Optimal distribution method for three-phase load balance of power distribution network

Abstract

The invention discloses a power distribution network three-phase load balance optimal distribution method, and relates to the field of power distribution network optimization in a power system. The problem that the loss of a power distribution network line is increased due to three-phase imbalance of an existing power distribution network is solved. According to the method, a user clustering-based load three-phase balanced distribution model is adopted, and a mountain climbing algorithm is adopted to optimize a moth flame algorithm, so that the local optimization capability of the moth flame optimization algorithm is enhanced, a three-phase balanced optimal distribution strategy is obtained, the three-phase load is balanced to the greatest extent from the source, and accurate loss reduction of the load side of the power distribution network is realized. The method is suitable for obtaining the three-phase load balance optimal distribution strategy.

Description

Technical Field

[0001] This invention relates to the field of power distribution network optimization in power systems. Background Technology

[0002] With the construction of a new power system based on new energy sources, the randomness and volatility of power output from wind power, photovoltaics, and other sources, as well as the access of new loads such as electric vehicles, have increased the oscillation of power flow in the distribution network and made reactive power regulation difficult. The losses of my country's distribution network account for 50%-60% of the total power grid losses.

[0003] The main causes of three-phase imbalance in power distribution networks include three-phase load imbalance, asymmetry in three-phase line and equipment parameters, and the integration of distributed power sources. Since existing low-voltage power distribution networks generally use a three-phase four-wire power supply method, when the three-phase imbalance becomes severe to a certain extent, in addition to the potential for damage such as overheating or even destruction of rotating motor rotors, overload of power distribution equipment, and malfunction of relay protection, the most important consequence is an increase in power distribution network line losses. Summary of the Invention

[0004] This invention aims to address the problem of increased line losses in existing distribution networks due to three-phase imbalance, and provides an optimal three-phase load balance allocation method for distribution networks.

[0005] The present invention provides an optimal three-phase load balancing allocation method for a distribution network, comprising:

[0006] Step 1: Determine the total number of users to be assigned load in the distribution network and the number of user categories after clustering. Set the maximum number of algorithm iterations threshold and construct the initial user cluster group through a random generation algorithm. Each individual in the user cluster group corresponds to a three-phase phase allocation scheme for a group of users.

[0007] Step 2: Construct a fitness function based on the three-phase load balance target of the distribution network, and use the fitness function to calculate the fitness value of each individual in the user cluster.

[0008] Step 3: Based on the fitness value of each individual, select the better individuals in the initial user cluster as the initial optimal solution positions; according to the dynamic adjustment rule of the number of optimal solution positions and the update mechanism of the optimal solution positions, by sorting the current user cluster and the historical optimal solution positions by fitness value, select the top N individuals to update the set of optimal solution positions; where N is the number of optimal solution positions after dynamic adjustment.

[0009] Step 4: Based on the set of optimal solution positions, construct a spiral trajectory mathematical model. Using the spiral trajectory mathematical model with each optimal solution position as the center, control the randomness of the spiral trajectory through adjustable parameters to drive each individual in the user cluster to update to the corresponding optimal solution position; obtain the updated optimal solution position.

[0010] Step 5: Using the updated optimal solution location as the center, introduce a local optimization algorithm for secondary optimization; compare the fitness value of the new location output by the local optimization algorithm with the fitness value of the current optimal solution location. If the fitness value of the new location is better, update the optimal solution location to the new location; if the fitness value of the new location is not better, repeat the local optimization operation in this step until the maximum number of iterations is reached; until the maximum number of iterations threshold is reached or the expected requirements for the three-phase load balance of the distribution network are met, output the user three-phase phase allocation scheme corresponding to the current optimal solution location as the optimal allocation result for the three-phase load balance of the distribution network; otherwise, return to step 3.

[0011] Furthermore, in this invention, in step one, the initial user cluster group is constructed using a random generation algorithm as follows:

[0012]

[0013]

[0014] Where n represents the quantity and d represents the total number of variables in the specific optimization problem. and This represents the upper and lower bounds of the value of the j-th variable corresponding to the i-th quantity.

[0015] Furthermore, in this invention, in step two, the fitness function constructed based on the three-phase load balance objective of the distribution network is as follows:

[0016]

[0017] Where n represents the quantity. This represents the fitness of the nth user.

[0018] Furthermore, in this invention, the dynamic adjustment rule for the number of optimal solution positions in step three is as follows:

[0019]

[0020] Where N represents the initial user group size, L represents the current iteration number, and T is the iteration limit. This represents the number of optimal solutions in this iteration.

