High-voltage distribution network reactive power planning method and system based on improved particle swarm optimization

Abstract

The invention particularly relates to a high-voltage distribution network reactive power planning method and system based on an improved particle swarm optimization algorithm, and the method comprises the steps: determining a target function of a high-voltage distribution network reactive power planning model; setting constraint conditions and related variables; a particle swarm algorithm is used as a reference algorithm, an improved particle swarm algorithm is adopted to optimize the reactive power planning model of the high-voltage distribution network, and the improved particle swarm algorithm comprises the step of introducing a self-adaptive adjustment strategy on an inertia weight and an acceleration factor; initializing related parameters in the particle swarm optimization; performing iterative calculation, updating the adaptive value, the individual optimal value and the global optimal value of each particle and the speed and the position of the particle to judge whether the maximum number of iterations is reached or not, and if the maximum number of iterations is reached, stopping iteration and outputting an optimal solution; and if the maximum number of iterations is not reached, continuing the iterative operation. According to the method, reactive power distribution can be optimized, network loss and operation cost are remarkably reduced, and reactive power optimization of the high-voltage power distribution network is more efficient and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of economic dispatching of distribution network systems, and in particular to a method and system for reactive power planning of a high-voltage distribution network based on an improved particle swarm algorithm. Background Art

[0002] In the high-voltage distribution network of the power system, under normal circumstances, reactive power flows from the power source to the load to meet the load, establish the magnetic field and other normal operation requirements, but when some abnormal working conditions or unreasonable operation modes occur, it may cause reactive power to flow in the opposite direction, resulting in reactive power reverse transmission problems in the high-voltage distribution network. To solve the reactive power reverse transmission problem, it is necessary to reasonably select the capacity of reactive power compensation equipment at the corresponding position of the high-voltage distribution network.

[0003] When using the existing particle swarm algorithm to solve the reactive power reverse transmission problem in high-voltage distribution networks, in the process of searching for the optimal solution, particles update their own speed and position by tracking individual extreme values ​​and group extreme values. However, in a complex search space, especially in the case of reactive power optimization of high-voltage distribution networks with multiple local optimal solutions, particles may gather near the local optimal solution too early, and it is difficult to jump out of the area to explore the global optimal solution later, so that the final solution is not the best solution to solve the reactive power reverse transmission problem to the greatest extent.

[0004] Therefore, it is necessary to provide a new reactive power planning method and system for high-voltage distribution network based on improved particle swarm algorithm. Summary of the invention

[0005] Based on the above problems existing in the prior art, the purpose of the embodiments of the present invention is to provide a high-voltage distribution network reactive power planning method and system based on an improved particle swarm algorithm that can optimize reactive power distribution, significantly reduce network losses and operating costs, and make reactive power optimization of high-voltage distribution networks more efficient and reliable.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a reactive power planning method for a high-voltage distribution network based on an improved particle swarm algorithm, comprising:

[0007] S1, determine the objective function of the reactive power planning model of the high-voltage distribution network;

[0008] S2, setting the constraints and related variables of the reactive power planning model of the high-voltage distribution network;

[0009] S3, taking the particle swarm algorithm as the benchmark algorithm, adopting the improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor;

[0010] S4, initializing the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing particle position and speed, randomly generating particle swarms, initializing the historical optimal position and global optimal position of each particle;

[0011] S5, iteratively calculate and update the fitness value, individual optimal value, global optimal value of each particle, and the speed and position of the particle;

[0012] S6, determine whether the maximum number of iterations has been reached, if so, stop the iteration and output the optimal solution; if not, return to step S5 to continue the iterative operation.

[0013] Further, in S1, the objective function of determining the reactive power planning model of the high-voltage distribution network includes:

[0014] Step S11, calculating the investment and operation cost of reactive power compensation equipment.

[0015] Step S12, calculating reactive power reverse transmission.

[0016] Step S13, calculating the power factor reactive sensitivity.

[0017] Step S14, calculating the objective function according to the reactive power compensation equipment investment and operation cost, reactive power reverse transmission and reactive power sensitivity of the power factor.

[0018] Furthermore, the calculation formula for the reactive equipment investment and operating cost is:

[0019]

[0020] C op,i =c i Q used,i

[0021] Among them, f1 is the investment and operation cost of reactive equipment, N is the total number of nodes, C inv,i is the investment cost of reactive power compensation equipment at node i, C pp,i is the operating cost of the reactive power compensation equipment at node i, a i is the unit capacity investment cost, Q comp,i is the equipment capacity of node i, b i is the fixed installation cost of the equipment, c i is the unit capacity operating cost, Q uscd,i is the actual reactive power compensation output power of node i.

