Power distribution network voltage fluctuation treatment method based on improved genetic algorithm

Abstract

The embodiment of the invention provides a power distribution network voltage fluctuation treatment method based on an improved genetic algorithm, and belongs to the technical field of power generation, and the method specifically comprises the steps: 1, building a distributed photovoltaic power distribution network treatment model according to the access position, active power and reactive power limitation of distributed photovoltaic; 2, determining a composite weight according to an analytic hierarchy process and a linear weighted synthesis method; and step 3, constructing an objective function according to the composite weight, and managing the voltage fluctuation of the power distribution network through configuring a distributed photovoltaic grid-connected point and a grid-connected capacity by combining the distributed photovoltaic power distribution network management model and adopting an improved genetic algorithm. Through the scheme disclosed by the invention, multi-aspect construction is carried out for the target function, the analytic hierarchy process and the linear weighted synthesis method are introduced to calculate the weight between the indexes of the target function, and the treatment effect of the voltage fluctuation of the power distribution network is improved.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of power generation technology, and in particular to a method for managing voltage fluctuations in a distribution network based on an improved genetic algorithm. Background Art

[0002] At present, with the energy problem becoming increasingly prominent and the vigorous development of clean energy, the scale of distributed photovoltaic access to the distribution network is getting larger and larger. Distributed photovoltaic power generation has high benefits, low investment risks, great prospects and cost competitiveness, and sustainable power supply; high reliability; flexible access location and control methods, and rich reactive power voltage control methods after grid connection. Distributed photovoltaic can efficiently utilize renewable resources, effectively optimize the reactive power of the distribution network by changing the access location and capacity of distributed photovoltaic, improve the voltage quality level of the distribution network, optimize and upgrade the grid structure, and enhance the flexibility and resilience of the system, which is in line with the development trend of smart grids.

[0003] In modern distribution networks, with the widespread access of distributed photovoltaics, the network architecture has become complex. Scientific planning of distributed power configuration is crucial to the safety and stability of the power grid. The core goal of voltage fluctuation management of distributed photovoltaic access to distribution networks is to optimize distributed grid-connected nodes and their grid-connected capacity, reduce voltage fluctuations in the system, and improve the economic benefits of the distribution network. Existing research methods include establishing a suitable distributed power site selection and capacity determination model, and using intelligent algorithms to study and solve the model, such as genetic algorithms, artificial neural network algorithms, particle swarm algorithms, etc. The many methods currently used in intelligent algorithms for voltage fluctuation management often have a relatively simple construction of objective functions, which fail to fully capture and integrate the multi-dimensional and multi-level characteristics of the power system operation process, making it difficult to accurately reflect the complexity and dynamics of the actual operation of the system. When using intelligent algorithms to manage voltage fluctuations in distribution networks, the setting of objective function weights usually relies on direct weighting. Although it is simple and easy, it is easy to ignore the complex correlations and mutual influences that may exist between various indicators. The direct weighting method may lead to unreasonable weight distribution, affecting the guidance and effectiveness of the genetic algorithm in the search process, affecting the grid-connected location and grid-connected capacity of distributed photovoltaics, and limiting the effect of voltage fluctuation management on distribution networks.

[0004] It can be seen that there is an urgent need for a distribution network voltage fluctuation control method based on an improved genetic algorithm with high control effect and accuracy. Summary of the invention

[0005] In view of this, an embodiment of the present disclosure provides a method for controlling voltage fluctuations in a distribution network based on an improved genetic algorithm, which at least partially solves the problem of poor control effect and accuracy in the prior art.

[0006] The present disclosure provides a method for managing voltage fluctuations in a distribution network based on an improved genetic algorithm, including:

[0007] Step 1: Establish a distributed photovoltaic distribution network governance model based on the access location, active power and reactive power limits of distributed photovoltaics;

[0008] Step 2, determine the composite weights according to the analytic hierarchy process and the linear weighted synthesis method;

[0009] Step 3: construct the objective function according to the composite weight, and combine it with the distributed photovoltaic distribution network management model, adopt the improved genetic algorithm, and manage the voltage fluctuation of the distribution network by configuring the grid connection point and grid connection capacity of distributed photovoltaic.