[0021] This invention presents a load three-phase balance distribution model based on user clustering. It uses a hill-climbing algorithm to optimize the moth-flame algorithm, enhancing the local optimization capability of the moth-flame optimization algorithm and obtaining the optimal three-phase balance distribution strategy. This enables the three-phase load to achieve the greatest possible balance from the source, thereby realizing precise loss reduction on the load side of the distribution network. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method described in this invention;

[0023] Figure 2 The power curves of the first three phases are shown in the diagram for balanced distribution;

[0024] Figure 3 Three-phase imbalance curve before balanced distribution;

[0025] Figure 4 A graph showing the three-phase imbalance curve after balanced allocation in the hill-climbing optimization algorithm;

[0026] Figure 5 The graph shows the comparison of the three-phase unbalance after balanced distribution.

[0027] Figure 6 A comparison curve of the three-phase imbalance before and after balanced distribution. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0029] Specific implementation method one: Refer to Figure 1 This embodiment describes a method for optimal three-phase load balancing in a distribution network, comprising:

[0030] Step 1: Determine the total number of users to be assigned load in the distribution network and the number of user categories after clustering. Set the maximum number of algorithm iterations threshold and construct the initial user cluster group through a random generation algorithm. Each individual in the user cluster group corresponds to a three-phase phase allocation scheme for a group of users.

[0031] Step 2: Construct a fitness function based on the three-phase load balance target of the distribution network, and use the fitness function to calculate the fitness value of each individual in the user cluster.

[0032] Step 3: Based on the fitness value of each individual, select the better individuals in the initial user cluster as the initial optimal solution positions; according to the dynamic adjustment rule of the number of optimal solution positions and the update mechanism of the optimal solution positions, by sorting the current user cluster and the historical optimal solution positions by fitness value, select the top N individuals to update the set of optimal solution positions; where N is the number of optimal solution positions after dynamic adjustment.

[0033] Step 4: Based on the set of optimal solution positions, construct a spiral trajectory mathematical model. Using the spiral trajectory mathematical model with each optimal solution position as the center, control the randomness of the spiral trajectory through adjustable parameters to drive each individual in the user cluster to update to the corresponding optimal solution position; obtain the updated optimal solution position.

[0034] Step 5: Using the updated optimal solution location as the center, introduce a local optimization algorithm for secondary optimization; compare the fitness value of the new location output by the local optimization algorithm with the fitness value of the current optimal solution location. If the fitness value of the new location is better, update the optimal solution location to the new location; if the fitness value of the new location is not better, repeat the local optimization operation in this step until the maximum number of iterations is reached; until the maximum number of iterations threshold is reached or the expected requirements for the three-phase load balance of the distribution network are met, output the user three-phase phase allocation scheme corresponding to the current optimal solution location as the optimal allocation result for the three-phase load balance of the distribution network; otherwise, return to step 3.

[0035] Furthermore, in this invention, in step one, the initial user cluster group is constructed using a random generation algorithm as follows:

[0036]

[0037]

[0038] Where n represents the quantity and d represents the total number of variables in the specific optimization problem. and This represents the upper and lower bounds of the value of the j-th variable corresponding to the i-th quantity.

[0039] Furthermore, in this invention, in step two, the fitness function constructed based on the three-phase load balance objective of the distribution network is as follows:

[0040]

[0041] Where n represents the quantity. This represents the fitness of the nth user.

[0042] Furthermore, in this invention, the dynamic adjustment rule for the number of optimal solution positions in step three is as follows:

[0043]

[0044] Where N represents the initial user group size, L represents the current iteration number, and T is the iteration limit. This represents the number of optimal solutions in this iteration.

[0045] During implementation, parameter settings are as follows: Assume that after cluster analysis, the 951 users are divided into several classes. Taking the k-th class with m users as an example, to solve for the optimal balanced allocation scheme for these users, some basic parameters need to be set. Since each user's phase has only three options, let... It is 3. The value is 1. It is stipulated that 3 corresponds to phase C, 2 to phase B, and 1 to phase A, which corresponds to the coding method. Then, the initial moth swarm is generated using formula (1):

[0046]

[0047] Here, n represents the number of variables, and m represents the total number of variables. Since the essence of the problem is to find the optimal phase combination for m users, the total number of users equals the total number of variables. The maximum number of iterations is set to 1000.

[0048] Taking the electricity consumption data of 951 users as an example, before the three-phase balance distribution, the phase distribution of various users and the phase imbalance degree within a day are as follows (for intuitive reasons, only the data at the hour are listed below):

[0049]

[0050] To make it more intuitive, the trends in electricity consumption per phase and the imbalance between phases throughout the day are shown as follows: Figure 2 and Figure 3 As shown, before balanced distribution, the power of each phase is relatively dispersed at the same time, with the highest three-phase imbalance at 13.76% and the lowest at 4.26%. The "Operating Regulations for Overhead Distribution Lines and Equipment" (SD292-1988) stipulates that the three-phase load imbalance should not exceed 15%. Although this is within the acceptable range, the highest three-phase imbalance is still relatively high.

