Distribution network planning method, device, terminal and medium based on improved harmony search

Abstract

The present application discloses a distribution network planning method, device, terminal and medium based on improved harmonic search, which relates to the field of distribution network technology. The solution provided by the present application constructs a distribution network planning calculation model, optimizes the distribution network planning scheme through improved harmonic search optimization logic and the distribution network planning calculation model, and when the optimal solution does not meet the preset iteration termination condition, the optimization parameters of the improved harmonic search optimization logic are dynamically adjusted, and the distribution network planning calculation model is solved by the adjusted improved harmonic search optimization logic to obtain the solution corresponding to the next iteration cycle, until the preset iteration termination condition is met, and the optimal solution in all iteration cycles is obtained as the optimal planning scheme outputted finally, which reduces redundant operations in the calculation process and improves calculation efficiency. In complex distribution network planning problems, a better solution can be found, showing a stronger optimization capability.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network technology, and in particular to a distribution network planning method, device, terminal and medium based on improved harmony search. Background Art

[0002] As a core component of power system design, distribution network planning must achieve a complex balance between economy, reliability, and sustainability under multiple dynamic constraints. Current distribution network planning methods primarily use optimization algorithms and objective functions, such as genetic algorithms, particle swarm optimization, and ant colony optimization, to optimize distribution network planning schemes and ultimately determine the optimal plan. However, with the increasing penetration of distributed energy resources, modern distribution network planning has evolved into a complex decision-making problem coupled across multiple spatiotemporal scales, characterized by typical multi-objective conflicts. The problem requires optimizing economic objectives encompassing both the investment and operating costs of new facilities, while also satisfying technical indicators such as voltage deviation and power supply reliability. This has led to an increasingly prominent dimensional explosion in the solution space for optimizing distribution network planning, resulting in high costs and low efficiency in current distribution network planning methods. Summary of the Invention

[0003] The present application provides a distribution network planning method, device, terminal and medium based on improved harmonic search, which are used to solve the technical problems of high cost and low efficiency in current distribution network planning methods.

[0004] To solve the above technical problems, the first aspect of the present application provides a distribution network planning method based on improved harmony search, comprising:

[0005] Acquiring historical operation data, wherein the historical operation data includes: historical load power data and historical electricity price data;

[0006] Constructing a distribution network planning calculation model, wherein the distribution network planning calculation model includes an objective function and constraints, and the objective function is used to achieve an optimization goal of minimizing costs;

[0007] Based on the historical operation data, the distribution network planning calculation model is solved in combination with a preset improved harmony search optimization logic to obtain a solution corresponding to the current iteration cycle;

[0008] When the convergence accuracy or the number of iterations of the optimal solution does not meet the preset iteration termination conditions, the optimization parameters of the improved harmonic search optimization logic are dynamically adjusted, and the distribution network planning calculation model is solved by the adjusted improved harmonic search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, the optimal solution in all iteration cycles is obtained, wherein the optimization parameters include: memory bank value probability, fine-tuning probability and pitch fine-tuning bandwidth.

[0009] Preferably, the objective function is expressed as follows:

[0010]

[0011] Where, is the comprehensive cost of distribution network planning; It is the sum of the return on investment and the equipment depreciation rate. is the total investment of the newly built i-th line; is the i-th transmission line to be selected in the optimization problem, is the unit electricity price; is the maximum load utilization time, is the active power loss of the ith line, is the overload penalty coefficient, The load exceeds the total load demand of the power system. is the penalty value, Indicates a branch set.

[0012] Preferably, the constraints include: second-order cone power flow constraints, electrical quantity safety constraints and distribution network radiation constraints.

[0013] Preferably, the expression of the second-order cone power flow constraint is specifically:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] Where, and denote the active power and reactive power injected into node i, respectively. and They represent the active power flow and reactive power flow from node i to node j, and represents the square of the current in branch ij and the square of the voltage at node i, and represents the conductance and susceptance from node i to ground, and Respectively represent the active power demand and reactive power demand of load node i, M represents a relatively large number, represents a branch set, Represents a collection of nodes, Represents the set of nodes powered by the upper power grid.