[0010] According to a specific implementation of the embodiment of the present disclosure, step 1 specifically includes:

[0011] Step 1.1, set the access location, active power and reactive power limits of distributed photovoltaics;

[0012] Step 1.2: According to the access location, active power and reactive power limits of distributed photovoltaics, the equality constraints that the distribution network system needs to satisfy are set as follows:

[0013]

[0014] Among them, P Gi is the active power of the power supply connected to node i, P Li is the active power of the load connected to node i, P PVi is the active power of distributed photovoltaic connected to node i, Q Gi is the reactive power of the power source connected to node i, Q Li is the reactive power of the load connected to node i, Q PVi is the reactive power of distributed photovoltaic connected to node i, U i is the voltage amplitude at node i, U j is the voltage amplitude at node j, G ij , B ij denote the conductance and susceptance between nodes i and j, respectively, θ ij represents the voltage phase difference between node i and node j, n PV is the number of distributed photovoltaics connected;

[0015] Step 1.3: According to the access location, active power and reactive power limits of distributed photovoltaics, the inequality constraints that the distribution network system needs to satisfy are set as follows:

[0016]

[0017] Among them, P PVa To compensate for the active power of distributed photovoltaic at node a, P PVa maxTo compensate for the upper limit of the active power of distributed photovoltaic at node a, P PVa min To compensate for the lower limit of the active power of distributed photovoltaic at node a, Q PVa is the reactive power of distributed photovoltaic at point a, Q PVa max is the upper limit of the reactive power of distributed photovoltaic at point a, Q PVa min is the lower limit of the reactive power of distributed photovoltaic at point a, U k is the voltage amplitude at node k, U kmax is the upper limit of the voltage amplitude at node k, U kmin is the lower limit of the voltage amplitude at node k, and n is the number of distribution network nodes.

[0018] According to a specific implementation of the embodiment of the present disclosure, step 2 specifically includes:

[0019] Step 2.1, establish a hierarchical analysis method model, decompose the decision problem into three levels, the top layer is the target layer M, which is the objective function of the genetic algorithm, the middle layer is the criterion layer, where the criterion layer includes the network loss C1 of the distribution network, the voltage stability C2 of the distribution network and the operation and maintenance cost C3 of the distributed photovoltaic construction, and the bottom layer is the solution layer, where the solution layer includes the active power loss P1 of the distribution network system, the voltage fluctuation P2, the construction and maintenance cost P3 of the distributed photovoltaic connected to the distribution network, the operating cost P4 of the active power generated by the distributed photovoltaic, and the cost P5 of the reactive power generated by the distributed photovoltaic;

[0020] Step 2.2, construct the judgment matrix MC: compare the three elements C1, C2, and C3 in the criterion layer C in pairs to obtain a comparison matrix;

[0021] Step 2.3, construct the judgment matrix C3-P according to the relationship between C3 and the solution layer;

[0022] Step 2.5, calculate the weight vector and consistency index according to the judgment matrix C3-P;

[0023] Step 2.5, based on the linear weighted comprehensive method, the weights corresponding to the active power loss P1, voltage fluctuation P2, distributed photovoltaic construction and maintenance costs P3, distributed photovoltaic active power operation costs P4, and distributed photovoltaic reactive power costs P5 in the scheme layer are calculated according to the comparison matrix, weight vector and consistency index to form a composite weight, wherein the expression of the composite weight is:

[0024] M=λ1P1+λ2P2+λ3P3+λ4P4+λ5P5.

[0025] Among them, λ1, λ2, λ3, λ4, and λ5 are the weights of P1, P2, P3, P4, and P5 respectively, and λ1+λ2+λ3+λ4+λ5=1.

[0026] According to a specific implementation of the embodiment of the present disclosure, step 3 specifically includes:

[0027] Step 3.1, using binary coding, the independent variables are the access location and capacity of distributed photovoltaics, the length of the chromosome is set to 23*PV, and the chromosome form is (X i ,Y i ,Z i ), set the population size and initialize the random generation of the initial population, where PV represents the number of distributed photovoltaic accesses, X i is the access location of the i-th distributed photovoltaic system, Y i represents the active capacity of the i-th distributed photovoltaic, Z i represents the reactive capacity of the i-th distributed photovoltaic;

[0028] Step 3.2: Construct the initial objective function with the goal of minimizing active power loss, voltage fluctuation, and distributed photovoltaic construction and operation and maintenance costs.