[0022] Furthermore, the reactive power reverse calculation formula is:

[0023]

[0024] Among them, f2 is reactive power reverse transmission, N is the total number of nodes, |Q send,i | is the reactive power transmitted from node i to other nodes, Q gen,i is the reactive power generation of node i, Q load,i is the reactive load of node i, Q comp,i is the equipment capacity of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j.

[0025] Furthermore, the calculation formula of the power factor reactive sensitivity is:

[0026]

[0027] Where f3 is the power factor reactive sensitivity, N is the total number of nodes, PF i is the power factor of node i, Q comp,i is the equipment capacity of node i, is the sensitivity of the power factor of node i to the reactive power compensation amount, S i is the apparent power of node i, P i is the active power of node i, Q i is the reactive power of node i.

[0028] Furthermore, in S2, the constraints of the reactive power planning model of the high-voltage distribution network include: voltage constraints of each node, branch power constraints, reactive power compensation capacity constraints and reactive power balance constraints, specifically:

[0029] Step S21, the voltage constraint of each node is:

[0030]

[0031] Among them, V min is the minimum value of the node voltage, V i is the voltage value of node i, V max is the maximum value of the node voltage;

[0032] Step S22: The power transmission of each branch must meet the capacity constraint, and the branch power constraint is:

[0033]

[0034] Among them, Q ij is the reactive power flowing from node i to adjacent node j, S max,ij is the maximum power capacity of branch (i, j);

[0035] Step S23: The reactive power compensation capacity of each node must be within the allowed range of the equipment, and the reactive power compensation capacity constraint is:

[0036]

[0037] Among them, Q comp,i is the equipment capacity of node i, Q comp,max,i is the maximum reactive power compensation capacity that can be installed at node i;

[0038] Step S24: The reactive power must satisfy the reactive power balance condition of each node. The reactive power balance constraint is:

[0039]

[0040] Among them, Q gen,i is the reactive power generation of node i, Q comp,i is the equipment capacity of node i, Q load,i is the reactive load of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j;

[0041] Step S25, setting the relevant variables of the high-voltage distribution network reactive power planning model to the reactive power compensation equipment capacity Q installed or adjusted at each node comp,i .

[0042] Further, in S3, the use of an improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network includes:

[0043] Step S31, improving the inertia weight ω in the particle swarm algorithm, including: adaptively and dynamically adjusting the inertia weight based on the diversity of the particle swarm to balance the global search and local development capabilities;

[0044] Step S32, improving the acceleration factors c1 and c2 in the particle swarm algorithm, including: adaptively and dynamically adjusting the acceleration factors to achieve an early focus on global search and a later focus on local development;

[0045] Furthermore, the calculation formula for the adaptive dynamic adjustment of the inertia weight is:

[0046]

[0047] Among them, ω is the inertia weight, ω max is the maximum inertia weight, ω min is the minimum inertia weight, σ is the current diversity of the particle swarm, and σ max is the diversity at initialization, N is the particle swarm size, x i is the position of particle i, and g is the global optimal position of the entire particle swarm.

[0048] Furthermore, the calculation formula for the adaptive dynamic adjustment acceleration factor is:

[0049]

[0050] Where c1 is the first acceleration factor, c 1,max is the maximum value of the first acceleration factor, c 1,min is the minimum value of the first acceleration factor, T max is the maximum number of iterations, t is the current number of iterations, c2 is the second acceleration factor, c 2,min is the maximum value of the second acceleration factor, c 2,min is the minimum value of the second acceleration factor.

[0051] A reactive power planning system for a high-voltage distribution network based on an improved particle swarm algorithm is applied to the reactive power planning method for a high-voltage distribution network based on an improved particle swarm algorithm. The system comprises:

[0052] A function determination module is used to determine the objective function of the reactive power planning model of the high-voltage distribution network;

[0053] Setting condition and variable module, used to set constraint conditions and related variables of reactive power planning model of high voltage distribution network;

[0054] An improved algorithm module is used to optimize the reactive power planning model of the high-voltage distribution network by using the improved particle swarm algorithm as a benchmark algorithm, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor;

[0055] The initialization module is used to initialize the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing particle position and speed, randomly generating particle swarms, and initializing the historical optimal position and global optimal position of each particle;

[0056] Iterative calculation module, used for iterative calculation and updating the fitness value, individual optimal value, global optimal value of each particle and the speed and position of the particle;

[0057] The output judgment module is used to judge whether the maximum number of iterations has been reached. If so, the iteration is stopped and the optimal solution is output; if not, the module returns to the previous step to continue the iterative operation.