[0021] Preferably, the expression of the electrical quantity safety constraint is specifically:

[0022]

[0023]

[0024]

[0025] Where, represents the voltage value of node i; and Indicates the upper and lower limits of the branch current of branch ij; and represents the upper and lower limits of the voltage at node i, represents a branch set, Represents a collection of nodes, Represents the set of nodes powered by the upper power grid.

[0026] Preferably, the dynamic adjustment formula of the optimization parameter is specifically:

[0027]

[0028]

[0029]

[0030] Where, is the probability of memory bank value in iteration cycle t, and are the upper and lower bounds of the probability of taking values ​​from the memory bank, is the topological overlap rate between the optimal solution of the current iteration cycle and the historical optimal solution, is the baseline attenuation step length, is the critical threshold of topological overlap, is the fine-tuning probability of iteration period t, and are the upper and lower bounds of the fine-tuning probability, is the pitch tuning bandwidth of iteration period t, and are the upper and lower limits of the pitch fine-tuning bandwidth, is the maximum number of iterations, is the absolute value of the maximum voltage deviation corresponding to the optimal solution of the current iteration cycle, It is the maximum voltage deviation threshold of the entire network.

[0031] Preferably, it also includes:

[0032] Collecting historical load data of each node in the distribution network, and then determining the load similarity between different nodes based on the historical load data;

[0033] Clustering the nodes according to the load similarity to obtain a number of load areas;

[0034] According to the load similarity of each node in the same load area, the similarity entropy value corresponding to each load area is calculated respectively, and then each similarity entropy value is normalized to use the normalized similarity entropy value as the initial value of the memory bank value probability.

[0035] At the same time, the second aspect of the present application provides a distribution network planning device based on improved harmony search, comprising:

[0036] A historical data acquisition unit, configured to acquire historical operation data, wherein the historical operation data includes: historical load power data and historical electricity price data;

[0037] A model building unit, configured to build a distribution network planning calculation model, wherein the distribution network planning calculation model includes an objective function and constraints, wherein the objective function is used to achieve an optimization goal of minimizing costs;

[0038] A model solving unit, configured to solve the distribution network planning calculation model based on the historical operation data and in combination with a preset improved harmonic search optimization logic to obtain a solution corresponding to a current iteration cycle;

[0039] A model iteration optimization control unit is used to dynamically adjust the optimization parameters of the improved harmonic search optimization logic when the convergence accuracy or the number of iterations of the optimal solution does not meet the preset iteration termination conditions, and then solve the distribution network planning calculation model through the adjusted improved harmonic search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, and the optimal solution in all iteration cycles is obtained, wherein the optimization parameters include: memory bank value probability, fine-tuning probability and pitch fine-tuning bandwidth.

[0040] A third aspect of the present application provides a distribution network planning terminal based on improved harmony search, comprising: a memory and a processor;

[0041] The memory is used to store program code, and the program code is used to implement a distribution network planning method based on improved harmony search as provided in the first aspect of the present application;

[0042] The processor is configured to read and execute the program code.

[0043] The fourth aspect of the present application provides a computer-readable storage medium, in which program code is stored. The program code is used to be read and executed by a processor to implement a distribution network planning method based on improved harmony search as provided in the first aspect of the present application.

[0044] It can be seen from the above technical solutions that this application has the following advantages:

[0045] The solution provided in the present application constructs a distribution network planning calculation model with minimum cost as the optimal objective function combined with overload limit and radial network structure as constraints, and optimizes the distribution network planning scheme through improved harmonic search optimization logic and distribution network planning calculation model. When the convergence accuracy or number of iterations of the optimal solution does not meet the preset iteration termination conditions, the optimization parameters of the improved harmonic search optimization logic are dynamically adjusted, and the distribution network planning calculation model is solved by the adjusted improved harmonic search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or number of iterations meets the preset iteration termination conditions, the optimal solution in all iteration cycles is obtained as the optimal planning scheme outputted finally, which reduces redundant operations in the calculation process, reduces calculation time, and improves calculation efficiency. In complex distribution network planning problems, it can find better solutions and show stronger optimization capabilities, thus solving the technical problems of high cost and low efficiency in the current distribution network planning methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 This is a flow chart of an embodiment of a distribution network planning method based on improved harmony search provided in this application.