[0029]

[0030] Among them, the system active loss and f1 correspond to P1 in the AHP model, the voltage fluctuation and f2 correspond to P2 in the AHP model, and the number of distributed photovoltaic connected to the distribution network n is used. PV Corresponding to P3 in the AHP model, the distributed photovoltaic active capacity P PV Corresponding to P4 in the AHP model, distributed photovoltaic reactive capacity Q PV Corresponding to P5 in the AHP model, is the maximum number of distributed photovoltaic connections, is the maximum active capacity of distributed photovoltaics, is the maximum reactive capacity of distributed photovoltaics, f loss represents the initial active power loss of the distribution network system, ΔV represents the initial voltage fluctuation of the distribution network, and R i is the resistance of the ith circuit, I ij is the current of node i and node j, U i is the voltage of the i-th node;

[0031] Step 3.3, introduce the voltage limit penalty function into the initial objective function to obtain the final objective function

[0032]

[0033] Among them, U imax =(1+7%)*voltage reference value, U imin =(1-7%)*voltage reference value, f0 is the penalty function;

[0034] Step 3.4, take the inverse of the final objective function as the fitness function

[0035]

[0036] In step 3.5, a new population is generated based on the fitness function, the genetic operator and the initial population, and the optimal population is selected to obtain the target population and manage the voltage fluctuation of the distribution network accordingly.

[0037] The distribution network voltage fluctuation control scheme based on the improved genetic algorithm in the embodiment of the present disclosure includes: step 1, establishing a distributed photovoltaic distribution network control model according to the access location, active power and reactive power limitations of distributed photovoltaics; step 2, determining the composite weight according to the hierarchical analysis method and the linear weighted synthesis method; step 3, constructing an objective function according to the composite weight, and combining the distributed photovoltaic distribution network control model, using the improved genetic algorithm, and controlling the distribution network voltage fluctuation by configuring the distributed photovoltaic grid connection point and grid connection capacity.

[0038] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, the minimization of active power loss and the improvement of voltage stability are combined, while the installation and operation and maintenance costs of distributed photovoltaics are taken into account, the practical application value of the objective function is enhanced, the voltage fluctuation of the distribution network is effectively managed, and the economic benefits of the distribution network are guaranteed; it is proposed to use the analytic hierarchy process (AHP) to analyze each indicator, scientifically calculate the optimal weight, and then integrate the weight through the linear weighted synthesis method (LWC) to form a composite weight system. This method improves the efficiency and accuracy of the weight calculation of the voltage fluctuation management objective function, reduces subjective interference, and provides strong technical support for voltage fluctuation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic diagram of a flow chart of a method for controlling voltage fluctuations in a distribution network based on an improved genetic algorithm provided in an embodiment of the present disclosure;

[0041] Figure 2 An indicator system diagram of a hierarchical analysis method provided in an embodiment of the present disclosure;

[0042] Figure 3 An algorithm flow chart of an improved genetic algorithm provided by an embodiment of the present disclosure;

[0043] Figure 4A topological diagram of an IEEE33 node provided in an embodiment of the present disclosure;

[0044] Figure 5 A voltage comparison diagram before and after distributed photovoltaic access provided by an embodiment of the present disclosure;

[0045] Figure 6 An evolution curve of an improved genetic algorithm under distributed photovoltaic access provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0047] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0048] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0049] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0050] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0051] The embodiment of the present disclosure provides a method for managing voltage fluctuations in a distribution network based on an improved genetic algorithm, which can be applied to the distribution network management process in power scenarios.

[0052] See also Figure 1 , is a flow chart of a method for managing voltage fluctuations in a distribution network based on an improved genetic algorithm provided by an embodiment of the present disclosure. Figure 1 As shown, the method mainly comprises the following steps:

[0053] Step 1: Establish a distributed photovoltaic distribution network governance model based on the access location, active power and reactive power limits of distributed photovoltaics;

[0054] In the specific implementation, a distributed photovoltaic distribution network governance model is established. In order to ensure the convergence speed of the genetic algorithm and meet the actual conditions, it is necessary to restrict the variables to ensure the feasibility and rapidity of the optimization. The access location, active power and reactive power restrictions of distributed photovoltaics are as follows.

[0055] Table 1

[0056]

[0057] Where: nPV is the number of nodes connected to the distributed photovoltaic power distribution network system.

