the distributed generation capacity constraint of the distribution network model respectively;

[0009] Step 4: Use the improved transient search optimization algorithm to solve the distribution network multi-objective optimization model, set the initial parameters of the improved transient search optimization algorithm, and construct a fitness function based on the active power loss of the distribution network model, the voltage deviation of the distribution network model, and the voltage stability coefficient of the distribution network model. Select the power factor of each distributed generation in the distribution network model as the decision variable of the optimization algorithm;

[0010] Step 5: Use the position vector element of each particle at the time of initialization iteration as the initial power factor of each distributed power source in the distribution network model, perform power flow calculation on the distribution network model, obtain the active power loss, voltage deviation and voltage stability coefficient of the distribution network model, update the position vector of each particle, calculate the fitness function value corresponding to the position vector of each particle based on the active power loss, voltage deviation and voltage stability coefficient obtained from the power flow calculation, and update the local optimal solution and global optimal solution of each particle within the constraint conditions;

[0011] Step 6: Repeat step 5 until the maximum number of iterations is reached to obtain the global optimal solution. According to the global optimal solution, the optimal power factor of each distributed power source in the distribution network model is obtained. Each distributed power source in the distribution network model operates at the corresponding optimal power factor, thereby achieving the optimal configuration of the distributed power sources in the distribution network model.

[0012] Preferably, the active power loss calculation formula of the distribution network model in step 2 is:

[0013]

[0014] Among them, P l,total is the total active power loss of the distribution network model, N is the total number of nodes in the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, R i,j is the resistance between the i-th node in the distribution network model and the j-th node in the distribution network model;

[0015] The calculation formula for the voltage deviation of the distribution network model in step 2 is:

[0016]

[0017] Among them, V d,total is the cumulative voltage deviation of the distribution network model, V i is the voltage of the ith node in the distribution network model, V b is the reference voltage of the node in the distribution network model, and N is the total number of nodes in the distribution network model;

[0018] The calculation formula for the voltage stability coefficient of the branch from node i to node j in the distribution network model described in step 2 is:

[0019]

[0020] Among them, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model, V i is the voltage of the ith node in the distribution network model, P j is the active power of node j, Q j is the reactive power of node j, R i,j is the resistance between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node and the j-th node in the distribution network model;

[0021] The voltage stability coefficient of the distribution network model described in step 2 is defined as:

[0022]

[0023] Among them, C is the voltage stability coefficient of the distribution network model, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model;

[0024] The multi-objective optimization model of the distribution network described in step 2 is:

[0025]

[0026] α1+α2+α3=1

[0027] Among them, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the weight coefficient of the active power loss of the distribution network, α2 is the weight coefficient of the voltage deviation of the distribution network, and α3 is the weight coefficient of the voltage stability of the distribution network;

[0028] Preferably, the power balance constraint of the distribution network model in step 3 is:

[0029]

[0030]

[0031] Among them, M is the total number of distributed power sources in the distribution network model, N is the total number of nodes in the distribution network model, and P l,total is the total active power loss of the distribution network model, P DG,r is the active power generated by the rth distributed generation in the distribution network model, P LD,iis the active load of the ith node in the distribution network model, Q DG,r is the reactive power generated by the rth distributed generation in the distribution network model, Q LD,i is the reactive load of the ith node in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, which is calculated as follows:

[0032]

[0033] Where N is the total number of nodes in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node in the distribution network model and the j-th node in the distribution network model;

[0034] The node voltage constraints of the distribution network model in step 3 are:

[0035] V min ≤V i ≤V max

[0036] Among them, V i is the voltage of the ith node in the distribution network model, V min is the minimum value of the node voltage in the distribution network model, V max is the maximum value of the node voltage of the distribution network model;

[0037] The branch current constraint of the distribution network model in step 3 is:

[0038] |I i,j |≤I max

[0039] Among them, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, I max The current flowing through the branch when the heating limit that the line can withstand is reached;

[0040] The distributed power capacity constraint of the distribution network model in step 3 is:

[0041] P DG,min ≤P DG ≤P DG,max

[0042] Q DG,min ≤Q DG ≤Q DG,max

[0043]

[0044] Among them, P DG is the active power of distributed generation in the distribution network, P DG,min is the minimum value of the active power of the distributed generation, P DG,max is the maximum value of the active power of the distributed generation, Q DG is the reactive power of distributed generation in the distribution network, Q DG,min is the minimum reactive power of distributed generation, Q DG,max is the maximum value of the reactive power of the distributed generation, is the power factor of the distributed generation in the distribution network, is the minimum value of the distributed power factor, is the maximum value of the distributed power factor;

[0045] As an example, the initial parameters of the improved transient search optimization algorithm are set in step 4 as follows:

[0046] Initialize the definition of particle search space dimension as M, the number of search particles as D, and the maximum number of iterations as k max , active power loss weight coefficient is α1, voltage deviation weight coefficient is α2, voltage stability weight coefficient is α3, algorithm adjustment coefficient is ξ, and algorithm step coefficient is λ;

[0047] Initialization defines the position vector of each particle at the initialization iteration as:

[0048]

[0049] in, is the position vector of particle i at the time of initialization iteration, that is, the position vector when the iteration number k = 0, which corresponds to the initial power factor of each distributed power source in the distribution network model. is the jth element of the position vector of particle i at the time of initialization iteration, which corresponds to the initial power factor of the jth distributed power source in the distribution network model. M is the dimension of the particle search space, which corresponds to the number of distributed power sources in the distribution network model.

[0050] Initialization defines the elements of the position vector of each particle at the initialization iteration as:

[0051]

[0052] in, is the jth element of the position vector of particle i at the initialization iteration, M is the dimension of the particle search space, is the rated power factor of the distributed power generator, rand(0,1) is a random variable in the range of (0,1), is the maximum value of the distributed power factor, is the minimum value of the distributed power factor;

[0053] The fitness function constructed in step 4 is:

[0054]

[0055] Among them, f is the fitness function constructed, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the weight coefficient of the active power loss of the distribution network, α2 is the weight coefficient of the voltage deviation of the distribution network, and α3 is the weight coefficient of the voltage stability of the distribution network;

[0056] The adjustment coefficient ξ described in step 4 is used to adjust the search mode of the particle. Its positive and negative values change randomly with each iteration, thereby achieving local optimization and global optimization in parallel. Its update method is as follows:

[0057]

[0058] Among them, μ1 is a random quantity in the range of [0,1], and μ1 is revalued in each iteration. k is the number of iterations. max is the maximum number of iterations;

[0059] The step size coefficient λ mentioned in step 4 is used to adjust the search step size of the particle. Its value gradually decreases with the increase of the number of iterations K, thereby adjusting the search step size to take into account both search speed and search accuracy. Its update method is as follows:

[0060]

[0061] Among them, μ2 is a random quantity in the range of [0,1], and μ2 is revalued in each iteration. c is a positive integer, k is the number of iterations, and k max is the maximum number of iterations;

[0062] As an example, the position vector of each particle in the improved transient search optimization algorithm described in step 5 is updated as follows:

[0063]

[0064] in, is the position vector of particle i after the kth iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, ξ is the adjustment coefficient, λ is the step coefficient, and e is a natural constant;

[0065] because:

[0066]

[0067] Among them, μ1 is a random quantity in the range of [0,1], k is the number of iterations, k max is the maximum number of iterations. The random variable μ1 will be randomly revalued in the range of [0,1] in each iteration. When μ1>0.5, that is, ξ>0, the algorithm executes the local development mode; when μ1<0, that is, ξ<0, the algorithm executes the global exploration optimization mode.