[0058] The beneficial effects of the present invention are as follows: the reactive power planning method of a high-voltage distribution network based on an improved particle swarm algorithm of the present invention includes: determining the objective function of the reactive power planning model of a high-voltage distribution network; setting the constraints and related variables of the reactive power planning model of a high-voltage distribution network; using the particle swarm algorithm as a benchmark algorithm, and using the improved particle swarm algorithm to optimize the reactive power planning model of a high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on inertia weight and acceleration factor; initializing the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing the particle position, speed, randomly generating a particle swarm, and initializing the historical optimal position and global optimal position of each particle. Perform iterative calculations and update the fitness value, individual optimal value, global optimal value, and speed and position of each particle; determine whether the maximum number of iterations has been reached, if reached, stop the iteration and output the optimal solution; if the maximum number of iterations has not been reached, return to the previous step to continue the iterative operation. The reactive power planning method for a high-voltage distribution network based on an improved particle swarm algorithm of the present invention introduces the inertia weight and acceleration factor in the particle swarm algorithm into an adaptive adjustment strategy, thereby enhancing the global search capability and convergence performance of the algorithm. In view of the particularity of reactive power planning, by introducing decision variables to optimize the objective function, factors such as the investment cost, operating loss and reactive power sensitivity of reactive power compensation equipment are comprehensively considered, thereby effectively avoiding reactive power reverse transmission, optimizing reactive power distribution, and significantly reducing network loss and operating cost, thereby making reactive power optimization of a high-voltage distribution network more efficient and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0060] In the figure:

[0061] Figure 1 A flowchart of a reactive power planning method for a high-voltage distribution network based on an improved particle swarm algorithm provided in the first embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a module of a high-voltage distribution network reactive power planning system based on an improved particle swarm algorithm provided in the second embodiment of the present invention;

[0063] Figure 3 It is a structural diagram of a network-side server provided according to a third embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0065] First embodiment:

[0066] The first embodiment of the present invention provides a method for reactive power planning of a high-voltage distribution network based on an improved particle swarm algorithm, including: determining the objective function of the reactive power planning model of the high-voltage distribution network; setting the constraints and related variables of the reactive power planning model of the high-voltage distribution network; using the particle swarm algorithm as a benchmark algorithm, and using the improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor; initializing the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing the particle position, speed, randomly generating a particle swarm, and initializing the historical optimal position and the global optimal position of each particle. Perform iterative calculations and update the fitness value, individual optimal value, global optimal value, and speed and position of each particle; determine whether the maximum number of iterations has been reached, if reached, stop the iteration and output the optimal solution; if the maximum number of iterations has not been reached, return to the previous step to continue the iterative operation. The reactive power planning method for a high-voltage distribution network based on an improved particle swarm algorithm of the present invention introduces the inertia weight and acceleration factor in the particle swarm algorithm into an adaptive adjustment strategy, thereby enhancing the global search capability and convergence performance of the algorithm. In view of the particularity of reactive power planning, by introducing decision variables to optimize the objective function, factors such as the investment cost, operating loss and reactive power sensitivity of reactive power compensation equipment are comprehensively considered, thereby effectively avoiding reactive power reverse transmission, optimizing reactive power distribution, and significantly reducing network loss and operating cost, thereby making reactive power optimization of a high-voltage distribution network more efficient and reliable.

[0067] The following is a detailed description of the implementation details of the high-voltage distribution network reactive power planning method based on the improved particle swarm algorithm in this embodiment. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution. The specific process of this embodiment is as follows: Figure 1 As shown, this implementation is applied to a reactive power planning system of a high-voltage distribution network based on an improved particle swarm algorithm.

[0068] Step S1, determining the objective function of the reactive power planning model of the high-voltage distribution network.

[0069] Specifically, the specific steps of determining the objective function of the reactive power planning model of the high-voltage distribution network include:

[0070] Step S11, calculating the investment and operation cost of reactive power compensation equipment.

[0071] As an example, the investment and operating costs of reactive power compensation equipment are important considerations for economic efficiency. The calculation formula for reactive power equipment investment and operating costs is:

[0072]

[0073] C op,i =c i Q used,i

[0074] Among them, f1 is the investment and operation cost of reactive equipment, N is the total number of nodes, C inv,i is the investment cost of reactive power compensation equipment at node i, C pp,i is the operating cost of the reactive power compensation equipment at node i, a i is the unit capacity investment cost, Q comp,i is the equipment capacity of node i, b i is the fixed installation cost of the equipment, c i is the unit capacity operating cost, Q uscd,i is the actual reactive power compensation output power of node i.