[0058] In order to ensure the stability and security of the system operation to the optimal level, the optimization configuration model must meet certain constraints. The equality constraints that the distribution network system needs to meet are:

[0059]

[0060] Where: P Gi is the active power of the power supply connected to node i, P Li is the active power of the load connected to node i, P PVi is the active power of distributed photovoltaic connected to node i, Q Gi is the reactive power of the power source connected to node i, Q Li is the reactive power of the load connected to node i, Q PVi is the reactive power of distributed photovoltaic connected to node i, U i is the voltage amplitude at node i, U j is the voltage amplitude of node j; G ij , B ij denote the conductance and susceptance between nodes i and j respectively; θ ijrepresents the voltage phase angle difference between node i and node j; n PV is the number of distributed photovoltaics connected.

[0061] The inequality constraints that the distribution network system needs to satisfy are:

[0062]

[0063] Among them, P PVa To compensate for the active power of distributed photovoltaic at node a, P PVa max To compensate for the upper limit of the active power of distributed photovoltaic at node a, P PVa min To compensate for the lower limit of the active power of distributed photovoltaic at node a, Q PVa is the reactive power of distributed photovoltaic at point a, Q PVa max is the upper limit of the reactive power of distributed photovoltaic at point a, Q PVa min is the lower limit of the reactive power of distributed photovoltaic at point a, U k is the voltage amplitude at node k, U kmax is the upper limit of the voltage amplitude at node k, U kmin is the lower limit of the voltage amplitude at node k, and n is the number of distribution network nodes.

[0064] Step 2, determine the composite weights according to the analytic hierarchy process and the linear weighted synthesis method;

[0065] In specific implementation, the specific process of determining the composite weight according to the hierarchical analysis method and the linear weighted comprehensive method can be as follows:

[0066] (1) Establishing the AHP model

[0067] The decision problem is decomposed into three levels. The top level is the target level M, which is the objective function of the genetic algorithm. The middle level is the criterion level, which includes the network loss C1 of the distribution network, the voltage stability C2 of the distribution network and the operation and maintenance cost C3 of the distributed photovoltaic construction. The bottom level is the solution level, which includes the active power loss P1 of the distribution network system, the voltage fluctuation P2, the maintenance cost of the distributed photovoltaic construction connected to the distribution network P3, the operating cost of the active power generated by the distributed photovoltaic P4, and the reactive power cost P5 of the distributed photovoltaic. The hierarchical analysis diagram is shown in Figure 2 shown.

[0068] (2) Model solution

[0069] ① Construct the judgment matrix MC: Compare the three elements C1, C2, and C3 in the criterion layer C in pairs, and get the comparison matrix as shown in Table 2.

[0070] Table 2

[0071]

[0072] Solve the eigenvalue of MC, and the weight vector is ω = (0.3331, 0.5695, 0.0974) T. The relationship between b and RI is shown in Table 3.

[0073]

[0074] Table 3

[0075]

[0076] The calculated CR=0.0236<0.10 passed the consistency test.

[0077] ② Construct the judgment matrix C3-P as shown in Table 4

[0078] Table 4:

[0079]

[0080] The weight vector and consistency index CR calculated by the above judgment matrix C3-P are listed in the table, as shown in Table 5.

[0081] Table 5

[0082]

[0083] From the table CR<0.1, we can see that the judgment matrix C3-P passes the consistency test.

[0084] (3) Determine the composite weight based on the linear weighted synthesis method

[0085] M=0.3331P1+0.5695P2+0.0159P3+0.0526P4+0.0289P5 (5)

[0086] Step 3: construct the objective function according to the composite weight, and combine it with the distributed photovoltaic distribution network management model, adopt the improved genetic algorithm, and manage the voltage fluctuation of the distribution network by configuring the grid connection point and grid connection capacity of distributed photovoltaic.

[0087] In specific implementation, the disclosed embodiment adopts an improved genetic algorithm to manage the voltage fluctuation of the distribution network by configuring the grid connection points and grid connection capacity of distributed photovoltaics.

[0088] (1) Chromosome encoding and population initialization

[0089] The present invention adopts binary coding, the independent variables are the access location and capacity of distributed photovoltaic, and the length of the chromosome is set to 23*PV, where PV represents the number of distributed photovoltaic access. The chromosome form is (X i ,Y i ,Zi ), X i is the access location of the i-th distributed photovoltaic system, Y i represents the active capacity of the i-th distributed photovoltaic, Z i Represents the reactive capacity of the i-th distributed photovoltaic. The present invention sets the population size to 60, which is randomly generated in the initialization stage.