[0068] Compare the fitness function value f corresponding to the updated position vector of each particle i k+1 And the fitness function value f of the current local optimal solution vector of each particle i k* , update the local optimal solution vector of each particle:

[0069]

[0070] in, is the local optimal position vector of particle i after the k+1th iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, f i k+1 is the fitness function value of the position vector of particle i after k+1 iterations, f i k* is the fitness function value of the local optimal solution vector of particle i after the kth iteration;

[0071] The global optimal solution is updated by selecting the local optimal solution, and its value is as follows:

[0072]

[0073] in, is the global optimal solution vector after the k+1th iteration, is the jth element of the global optimal solution vector after the k+1th iteration, g is the number of the particle that minimizes the fitness function value after the k+1th iteration, and f i k+1* is the fitness function value of the local optimal solution vector of the i-th particle after k+1 iterations, and D is the number of particles.

[0074] Preferably, the global optimal solution in step 6 is:

[0075] X g =[X g,1 ,Xg,2 ,...X g,j ,...X g,M ]

[0076] j=1,2,...,M

[0077] Among them, X g is the global optimal solution, that is, the optimal power factor of each distributed power source in the distribution network model, X g,j is the jth element of the global optimal solution vector, which corresponds to the optimal solution of the jth distributed generation in the distribution network model. M is the dimension of the particle search space, which corresponds to the total number of distributed generation in the distribution network.

[0078] The global optimal solution obtained according to the optimization algorithm in step 6 is the optimal power factor of each distributed power source, which corresponds to the following:

[0079]

[0080] in, is the optimal power factor of the jth distributed generation in the distribution network model, X g,j is the jth element of the global optimal solution vector, M is the dimension of the particle search space, which corresponds to the number of distributed generation in the distribution network model;

[0081] Each distributed power source in the distribution network operates at the optimal power factor, achieving optimal configuration of the distributed power source in the distribution network, ensuring the minimum active power loss, minimum voltage deviation and maximum voltage stability of the distribution network.

[0082] The present invention also provides a computer-readable medium, which stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the distribution network distributed power supply optimization configuration method.

[0083] The advantages of the present invention lie in its flexible optimization mechanism and simple control method, which enables the parallel execution of local optimization and global optimization, and can stably obtain the global optimal solution while ensuring that the algorithm has a faster convergence speed. It has superiority in the application of optimizing the configuration of distributed power sources in distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 : A flow chart of a method according to an embodiment of the present invention;

[0085] Figure 2 : Flowchart of the improved transient search optimization algorithm of an embodiment of the present invention. DETAILED DESCRIPTION

[0086] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0087] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0088] The following combination Figure 1-2 The technical solution of the embodiment of the present invention is a method for optimizing the configuration of distributed power sources in a distribution network, which is specifically as follows:

[0089] Step 1: Input the line load data of the distribution network, build a distribution network model, and select multiple nodes in the distribution network model to incorporate distributed power sources;

[0090] Step 2: Calculate the active power loss, voltage deviation, and voltage stability coefficient of the distribution network model, and construct a multi-objective optimization model for the distribution network by minimizing the active power loss, voltage deviation, and voltage stability of the distribution network model.

[0091] The calculation formula for the active power loss of the distribution network model in step 2 is:

[0092]

[0093] Among them, P l,total is the total active power loss of the distribution network model, N=69 is the total number of nodes in the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, R i,j is the resistance between the i-th node in the distribution network model and the j-th node in the distribution network model;

[0094] The calculation formula for the voltage deviation of the distribution network model in step 2 is:

[0095]

[0096] Among them, V d,total is the cumulative voltage deviation of the distribution network model, V i is the voltage of the ith node in the distribution network model, V bis the reference voltage of the node in the distribution network model, N=69 is the total number of nodes in the distribution network model;

[0097] The calculation formula for the voltage stability coefficient of the branch from node i to node j in the distribution network model described in step 2 is:

[0098]

[0099] Among them, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model, V i is the voltage of the ith node in the distribution network model, P j is the active power of node j, Q j is the reactive power of node j, R i,j is the resistance between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node and the j-th node in the distribution network model;

[0100] The voltage stability coefficient of the distribution network model described in step 2 is defined as:

[0101] C=maxC i,j

[0102] i=1,2,...,N

[0103] j=1,2,...,N

[0104] i≠j

[0105] Among them, C is the voltage stability coefficient of the distribution network model, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model;

[0106] The multi-objective optimization model of the distribution network described in step 2 is:

[0107]

[0108] α1+α2+α3=1

[0109] Among them, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the weight coefficient of the active power loss of the distribution network, α2 is the weight coefficient of the voltage deviation of the distribution network, and α3 is the weight coefficient of the voltage stability of the distribution network;

[0110] Step 3: Construct the power balance constraint of the distribution network model, the node voltage constraint of the distribution network model, the branch current constraint of the distribution network model, and the distributed generation capacity constraint of the distribution network model respectively;

[0111] The power balance constraint of the distribution network model in step 3 is:

[0112]

[0113]

[0114] Among them, M = 10 is the total number of distributed power sources in the distribution network model, N = 69 is the total number of nodes in the distribution network model, and P l,total is the total active power loss of the distribution network model, P DG,r is the active power generated by the rth distributed generation in the distribution network model, P LD,i is the active load of the ith node in the distribution network model, Q DG,r is the reactive power generated by the rth distributed generation in the distribution network model, Q LD,i is the reactive load of the ith node in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, which is calculated as follows:

[0115]

[0116] Among them, N=69 is the total number of nodes in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node in the distribution network model and the j-th node in the distribution network model;

[0117] The node voltage constraints of the distribution network model in step 3 are:

[0118] V min ≤V i ≤V max

[0119] Among them, V i is the voltage of the ith node in the distribution network model, V min is the minimum value of the node voltage in the distribution network model, V max is the maximum value of the node voltage of the distribution network model;

[0120] The branch current constraint of the distribution network model in step 3 is:

[0121] |I i,j |≤I max

[0122] Among them, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, I max The current flowing through the branch when the heating limit that the line can withstand is reached;

[0123] The distributed power capacity constraint of the distribution network model in step 3 is:

[0124] P DG,min ≤P DG ≤P DG,max

[0125] Q DG,min ≤Q DG ≤Q DG,max

[0126]

[0127] Among them, P DG is the active power of distributed generation in the distribution network, P DG,min is the minimum value of the active power of the distributed generation, P DG,max is the maximum value of the active power of the distributed generation, Q DG is the reactive power of distributed generation in the distribution network, Q DG,min is the minimum reactive power of distributed generation, Q DG,max is the maximum value of the reactive power of the distributed generation, is the power factor of the distributed generation in the distribution network, is the minimum value of the distributed power factor, is the maximum value of the distributed power factor;

[0128] Step 4: Use the improved transient search optimization algorithm to solve the distribution network multi-objective optimization model, set the initial parameters of the improved transient search optimization algorithm, and build a fitness function based on the active power loss of the distribution network model, the voltage deviation of the distribution network model, and the voltage stability coefficient of the distribution network model. Select the power factor of each distributed power source in the distribution network model as the decision variable of the optimization algorithm, such as Figure 2 As shown;

[0129] Step 4 sets the initial parameters of the improved transient search optimization algorithm as follows:

[0130] Initialize the particle search space dimension to be M=10, the number of search particles to be D=100, and the maximum number of iterations to be k. max =500, active power loss weight coefficient is α1=0.4, voltage deviation weight coefficient is α2=0.3, voltage stability weight coefficient is α3=0.3, algorithm adjustment coefficient is ξ, and algorithm step size coefficient is λ;

[0131] Initialization defines the position vector of each particle at the initialization iteration as:

[0132]

[0133] in, is the position vector of particle i at the time of initialization iteration, that is, the position vector when the iteration number k = 0, which corresponds to the initial power factor of each distributed power source in the distribution network model. is the jth element of the position vector of particle i at the time of initialization iteration, which corresponds to the initial power factor of the jth distributed power source in the distribution network model. M = 10 is the dimension of the particle search space, which corresponds to the number of distributed power sources in the distribution network model.