[0051] This paper applies the proposed three-phase distribution network loss reduction strategy to calculate the optimal load distribution scheme for local load users. Based on cluster analysis of 951 users, this section uses a hill-climbing algorithm combined with a moth swarm optimization algorithm to solve the load distribution problem model. To further compare the performance of the algorithms before and after the improvement, Tables 4.3 to 4.4 list the corresponding objective function values ​​and iteration counts for the two algorithms. Each table corresponds to the process of solving the optimal distribution solution for each user class using the two algorithms. In Tables 4.3 to 4.8, the hill-climbing optimization algorithm is denoted as HILL-MFO.

[0052]

[0053] As shown in Tables 4.3 to 4.8, under the same number of iterations, the objective function value of the hill-climbing combined with moth swarm optimization algorithm is generally smaller than that of the moth swarm optimization algorithm. When solving for the optimal equilibrium allocation solution for each user class, both algorithms converge after approximately 500 iterations. However, the objective function value of the hill-climbing combined with moth swarm optimization algorithm is smaller than that of the moth swarm optimization algorithm at each convergence. This further demonstrates that the improved algorithm has better global optimization capabilities.

[0054] After the users are balanced and assigned according to the solutions of the two algorithms, the imbalance degree of each phase of A, B, and C is as follows: Figure 4-5 As shown;

[0055] After the users are evenly allocated according to the solutions of the two algorithms, the three-phase imbalance is as follows: Figure 6 As shown;

[0056] from Figure 2-6 It can be seen that after adjustment using the moth colony optimization algorithm, the imbalance of phases A, B, and C in this area reaches a maximum of approximately 2.9%. However, after adjustment using the hill climbing optimization algorithm, the imbalance of phases A, B, and C reaches a maximum of approximately 2.5%. This indicates that the improved algorithm achieves better balanced distribution. Figure 4-6 It can be seen that after the hill-climbing algorithm combined with the moth swarm optimization algorithm, the overall three-phase imbalance is lower than that of the moth swarm optimization algorithm. Combining this with the previous graphs, we can conclude that the addition of the hill-climbing algorithm in each iteration enhances the local optimization capability of the moth swarm optimization algorithm. Since the global optimum is generated from multiple local optima, the improved hill-climbing algorithm is closer to the theoretical optimum than the original algorithm.

[0057] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. An optimal three-phase load balancing allocation method for power distribution networks, characterized in that, include: Step 1: Determine the total number of users to be assigned load in the distribution network and the number of user categories after clustering. Set the maximum number of algorithm iterations threshold and construct the initial user cluster group through a random generation algorithm. Each individual in the user cluster group corresponds to a three-phase phase allocation scheme for a group of users. Step 2: Construct a fitness function based on the three-phase load balance target of the distribution network, and use the fitness function to calculate the fitness value of each individual in the user cluster. Step 3: Based on the fitness value of each individual, select the better individuals in the initial user cluster as the initial optimal solution positions; according to the dynamic adjustment rule of the number of optimal solution positions and the update mechanism of the optimal solution positions, by sorting the current user cluster and the historical optimal solution positions by fitness value, select the top N individuals to update the set of optimal solution positions; where N is the number of optimal solution positions after dynamic adjustment. Step 4: Based on the set of optimal solution positions, construct a spiral trajectory mathematical model. Using the spiral trajectory mathematical model with each optimal solution position as the center, control the randomness of the spiral trajectory through adjustable parameters to drive each individual in the user cluster to update to the corresponding optimal solution position; obtain the updated optimal solution position. Step 5: Using the updated optimal solution location as the center, introduce a local optimization algorithm for secondary optimization; compare the fitness value of the new location output by the local optimization algorithm with the fitness value of the current optimal solution location. If the fitness value of the new location is better, update the optimal solution location to the new location; if the fitness value of the new location is not better, repeat the local optimization operation in this step until the maximum number of iterations is reached; until the maximum number of iterations threshold is reached or the expected requirements for the three-phase load balance of the distribution network are met, output the user three-phase phase allocation scheme corresponding to the current optimal solution location as the optimal allocation result for the three-phase load balance of the distribution network; otherwise, return to step 3.

2. The optimal allocation method for three-phase load balancing in a distribution network according to claim 1, characterized in that, In step one, the initial user clusters are constructed using a random generation algorithm as follows: Where n represents the quantity and d represents the total number of variables in the specific optimization problem. and This represents the upper and lower bounds of the value of the j-th variable corresponding to the i-th quantity.

3. The optimal allocation method for three-phase load balancing in a distribution network according to claim 1 or 2, characterized in that, In step two, the fitness function is constructed based on the three-phase load balance objective of the distribution network as follows: Where n represents the quantity. This represents the fitness of the nth user.

4. The optimal allocation method for three-phase load balancing in a distribution network according to claim 1 or 2, characterized in that, In step three, the dynamic adjustment rule for the number of optimal solution positions is as follows: Where N represents the initial user group size, L represents the current iteration number, and T is the iteration limit. This represents the number of optimal solutions in this iteration.

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