[0090] (2) Determine the objective function

[0091] The distributed photovoltaic optimization configuration should be determined according to the optimization purpose and the actual system operation requirements. Generally, the economy and safety of the system are considered, and the choice of objective function is diverse. The present invention starts with the minimum active power loss, the minimum voltage fluctuation and the minimum distributed photovoltaic construction and operation cost. The objective function is obtained by weighting these three objectives.

[0092] The formula for the system active power loss and

[0093]

[0094] The voltage fluctuation and formula

[0095]

[0096] Among them, f loss Represents the initial active power loss of the distribution network system, R i is the resistance of the ith circuit, I ij is the current of node i and node j, U i is the voltage of the i-th node;

[0097] Set the maximum number of nodes connected to distributed photovoltaics in the distribution network system to 3. The system active power loss and f1 correspond to P1 in the hierarchical analysis model, and the voltage fluctuation and f2 correspond to P2 in the hierarchical analysis model. The number of distributed photovoltaics connected to the distribution network n PV Corresponding to P3 in the AHP model, the distributed photovoltaic active capacity P PV Corresponding to P4 in the AHP model, distributed photovoltaic reactive capacity Q PV Corresponding to P5 in the AHP model. According to the initial voltage fluctuation and initial active power loss of the distribution network system to which distributed photovoltaics are connected, the voltage fluctuation and active power loss are normalized; according to the maximum number of nodes connected to distributed photovoltaics, the maximum active capacity and the maximum reactive capacity, the construction and operation and maintenance costs of distributed photovoltaics are normalized. According to the composite weights determined by the first step of the AHP and the linear weighted comprehensive method, the objective function is obtained as follows:

[0098]

[0099] Where: f lossrepresents the initial active power loss of the distribution network system, ΔV represents the initial voltage fluctuation of the distribution network, and U i is the voltage of the ith node.

[0100] Introduce the penalty function of voltage exceeding the limit into the objective function:

[0101]

[0102] Among them: U i is the voltage of the ith node. In order to increase the control of the algorithm on the voltage, the present invention selects U imax =(1+7%)*voltage reference value, U imin =(1-7%)*voltage reference value, f0 is the penalty function, and the present invention takes λ=10.

[0103] Finally, the objective function is formed

[0104]

[0105] By converting the objective function into a fitness index, it is ensured that individuals with high fitness values ​​are more competitive, effectively guiding the population evolution and approaching the optimal solution. The present invention takes the inverse of the objective function as the fitness function.

[0106]

[0107] (3) Genetic Operator

[0108] Basic genetic operators include: selection operation; crossover operation; mutation operation.

[0109] Implementation of the selection algorithm. The selection algorithm evaluates individuals in the population based on the fitness function to achieve survival of the fittest, screen out excellent individuals and eliminate individuals with poor performance. Select the roulette method, assuming that the population size is n, and the fitness value of the ath individual is F a , the probability of selecting the ath individual is P a for:

[0110]

[0111] Implementation of crossover algorithm. The present invention introduces a multi-point crossover strategy. According to a specific coding mechanism, multiple crossover points are randomly set on the chromosomes of the two parent generations, and gene fragments are exchanged to generate offspring. Whether the individuals in the parent generation crossover is determined based on the crossover probability. The present invention improves it by adopting an adaptive crossover probability strategy, and can effectively avoid the problem of premature convergence. The crossover probability is:

[0112]

[0113] Where: f avgis the average fitness value (excluding the selected individuals in the current population); f max is the maximum fitness value (excluding the selected individuals in the population); f' is the maximum fitness value of the individual to be crossed; the present invention takes P c1 =0.9, P c2 =0.6.

[0114] Implementation of mutation algorithm. By randomly selecting a specific position of the gene sequence and changing its gene value, mutation introduces new genetic variation, enhancing the algorithm's global search capability and solution space exploration. And invert the binary number of the mutation position, while keeping other genes unchanged, to generate a new individual:

[0115]

[0116] In order to avoid the trap of local optimal solution during the iteration process, the present invention introduces an adaptive crossover probability strategy to optimize the mutation operation. The mutation probability is:

[0117]

[0118] Where: f" is the fitness of the selected variant individuals in the population; favg is the average fitness value (excluding the selected individuals in the current population); the present invention takes P m1 =0.1, P m2 = 0.01. The mutation operation is completed through formula (15).