[0134] Initialization defines the elements of the position vector of each particle at the initialization iteration as:

[0135]

[0136] in, is the jth element of the position vector of particle i at the time of initialization iteration, M=10 is the dimension of particle search space, is the rated power factor of the distributed power generator, rand(0,1) is a random variable in the range of (0,1), is the maximum value of the distributed power factor, is the minimum value of the distributed power factor;

[0137] The fitness function constructed in step 4 is:

[0138]

[0139] Among them, f is the fitness function constructed, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the weight coefficient of the active power loss of the distribution network, α2 is the weight coefficient of the voltage deviation of the distribution network, and α3 is the weight coefficient of the voltage stability of the distribution network;

[0140] The adjustment coefficient ξ described in step 4 is used to adjust the search mode of the particle. Its positive and negative values change randomly with each iteration, thereby achieving local optimization and global optimization in parallel. Its update method is as follows:

[0141]

[0142] Among them, μ1 is a random quantity in the range of [0,1], and μ1 is revalued in each iteration. k is the number of iterations. max is the maximum number of iterations;

[0143] The step size coefficient λ mentioned in step 4 is used to adjust the search step size of the particle. Its value gradually decreases with the increase of the number of iterations K, thereby adjusting the search step size to take into account both search speed and search accuracy. Its update method is as follows:

[0144]

[0145] Among them, μ2 is a random quantity in the range of [0,1], and μ2 is revalued in each iteration. c is a positive integer, k is the number of iterations, and k max is the maximum number of iterations;

[0146] Step 5: Use the position vector element of each particle at the time of initialization iteration as the initial power factor of each distributed power source in the distribution network model, perform power flow calculation on the distribution network model, obtain the active power loss, voltage deviation and voltage stability coefficient of the distribution network model, update the position vector of each particle, calculate the fitness function value corresponding to the position vector of each particle based on the active power loss, voltage deviation and voltage stability coefficient obtained from the power flow calculation, and update the local optimal solution and global optimal solution of each particle within the constraint conditions;

[0147] The method for updating the position vector of each particle in the improved transient search optimization algorithm described in step 5 is as follows:

[0148]

[0149] in, is the position vector of particle i after the kth iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, ξ is the adjustment coefficient, λ is the step coefficient, and e is a natural constant;

[0150] because:

[0151]

[0152] Among them, μ1 is a random quantity in the range of [0,1], k is the number of iterations, k max is the maximum number of iterations. The random variable μ1 will be randomly revalued in the range of [0,1] in each iteration. When μ1>0.5, that is, ξ>0, the algorithm executes the local development mode; when μ1<0, that is, ξ<0, the algorithm executes the global exploration optimization mode.

[0153] Compare the fitness function value f corresponding to the updated position vector of each particle ik+1 And the fitness function value f of the current local optimal solution vector of each particle i k* , update the local optimal solution vector of each particle:

[0154]

[0155] in, is the local optimal position vector of particle i after the k+1th iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, f i k+1 is the fitness function value of the position vector of particle i after k+1 iterations, f i k* is the fitness function value of the local optimal solution vector of particle i after the kth iteration;

[0156] The global optimal solution is updated by selecting the local optimal solution, and its value is as follows:

[0157]

[0158] in, is the global optimal solution vector after the k+1th iteration, is the jth element of the global optimal solution vector after the k+1th iteration, g is the number of the particle that minimizes the fitness function value after the k+1th iteration, and f i k+1* is the fitness function value of the local optimal solution vector after k+1 iterations of the i-th particle, M=10 is the dimension of the particle search space, which corresponds to the total number of distributed power sources in the distribution network, and D=100 is the number of particles.

[0159] Step 6: Repeat step 5 until the maximum number of iterations is reached to obtain the global optimal solution. According to the global optimal solution, the optimal power factor of each distributed power source in the distribution network model is obtained. Each distributed power source in the distribution network model operates at the corresponding optimal power factor, thereby achieving the optimal configuration of the distributed power sources in the distribution network model.