[0075] Step S12, calculating reactive power reverse transmission.

[0076] As an example, reactive power reverse transmission will increase the reactive power transmission loss of the power grid. The reactive power reverse transmission calculation formula is:

[0077]

[0078] Among them, f2 is reactive power reverse transmission, N is the total number of nodes, |Q send,i | is the reactive power transmitted from node i to other nodes, Q gen,i is the reactive power generation of node i, Q load,i is the reactive load of node i, Q comp,i is the equipment capacity of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j.

[0079] Step S13, calculating the power factor reactive sensitivity.

[0080] As an example, power factor reactive sensitivity is an important indicator for measuring voltage regulation capability. The calculation formula for power factor reactive sensitivity is:

[0081]

[0082] Where f3 is the power factor reactive sensitivity, N is the total number of nodes, PF iis the power factor of node i, Q comp,i is the equipment capacity of node i, is the sensitivity of the power factor of node i to the reactive power compensation amount, S i is the apparent power of node i, P i is the active power of node i, Q i is the reactive power of node i.

[0083] Step S14, calculating the objective function according to the reactive power compensation equipment investment and operation cost, reactive power reverse transmission and reactive power sensitivity of the power factor.

[0084] As an example, the objective function of this embodiment is to minimize the investment and operating cost of reactive power compensation equipment, minimize reactive power reverse transmission, and maximize the reactive power sensitivity of the power factor.

[0085] Furthermore, the calculation formula of the objective function is:

[0086] MinF=w1f1+w2f2-w3f3

[0087] Among them, w1 is the weight factor corresponding to the reactive equipment investment and operating cost, w2 is the weight factor corresponding to the reactive reverse transmission, w3 is the weight factor corresponding to the reactive sensitivity of the power factor, f1 is the reactive equipment investment and operating cost, f2 is the reactive reverse transmission, and f3 is the reactive sensitivity of the power factor.

[0088] Furthermore, w1+w2+w3=1, that is, all weights satisfy ∑w i =1.

[0089] Step S2, setting the constraints and related variables of the reactive power planning model of the high-voltage distribution network;

[0090] Specifically, the constraints of the reactive power planning model of the high-voltage distribution network include: voltage constraints of each node, branch power constraints, reactive power compensation capacity constraints and reactive power balance constraints.

[0091] Step S21, the voltage constraint of each node is:

[0092]

[0093] Among them, V min is the minimum value of the node voltage, V i is the voltage value of node i, V max is the maximum value of the node voltage.

[0094] Step S22: The power transmission of each branch must meet the capacity constraint, and the branch power constraint is:

[0095]

[0096] Among them, Q ij is the reactive power flowing from node i to adjacent node j, S max,ij is the maximum power capacity of branch (i, j).

[0097] Step S23: The reactive power compensation capacity of each node must be within the allowed range of the equipment, and the reactive power compensation capacity constraint is:

[0098]

[0099] Among them, Q comp,i is the equipment capacity of node i, Q comp,max,i is the maximum reactive power compensation capacity that can be installed at node i.

[0100] Step S24: The reactive power must satisfy the reactive power balance condition of each node. The reactive power balance constraint is:

[0101]

[0102] Among them, q gen,i is the reactive power generation of node i, Q comp,i is the equipment capacity of node i, Q load,i is the reactive load of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j.

[0103] Step S25, setting the relevant variables of the high-voltage distribution network reactive power planning model to the reactive power compensation equipment capacity Q installed or adjusted at each node comp,i .

[0104] Step S3, using the particle swarm algorithm as a benchmark algorithm, and using an improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor.

[0105] Specifically, the existing particle swarm algorithm simulates a group of particles moving in the search space, and the position of each particle represents a possible solution. The particle adjusts its position based on its own experience and the experience of the entire group. The individual experience is the optimal position, and the experience of the entire group is the global optimal position.

[0106] The existing particle swarm algorithm first defines the particle swarm size, that is, the number of particles, and randomly initializes the position and velocity of each particle, namely:

[0107] x i ~U(x min ,x max ),v i ~U(v min ,vax )

[0108] Among them, x i is the position of particle i, x min is the lower limit of the position of particle i, x max is the upper limit of the position of particle i, v i is the velocity of particle i, v min is the minimum velocity of particle i, v max is the maximum velocity of particle i.

[0109] Then the fitness value of each particle, the individual optimal position, the global optimal position, the particle's velocity and position are updated.