[0119] (5) Implementation of elimination and optimization algorithm

[0120] In order to ensure that the performance of the improved genetic algorithm is not affected by inferior individuals, individuals with lower fitness values ​​in the population are screened and eliminated. tt = 0.1. Sort the individuals in order of fitness value and replace the first N with excellent individuals. tt Individual.

[0121] The specific flow chart of the improved genetic algorithm is as follows: Figure 3 shown.

[0122] The distribution network voltage fluctuation control method based on the improved genetic algorithm provided in this embodiment is constructed in many aspects through the objective function, combining the minimization of active power loss and the improvement of voltage stability, while taking into account the installation and operation and maintenance costs of distributed photovoltaics, enhancing the practical application value of the objective function, so that the voltage fluctuation of the distribution network can be effectively controlled, while ensuring the economic benefits of the distribution network; it is proposed to use the analytic hierarchy process (AHP) to analyze each indicator, scientifically calculate the optimal weight, and then integrate the weight through the linear weighted comprehensive method (LWC) to form a composite weight system. This method improves the efficiency and accuracy of the weight calculation of the voltage fluctuation control objective function, reduces subjective interference, and provides strong technical support for voltage fluctuation control.

[0123] The method disclosed in the present invention will be further described below in conjunction with a specific embodiment. Taking the access of the distribution network to the IEEE33 node as an example, the topology diagram is as follows: Figure 4 Before testing the governance strategy, the initial power flow calculation of the IEEE33 node system is performed, where the total active power loss is 202kW, the voltage fluctuation is 1.618 (per unit), and the fitness function is:

[0124]

[0125] The optimization configuration results of the distributed photovoltaic access distribution network system are shown in Table 6.

[0126] Table 6

[0127]

[0128] When a distributed photovoltaic system is connected to the distribution network, the optimization result shows the maximum fitness value, reaching 6.2712. The number of access nodes is 6, the active power is 630kW, and the reactive power is 240kvar. This configuration scheme can reduce voltage fluctuations, and the voltage fluctuation is reduced to 0.377. It can also effectively reduce the active power loss of the distribution network, thereby improving the voltage stability and economy of the system. The voltage comparison before and after the distributed photovoltaic system is connected is shown in Figure 5; the evolution curve of the improved genetic algorithm under the distributed photovoltaic system is shown in Figure 6. In the optimization process of the distributed photovoltaic system integrated into the distribution network, this algorithm successfully achieved the convergence of the fitness function and effectively governed the voltage fluctuation of the distribution network. After optimization, the voltage of each network node meets the requirements of national standards, which is more robust than before the photovoltaic system is connected, and the maximum fluctuation is only 3.1%. It has well achieved the goal of stabilizing the voltage control target node operating voltage, and improved the voltage quality of the distribution network and the economy of the system operation.

[0129] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0130] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for controlling voltage fluctuations in a distribution network based on an improved genetic algorithm, characterized in that: include: Step 1: Establish a distributed photovoltaic distribution network governance model based on the access location, active power and reactive power limits of distributed photovoltaics; Step 2, determine the composite weights according to the analytic hierarchy process and the linear weighted synthesis method; Step 3: construct the objective function according to the composite weight, and combine it with the distributed photovoltaic distribution network management model, adopt the improved genetic algorithm, and manage the voltage fluctuation of the distribution network by configuring the grid connection point and grid connection capacity of distributed photovoltaic.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1, set the access location, active power and reactive power limits of distributed photovoltaics; Step 1.2: According to the access location, active power and reactive power limits of distributed photovoltaics, the equality constraints that the distribution network system needs to satisfy are set as follows: Among them, P Gi is the active power of the power supply connected to node i, P Li is the active power of the load connected to node i, P PVi is the active power of distributed photovoltaic connected to node i, Q Gi is the reactive power of the power source connected to node i, Q Li is the reactive power of the load connected to node i, Q PVi is the reactive power of distributed photovoltaic connected to node i, U i is the voltage amplitude at node i, U j is the voltage amplitude at node j, G ij , B ij denote the conductance and susceptance between nodes i and j, respectively, θ ij represents the voltage phase difference between node i and node j, n PV is the number of distributed photovoltaics connected; Step 1.3: According to the access location, active power and reactive power limits of distributed photovoltaics, the inequality constraints that the distribution network system needs to satisfy are set as follows: Among them, P PVa To compensate for the active power of distributed photovoltaic at node a, P PVamax To compensate for the upper limit of the active power of distributed photovoltaic at node a, P PVamin To compensate for the lower limit of the active power of distributed photovoltaic at node a, Q PVa is the reactive power of distributed photovoltaic at point a, Q PVamax is the upper limit of the reactive power of distributed photovoltaic at point a, Q PVamin is the lower limit of the reactive power of distributed photovoltaic at point a, U k is the voltage amplitude at node k, U kmax is the upper limit of the voltage amplitude at node k, U kmin is the lower limit of the voltage amplitude at node k, and n is the number of distribution network nodes.