[0160] The global optimal solution in step 6 is:

[0161] X g =[X g,1 ,X g,2 ,...X g,j ,...X g,M ]

[0162] j=1,2,...,M

[0163] Among them, Xg is the global optimal solution, that is, the optimal power factor of each distributed power source in the distribution network model, X g,j is the jth element of the global optimal solution vector, which corresponds to the optimal solution of the jth distributed generation in the distribution network model. M = 10 is the dimension of the particle search space, which corresponds to the total number of distributed generation in the distribution network.

[0164] The global optimal solution obtained according to the optimization algorithm in step 6 is the optimal power factor of each distributed power source, which corresponds to the following:

[0165]

[0166] in, is the optimal power factor of the jth distributed generation in the distribution network model, X g,j is the jth element of the global optimal solution vector, M = 10 is the dimension of the particle search space, which corresponds to the number of distributed generation in the distribution network model;

[0167] Each distributed power source in the distribution network operates at the optimal power factor, achieving optimal configuration of the distributed power source in the distribution network, ensuring the minimum active power loss, minimum voltage deviation and maximum voltage stability of the distribution network.

[0168] A specific embodiment of the present invention also provides a computer-readable medium.

[0169] The computer readable medium is a server workstation;

[0170] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method for optimizing configuration of distributed power sources in a distribution network according to an embodiment of the present invention.

[0171] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0172] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A method for optimizing the configuration of distributed power sources in a distribution network, characterized in that: The following steps are involved: Step 1: Input the line load data of the distribution network, build a distribution network model, and select multiple nodes in the distribution network model to incorporate distributed power sources; Step 2: Calculate the active power loss, voltage deviation, and voltage stability coefficient of the distribution network model, and construct a multi-objective optimization model for the distribution network by minimizing the active power loss, voltage deviation, and voltage stability of the distribution network model. Step 3: Construct the power balance constraint of the distribution network model, the node voltage constraint of the distribution network model, the branch current constraint of the distribution network model, and the distributed generation capacity constraint of the distribution network model respectively; Step 4: Use the improved transient search optimization algorithm to solve the distribution network multi-objective optimization model, set the initial parameters of the improved transient search optimization algorithm, and construct a fitness function based on the active power loss of the distribution network model, the voltage deviation of the distribution network model, and the voltage stability coefficient of the distribution network model. Select the power factor of each distributed generation in the distribution network model as the decision variable of the optimization algorithm; Step 5: Use the position vector element of each particle at the time of initialization iteration as the initial power factor of each distributed power source in the distribution network model, perform power flow calculation on the distribution network model, obtain the active power loss, voltage deviation and voltage stability coefficient of the distribution network model, update the position vector of each particle, calculate the fitness function value corresponding to the position vector of each particle based on the active power loss, voltage deviation and voltage stability coefficient obtained from the power flow calculation, and update the local optimal solution and global optimal solution of each particle within the constraint conditions; The method for updating the position vector of each particle in the improved transient search optimization algorithm described in step 5 is as follows: in, is the position vector of particle i after the kth iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, ξ is the adjustment coefficient, λ is the step coefficient, and e is a natural constant; Step 6: Repeat step 5 until the maximum number of iterations is reached to obtain the global optimal solution. According to the global optimal solution, the optimal power factor of each distributed power source in the distribution network model is obtained. Each distributed power source in the distribution network model operates at the corresponding optimal power factor to achieve the optimal configuration of the distributed power sources in the distribution network model.

2. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 1, wherein: The calculation formula for the active power loss of the distribution network model in step 2 is: Among them, P l,total is the total active power loss of the distribution network model, N is the total number of nodes in the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, R i,j is the resistance between the i-th node in the distribution network model and the j-th node in the distribution network model; The calculation formula for the voltage deviation of the distribution network model in step 2 is: Among them, V d,total is the cumulative voltage deviation of the distribution network model, V i is the voltage of the ith node in the distribution network model, V b is the reference voltage of the node in the distribution network model, and N is the total number of nodes in the distribution network model; The calculation formula for the voltage stability coefficient of the branch from node i to node j in the distribution network model described in step 2 is: Among them, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model, V i is the voltage of the ith node in the distribution network model, P j is the active power of node j, Q j is the reactive power of node j, R i,j is the resistance between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node and the j-th node in the distribution network model; The voltage stability coefficient of the distribution network model described in step 2 is defined as: C=maxC i,j i=1,2,...,N j=1,2,...,N i≠j Among them, C is the voltage stability coefficient of the distribution network model, C i,j is the voltage stability coefficient of the branch from node i to node j in the distribution network model.

3. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 2, wherein: The multi-objective optimization model of the distribution network described in step 2 is: α1+α2+α3＝1 Among them, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the active loss weight coefficient of the distribution network, α2 is the voltage deviation weight coefficient of the distribution network, and α3 is the voltage stability weight coefficient of the distribution network.

4. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 3, wherein: The power balance constraint of the distribution network model in step 3 is: Among them, M is the total number of distributed power sources in the distribution network model, N is the total number of nodes in the distribution network model, and P l,total is the total active power loss of the distribution network model, P DG,r is the active power generated by the rth distributed generation in the distribution network model, P LD,i is the active load of the ith node in the distribution network model, Q DG,r is the reactive power generated by the rth distributed generation in the distribution network model, Q LD,i is the reactive load of the ith node in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, which is calculated as follows: Where N is the total number of nodes in the distribution network model, Q l,total is the total reactive power loss of the distribution network model, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, X i,j is the reactance between the i-th node in the distribution network model and the j-th node in the distribution network model; The node voltage constraints of the distribution network model in step 3 are: In min ≤V i ≤V max Among them, V i is the voltage of the ith node in the distribution network model, V min is the minimum value of the node voltage in the distribution network model, V max is the maximum value of the node voltage of the distribution network model; The branch current constraint of the distribution network model in step 3 is: |I i,j |≤I max Among them, I i,j is the branch current between the i-th node and the j-th node in the distribution network model, I max The current flowing through the branch when the heating limit that the line can withstand is reached; The distributed power capacity constraint of the distribution network model in step 3 is: P DG,min ≤P DG ≤P DG,max Q DG,min ≤Q DG ≤Q DG,max Among them, P DG is the active power of distributed generation in the distribution network, P DG,min is the minimum value of the active power of the distributed generation, P DG,max is the maximum value of the active power of the distributed generation, Q DG is the reactive power of distributed generation in the distribution network, Q DG,min is the minimum reactive power of distributed generation, Q DG,max is the maximum value of the reactive power of the distributed generation, is the power factor of the distributed generation in the distribution network, is the minimum value of the distributed power factor, It is the maximum value of the distributed power factor.

5. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 4, characterized in that: Step 4 sets the initial parameters of the improved transient search optimization algorithm as follows: Initialize the definition of particle search space dimension as M, the number of search particles as D, and the maximum number of iterations as k max , active power loss weight coefficient is α1, voltage deviation weight coefficient is α2, voltage stability weight coefficient is α3, algorithm adjustment coefficient is ξ, and algorithm step coefficient is λ; Initialization defines the position vector of each particle at the initialization iteration as: j=1,2,...,M in, is the position vector of particle i at the time of initialization iteration, that is, the position vector when the iteration number k = 0, which corresponds to the initial power factor of each distributed power source in the distribution network model. is the jth element of the position vector of particle i at the time of initialization iteration, which corresponds to the initial power factor of the jth distributed power source in the distribution network model. M is the dimension of the particle search space, which corresponds to the number of distributed power sources in the distribution network model. Initialization defines the elements of the position vector of each particle at the initialization iteration as: j=1,2,...,M in, is the jth element of the position vector of particle i at the initialization iteration, M is the dimension of the particle search space, is the rated power factor of the distributed power generator, rand(0,1) is a random variable in the range of (0,1), is the maximum value of the distributed power factor, is the minimum value of the distributed power factor.

6. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 5, characterized in that: The fitness function constructed in step 4 is: Among them, f is the fitness function constructed, P l,total is the total active power loss of the distribution network model, V d,total is the cumulative voltage deviation of the distribution network model, P b is the rated transmission power of the distribution network, V b is the reference voltage of the node in the distribution network model, C is the voltage stability coefficient of the entire distribution network model, α1 is the weight coefficient of the active power loss of the distribution network, α2 is the weight coefficient of the voltage deviation of the distribution network, and α3 is the weight coefficient of the voltage stability of the distribution network; The adjustment coefficient ξ described in step 4 is used to adjust the search mode of the particle. Its positive and negative values change randomly with each iteration, thereby achieving local optimization and global optimization in parallel. Its update method is as follows: Among them, μ1 is a random quantity in the range of [0,1], and μ1 is revalued in each iteration. k is the number of iterations. max is the maximum number of iterations; The step size coefficient λ mentioned in step 4 is used to adjust the search step size of the particle. Its value gradually decreases with the increase of the number of iterations K, thereby adjusting the search step size to take into account both search speed and search accuracy. Its update method is as follows: Among them, μ2 is a random quantity in the range of [0,1], and μ2 is revalued in each iteration. c is a positive integer, k is the number of iterations, and k max is the maximum number of iterations.

7. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 6, wherein: Among them, μ1 is a random quantity in the range of [0,1], k is the number of iterations, k max is the maximum number of iterations. The random variable μ1 will be randomly revalued in the range of [0,1] in each iteration. When μ1>0.5, that is, ξ>0, the algorithm executes the local development mode; when μ1<0, that is, ξ<0, the algorithm executes the global exploration optimization mode. Compare the fitness function values corresponding to the updated position vectors of each particle And the fitness function value of the current local optimal solution vector of each particle Update the local optimal solution vector of each particle: in, is the local optimal position vector of particle i after the k+1th iteration, is the position vector of particle i after the k+1th iteration, is the local optimal position vector of particle i after the kth iteration, is the fitness function value of the position vector of particle i after k+1 iterations, is the fitness function value of the local optimal solution vector of particle i after the kth iteration; The global optimal solution is updated by selecting the local optimal solution, and its value is as follows: j=1,2,...,M in, is the global optimal solution vector after the k+1th iteration, is the jth element of the global optimal solution vector after the k+1th iteration, g is the number of the particle that minimizes the fitness function value after the k+1th iteration, is the fitness function value of the local optimal solution vector of the i-th particle after k+1 iterations, and D is the number of particles.

8. The method for optimizing the configuration of distributed power sources in a distribution network according to claim 7, wherein: The global optimal solution in step 6 is: X g =[X g,1 ,X g,2 ,...X g,j ,...X g,M ] j=1,2,...,M Among them, X g is the global optimal solution, that is, the optimal power factor of each distributed power source in the distribution network model, X g,j is the jth element of the global optimal solution vector, which corresponds to the optimal solution of the jth distributed generation in the distribution network model. M is the dimension of the particle search space, which corresponds to the total number of distributed generation in the distribution network. The global optimal solution obtained according to the optimization algorithm in step 6 is the optimal power factor of each distributed power source, which corresponds to the following: j=1,2,...,M in, is the optimal power factor of the jth distributed generation in the distribution network model, X g,j is the jth element of the global optimal solution vector, M is the dimension of the particle search space, which corresponds to the number of distributed generation in the distribution network model; Each distributed power source in the distribution network operates at the optimal power factor, achieving optimal configuration of the distributed power source in the distribution network, ensuring the minimum active power loss, minimum voltage deviation and maximum voltage stability of the distribution network.

9. A computer-readable medium, characterized in that It stores a computer program executed by an electronic device, and when the computer program is run on the electronic device, the electronic device executes the steps of the method according to claims 1 to 8.

Images