[0110] Update the fitness value of each particle, that is, calculate the position x of each particle i The fitness value f(x i ).

[0111] Update the individual optimal position p of each particle i for:

[0112]

[0113] Among them, p i is the individual optimal position of each particle, x i The position of each particle.

[0114] Update the global optimal position g of the entire particle swarm to:

[0115]

[0116] Among them, g is the global optimal position of the entire particle swarm, p i is the individual optimal position of each particle.

[0117] The formula for updating the particle speed is:

[0118]

[0119] in, is the velocity of the particle at the t+1th iteration, is the velocity of the particle at the tth iteration, ω is the inertia weight, c1 is the first acceleration factor, c2 is the second acceleration factor, r1 and r2 are random numbers, p i is the historical optimal position of particle i, and g is the global optimal position of the entire particle swarm.

[0120] Furthermore, the inertia weight ω is used to control the attenuation of the particle speed. The first acceleration factor c1 is used to measure the impact of the individual optimum on the speed update, and the second acceleration factor c2 is used to measure the impact of the global optimum on the speed update. The typical value range of c1 and c2 is [0,2]. Usually c1+c2=4 can maintain convergence. r1 and r2 are random numbers, uniformly distributed on [0,1]. Because the inertia weight ω determines the balance between exploration and development. A larger ω is conducive to global search, and a smaller ω is conducive to local search. In the prior art, the inertia weight ω often adopts a linear decreasing strategy as follows:

[0121]

[0122] Among them, ω is the inertia weight, ω max is the maximum inertia weight, ω min is the minimum inertia weight, T is the maximum number of iterations, and t is the current number of iterations.

[0123] Furthermore, in the formula for updating the particle's velocity, As an inertia term, it represents the particle's maintenance of the current velocity and controls the particle's exploration ability in the search space. As an individual cognitive item, it is used to guide particles closer to their historical optimal position. As a social cognitive item, it guides particles to move toward the global optimal position of the group.

[0124] The calculation formula for updating the particle position is:

[0125]

[0126] in, is the position of the particle at the t+1th iteration, is the position of the particle at the tth iteration, is the velocity of the particle at the t+1th iteration.

[0127] Furthermore, if the particle's position or velocity exceeds the limit, a boundary adjustment is performed. As an example:

[0128] v i =min(max(v i ,v min ),v max )

[0129] x i =min(max(x i ,x min ),x max )

[0130] Among them, v i is the velocity of particle i, v minis the minimum velocity of particle i, v max is the maximum velocity of particle i, x i is the position of particle i, x min is the lower limit of the position of particle i, x max is the upper limit of the position of particle i.

[0131] Furthermore, when the maximum number of iterations is reached or the change of the global optimal value is lower than a certain threshold, the iteration is stopped.

[0132] Although the particle swarm optimization algorithm is simple, efficient and has global search capabilities, it also has some defects in practical applications. The particle swarm optimization algorithm is prone to fall into the local optimal solution, especially in multi-peak optimization problems. The group may converge to the suboptimal solution too early and it is difficult to jump out of the local area. At the same time, there is a contradiction between the fast convergence speed and the insufficient solution accuracy. Its fast convergence reduces the ability to explore the solution space. In addition, the algorithm performance is highly dependent on parameters such as inertia weight and acceleration coefficient, and lacks a general optimization criterion, which increases the difficulty of application. During the iteration process, the diversity of the particle swarm decreases, reducing the global search ability. In the later stage, it relies more on information sharing between particles and ignores the unexplored area. The particle swarm optimization algorithm has limited performance in complex constrained optimization problems and dynamic optimization problems. Constraint processing requires additional design mechanisms and is insufficient to adapt to dynamic changes. Therefore, it is necessary to improve the original particle swarm optimization algorithm.

[0133] Specifically, the improved particle swarm algorithm is used to optimize the reactive power planning model of high-voltage distribution network, which includes the following steps:

[0134] Step S31, improving the inertia weight ω in the particle swarm algorithm, including: adaptively and dynamically adjusting the inertia weight based on the diversity of the particle swarm to balance the global search and local development capabilities.

[0135] Specifically, the calculation formula for adaptively and dynamically adjusting the inertia weight is:

[0136]

[0137] Among them, ω is the inertia weight, ω max is the maximum inertia weight, ω min is the minimum inertia weight, σ is the current diversity of the particle swarm, and σ max is the diversity at initialization, N is the particle swarm size, x i is the position of particle i, and g is the global optimal position of the entire particle swarm.