3. The method according to claim 2, characterized in that The step 2 specifically includes: Step 2.1, establish a hierarchical analysis method model, decompose the decision problem into three levels, the top layer is the target layer M, which is the objective function of the genetic algorithm, the middle layer is the criterion layer, where the criterion layer includes the network loss C1 of the distribution network, the voltage stability C2 of the distribution network and the operation and maintenance cost C3 of the distributed photovoltaic construction, and the bottom layer is the solution layer, where the solution layer includes the active power loss P1 of the distribution network system, the voltage fluctuation P2, the construction and maintenance cost P3 of the distributed photovoltaic connected to the distribution network, the operating cost P4 of the active power generated by the distributed photovoltaic, and the cost P5 of the reactive power generated by the distributed photovoltaic; Step 2.2, construct the judgment matrix MC: compare the three elements C1, C2, and C3 in the criterion layer C in pairs to obtain a comparison matrix; Step 2.3, construct the judgment matrix C3-P according to the relationship between C3 and the solution layer; Step 2.5, calculate the weight vector and consistency index according to the judgment matrix C3-P; Step 2.5, based on the linear weighted comprehensive method, the weights corresponding to the active power loss P1, voltage fluctuation P2, distributed photovoltaic construction and maintenance costs P3, distributed photovoltaic active power operation costs P4, and distributed photovoltaic reactive power costs P5 in the scheme layer are calculated according to the comparison matrix, weight vector and consistency index to form a composite weight, wherein the expression of the composite weight is: M=λ1P1+λ2P2+λ3P3+λ4P4+λ5P5 Among them, λ1, λ2, λ3, λ4, and λ5 are the weights of P1, P2, P3, P4, and P5 respectively, and λ1+λ2+λ3+λ4+λ5=1.

4. The method according to claim 3, characterized in that The step 3 specifically includes: Step 3.1, using binary coding, the independent variables are the access location and capacity of distributed photovoltaics, the length of the chromosome is set to 23*PV, and the chromosome form is (X i ,Y i ,Z i ), set the population size and initialize the random generation of the initial population, where PV represents the number of distributed photovoltaic accesses, X i is the access location of the i-th distributed photovoltaic system, Y i represents the active capacity of the i-th distributed photovoltaic, Z i represents the reactive capacity of the i-th distributed photovoltaic; Step 3.2: Construct the initial objective function with the goal of minimizing active power loss, voltage fluctuation, and distributed photovoltaic construction and operation and maintenance costs. Among them, the system active loss and f1 correspond to P1 in the AHP model, the voltage fluctuation and f2 correspond to P2 in the AHP model, and the number of distributed photovoltaic connected to the distribution network n is used. PV Corresponding to P3 in the AHP model, the distributed photovoltaic active capacity P PV Corresponding to P4 in the AHP model, distributed photovoltaic reactive capacity Q PV Corresponding to P5 in the AHP model, is the maximum number of distributed photovoltaic connections, is the maximum active capacity of distributed photovoltaics, is the maximum reactive capacity of distributed photovoltaics, f loss represents the initial active power loss of the distribution network system, ΔV represents the initial voltage fluctuation of the distribution network, and R i is the resistance of the ith circuit, I ij is the current between nodes i and j, U i is the voltage of the i-th node; Step 3.3, introduce the voltage limit penalty function into the initial objective function to obtain the final objective function Among them, U imax =(1+7%)*voltage reference value, U imin =(1-7%)*voltage reference value, f0 is the penalty function; Step 3.4, take the inverse of the final objective function as the fitness function In step 3.5, a new population is generated based on the fitness function, the genetic operator and the initial population, and the optimal population is selected to obtain the target population and manage the voltage fluctuation of the distribution network accordingly.

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