[0138] Step S32, improving the acceleration factors c1 and c2 in the particle swarm algorithm, including: adaptively and dynamically adjusting the acceleration factors to achieve an early focus on global search and a later focus on local development.

[0139] The calculation formula for adaptive dynamic adjustment acceleration factor is:

[0140]

[0141] Where c1 is the first acceleration factor, c 1,max is the maximum value of the first acceleration factor, c 1,min is the minimum value of the first acceleration factor, T max is the maximum number of iterations, t is the current number of iterations, c2 is the second acceleration factor, c 2,min is the maximum value of the second acceleration factor, c 2,mim is the minimum value of the second acceleration factor.

[0142] By adaptively and dynamically adjusting the acceleration factor, c1>c2 is set in the early stage, and particles pay more attention to individual experience; in the later stage, c2>c1, and particles pay more attention to group collaboration.

[0143] Step S4, initializing the relevant parameters in the particle swarm algorithm includes: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing particle position and speed, randomly generating a particle swarm, and initializing the historical optimal position and global optimal position of each particle.

[0144] Specifically, the capacity of reactive power compensation equipment installed or adjusted at each node is Q comp,i As a decision variable, first initialize the particle swarm, set the particle swarm size N, particle dimension D, and maximum number of iterations T max , initialize the particle position and speed Randomly generate a particle swarm and initialize the historical optimal position p of each particle i and the global optimal position g o .

[0145] Step S5, iteratively calculate and update the fitness value, individual optimal value, global optimal value of each particle, and the speed and position of the particle.

[0146] Specifically, the existing particle swarm algorithm is used to calculate the fitness f(x i ), update the particle's historical optimal position and the global optimal position

[0147] As an example, the inertia weight and acceleration factor after adaptive dynamic adjustment are added to the speed update formula. The speed update formula of the reactive power planning model of the high-voltage distribution network optimized by the improved particle swarm algorithm is:

[0148] v i (t+1)=w(t)·v i (t)+c1(t)·r1·(pi -x i )+c2(t)·r2·(gx i )

[0149]

[0150] Among them, v i (t+1) is the velocity of particle i at the t+1th iteration, w(t) is the inertia weight at the tth iteration, v i (t) is the velocity of the particle at the tth iteration, c1(t) is the first acceleration factor at the tth iteration, p i is the historical optimal position of particle i, x i is the position of particle i, c2(t) is the second acceleration factor at the tth iteration, ω max is the maximum inertia weight, ω min is the minimum inertia weight, σ is the current diversity of the particle swarm, and σ max is the diversity at initialization, N is the particle swarm size, c 1,max is the maximum value of the first acceleration factor, c 1,min is the minimum value of the first acceleration factor, T max is the maximum number of iterations, t is the current number of iterations, c 2,min is the maximum value of the second acceleration factor, c 2,min is the minimum value of the second acceleration factor.

[0151] The position update formula of the improved particle swarm optimization algorithm to optimize the reactive power planning model of high-voltage distribution network is:

[0152] x i (t+1)=x i (t)+v i (t+1)

[0153] Among them, x i (t+1) is the position of particle i at the t+1th iteration, x i (t) is the position of particle i at the tth iteration, v i (t+1) is the velocity of particle i at the t+1th iteration.

[0154] Step S6, determine whether the maximum number of iterations has been reached. If so, stop the iteration and output the optimal solution. If not, return to step S5 to continue the iterative operation.

[0155] The first embodiment of the present invention provides a method for reactive power planning of a high-voltage distribution network based on an improved particle swarm algorithm, including: determining the objective function of the reactive power planning model of the high-voltage distribution network; S2, setting the constraints and related variables of the reactive power planning model of the high-voltage distribution network; using the particle swarm algorithm as the benchmark algorithm, and using the improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor; initializing the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing the particle position, speed, randomly generating a particle swarm, and initializing the historical optimal position and global optimal position of each particle. Perform iterative calculations and update the fitness value, individual optimal value, global optimal value, and speed and position of each particle; determine whether the maximum number of iterations has been reached, if so, stop the iteration and output the optimal solution; if the maximum number of iterations has not been reached, return to the previous step to continue the iterative operation. The reactive power planning method of high-voltage distribution network based on improved particle swarm algorithm of the present invention introduces the inertia weight and acceleration factor in the particle swarm algorithm into the adaptive adjustment strategy, enhances the global search capability and convergence performance of the algorithm, and optimizes the objective function by introducing decision variables for the particularity of reactive power planning, comprehensively considers the investment cost, operation loss and reactive power sensitivity of reactive power compensation equipment, can effectively avoid the phenomenon of reactive power reverse transmission, optimize reactive power distribution, and significantly reduce network loss and operation cost, so that the reactive power optimization of high-voltage distribution network is more efficient and reliable. The improved particle swarm algorithm of the present invention has better optimization performance, and improves the economy of reactive power optimization of high-voltage distribution network to a certain extent.

[0156] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0157] Second implementation method:

[0158] like Figure 2 As shown, the second embodiment of the present invention provides a high-voltage distribution network reactive power planning system based on an improved particle swarm algorithm, the system comprising: a function determination module 201, a condition and variable setting module 202, an improved algorithm module 203, an initialization module 204, an iterative calculation module 205, and a judgment output module 206.

[0159] Specifically, a function determination module 201 is used to determine the objective function of the reactive power planning model of the high-voltage distribution network; a condition and variable setting module 202 is used to set the constraint conditions and related variables of the reactive power planning model of the high-voltage distribution network; an improved algorithm module 203 is used to optimize the reactive power planning model of the high-voltage distribution network by using an improved particle swarm algorithm with a particle swarm algorithm as a benchmark algorithm, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor; an initialization module 204 is used to initialize the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing the particle position and speed, randomly generating a particle swarm, and initializing the historical optimal position and the global optimal position of each particle; an iterative calculation module 205 is used to perform iterative calculations and update the fitness value, individual optimal value, global optimal value, and speed and position of each particle; a judgment output module 206 is used to judge whether the maximum number of iterations has been reached, and if so, stop the iteration and output the optimal solution; if the maximum number of iterations has not been reached, return to the previous step to continue the iterative operation.

[0160] It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0161] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0162] A third embodiment of the present invention relates to a network side server, such as Figure 3 As shown, it includes at least one processor 302; and a memory 301 that is communicatively connected to the at least one processor 302; wherein the memory 301 stores instructions that can be executed by the at least one processor 302, and the instructions are executed by the at least one processor 302 so that the at least one processor 302 can execute the above-mentioned data processing method.

[0163] The memory 301 and the processor 302 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 302 and the memory 301 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 302 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 302.

[0164] The processor 302 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 301 can be used to store data used by the processor 302 when performing operations.

[0165] The fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for reactive power planning of a high-voltage distribution network based on an improved particle swarm algorithm in the first embodiment is implemented.

[0166] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0167] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

[0168] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A reactive power planning method for high-voltage distribution network based on improved particle swarm algorithm, characterized in that: include: S1, determine the objective function of the reactive power planning model of the high-voltage distribution network; S2, setting the constraints and related variables of the reactive power planning model of the high-voltage distribution network; S3, taking the particle swarm algorithm as the benchmark algorithm, adopting the improved particle swarm algorithm to optimize the reactive power planning model of the high-voltage distribution network, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor; S4, initializing the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing particle position and speed, randomly generating particle swarms, initializing the historical optimal position and global optimal position of each particle; S5, iteratively calculate and update the fitness value, individual optimal value, global optimal value of each particle, and the speed and position of the particle; S6, determine whether the maximum number of iterations has been reached, if so, stop the iteration and output the optimal solution; if not, return to step S5 to continue the iterative operation.

2. The high-voltage distribution network reactive power planning method based on improved particle swarm algorithm according to claim 1 is characterized in that: In S1, the objective function of determining the reactive power planning model of the high-voltage distribution network includes: Step S11, calculating the investment and operation cost of reactive power compensation equipment. Step S12, calculating reactive power reverse transmission. Step S13, calculating the power factor reactive sensitivity. Step S14, calculating the objective function according to the reactive power compensation equipment investment and operation cost, reactive power reverse transmission and reactive power sensitivity of the power factor.

3. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 2 is characterized in that: The calculation formula for the reactive equipment investment and operating cost is: C inv，i =a i ·Q comp，i +b i C op，i =c i ·Q used，i Among them, f1 is the investment and operation cost of reactive equipment, N is the total number of nodes, C inv，i is the investment cost of reactive power compensation equipment at node i, C op，i is the operating cost of the reactive power compensation equipment at node i, a i is the unit capacity investment cost, Q comp，i is the equipment capacity of node i, b i is the fixed installation cost of the equipment, c i is the unit capacity operating cost, Q usce，i is the actual reactive power compensation output power of node i.

4. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 2 is characterized in that: The reactive power reverse calculation formula is: Among them, f2 is reactive power reverse transmission, N is the total number of nodes, |Q send，i | is the reactive power transmitted from node i to other nodes, Q gen，i is the reactive power generation of node i, Q load，i is the reactive load of node i, Q comp，i is the equipment capacity of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j.

5. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 2 is characterized in that: The calculation formula of the power factor reactive sensitivity is: Where f3 is the power factor reactive sensitivity, N is the total number of nodes, PF i is the power factor of node i, Q comp，i is the equipment capacity of node i, is the sensitivity of the power factor of node i to the reactive compensation amount, Si is the apparent power of node i, Pi is the active power of node i, Q i is the reactive power of node i.

6. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 1, characterized in that: In S2, the constraints of the reactive power planning model of the high-voltage distribution network include: voltage constraints of each node, branch power constraints, reactive power compensation capacity constraints and reactive power balance constraints, specifically: Step S21, the voltage constraint of each node is: Among them, V min is the minimum value of the node voltage, V i is the voltage value of node i, V max is the maximum value of the node voltage; Step S22: The power transmission of each branch must meet the capacity constraint, and the branch power constraint is: Among them, Q ij is the reactive power flowing from node i to adjacent node j, S max，ij is the maximum power capacity of branch (i, j); Step S23: The reactive power compensation capacity of each node must be within the allowed range of the equipment, and the reactive power compensation capacity constraint is: Among them, Q comp，i is the equipment capacity of node i, Q comp，max，i is the maximum reactive power compensation capacity that can be installed at node i; Step S24: The reactive power must satisfy the reactive power balance condition of each node. The reactive power balance constraint is: Among them, Q gen，i is the reactive power generation of node i, Q comp，i is the equipment capacity of node i, Q load，i is the reactive load of node i, ∑ j∈adj(i) Q ij is the sum of reactive power flowing from node i to adjacent node j; Step S25, setting the relevant variables of the high-voltage distribution network reactive power planning model to the reactive power compensation equipment capacity Q installed or adjusted at each node comp，i .

7. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 1 is characterized in that: In S3, the method of optimizing the reactive power planning model of the high-voltage distribution network using the improved particle swarm algorithm includes: Step S31, improving the inertia weight ω in the particle swarm algorithm, including: adaptively and dynamically adjusting the inertia weight based on the diversity of the particle swarm to balance the global search and local development capabilities; Step S32, improving the acceleration factors c1 and c2 in the particle swarm algorithm, including: adaptively and dynamically adjusting the acceleration factors to achieve an early focus on global search and a later focus on local development.

8. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 7 is characterized in that: The calculation formula for the adaptive dynamic adjustment of inertia weight is: Among them, ω is the inertia weight, ω max is the maximum inertia weight, ω min is the minimum inertia weight, σ is the current diversity of the particle swarm, and σ max is the diversity at initialization, N is the particle swarm size, x i is the position of particle i, and g is the global optimal position of the entire particle swarm.

9. The method for reactive power planning of high-voltage distribution network based on improved particle swarm algorithm according to claim 7, characterized in that: The calculation formula for the adaptive dynamic adjustment acceleration factor is: Where c1 is the first acceleration factor, c 1,max is the maximum value of the first acceleration factor, c 1,min is the minimum value of the first acceleration factor, T max is the maximum number of iterations, t is the current number of iterations, c2 is the second acceleration factor, c 2,min is the maximum value of the second acceleration factor, c 2,mim is the minimum value of the second acceleration factor.

10. A high-voltage distribution network reactive power planning system based on an improved particle swarm algorithm is characterized by: The method for reactive power planning of a high-voltage distribution network based on an improved particle swarm algorithm as described in claim 1 comprises: A function determination module is used to determine the objective function of the reactive power planning model of the high-voltage distribution network; Setting condition and variable module, used to set constraint conditions and related variables of reactive power planning model of high voltage distribution network; An improved algorithm module is used to optimize the reactive power planning model of the high-voltage distribution network by using the improved particle swarm algorithm as a benchmark algorithm, wherein the improved particle swarm algorithm includes introducing an adaptive adjustment strategy on the inertia weight and the acceleration factor; The initialization module is used to initialize the relevant parameters in the particle swarm algorithm, including: setting the particle swarm scale, particle dimension, maximum number of iterations, initializing particle position and speed, randomly generating particle swarms, and initializing the historical optimal position and global optimal position of each particle; Iterative calculation module, used for iterative calculation and updating the fitness value, individual optimal value, global optimal value of each particle and the speed and position of the particle; The output judgment module is used to judge whether the maximum number of iterations has been reached. If so, the iteration is stopped and the optimal solution is output; if not, the module returns to the previous step to continue the iterative operation.

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