[0048] Figure 2This is a flow chart of the process of determining the initial value of the HMCR parameter in a distribution network planning method based on improved harmony search provided in this application.

[0049] Figure 3 This is a structural diagram of an embodiment of a distribution network planning device based on improved harmony search provided by this application.

[0050] Figure 4 A schematic structural diagram of an embodiment of a distribution network planning terminal based on improved harmony search provided in this application. DETAILED DESCRIPTION

[0051] The embodiments of the present application provide a distribution network planning method, device, terminal and medium based on improved harmonic search, which are used to solve the technical problems of high cost and low efficiency in current distribution network planning methods.

[0052] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0053] First, a detailed description of an embodiment of a distribution network planning method based on improved harmony search provided by this application is as follows:

[0054] See also Figure 1 , the present application provides an embodiment of a distribution network planning method based on improved harmony search, comprising:

[0055] Step 101: Obtain historical operation data;

[0056] It should be noted that, first, historical operation data is collected, including load power data, unit electricity price, etc. For the convenience of calculation, the historical operation optimization data of each period can be arranged in a matrix format, with different operations representing different types of operation data.

[0057] Step 102: Construct a distribution network planning calculation model;

[0058] It should be noted that a distribution network planning calculation model is then constructed for optimizing the distribution network planning scheme. The distribution network planning calculation model includes an objective function and constraints. The objective function of the distribution network planning calculation model is used to achieve the optimization goal of minimizing cost. The specific calculation formula can be referred to as follows:

[0059]

[0060] Where, It is the comprehensive cost of distribution network planning, usually in years; , is the return on investment; is the equipment depreciation rate; is the total investment in building the i-th line; is an n-dimensional decision vector; are the n transmission lines to be selected in the optimization problem. yes The element of , when the i-th line is newly built, its value is 1, otherwise it is 0; is the unit electricity price; is the maximum load utilization time, specifically the annual maximum load utilization hours; is the active power loss of the i-th line; is the overload penalty coefficient; The load exceeds the total load demand of the power system; is a very large penalty value, which is the target value when the radial constraint is not satisfied; Indicates a branch set.

[0061] The constraints of the distribution network planning calculation model include: second-order cone power flow constraints, electrical quantity security constraints and distribution network radiation constraints.

[0062] More specifically, the second-order cone power flow constraint provided in this embodiment is expressed as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Where, and denote the active power and reactive power injected into node i, respectively. and They represent the active power flow and reactive power flow from node i to node j, and represents the square of the current in branch ij and the square of the voltage at node i, and represents the conductance and susceptance from node i to ground, and Respectively represent the active power demand and reactive power demand of load node i, M represents a relatively large number, Represents a collection of nodes, Represents the set of nodes powered by the upper power grid.

[0070] The electrical quantity safety constraint provided in this embodiment is specifically used to balance the reliability of distribution network operation, and its expression is as follows:

[0071]

[0072]

[0073]

[0074] Where, represents the voltage value of node i, Indicates the current value of branch ij; and Indicates the upper and lower limits of the branch current of branch ij; and Indicates the upper and lower limits of the voltage at node i.

[0075] The distribution network radiation constraint of this embodiment is used to reflect the characteristics of closed-loop construction and open-loop operation of the distribution network. Its specific expression is as follows:

[0076]

[0077] Where, Represents a collection of nodes, represents the set of power supply nodes of the upper power grid, Indicates a branch set.

[0078] Step 103: Based on the historical operation data, the distribution network planning calculation model is solved in combination with the preset improved harmony search optimization logic to obtain a solution corresponding to the current iteration cycle;

[0079] It should be noted that based on the model constructed in step 102, the improved harmony search algorithm (IHS) is used to solve the distribution network planning calculation model. The IHS algorithm creates a new harmony (called "improvisation") based on the HS algorithm. The main control parameters are the harmony library (HM), harmony library size (HMS), memory library value probability (HMCR), fine-tuning probability (PAR), and pitch fine-tuning bandwidth (BW). During the iterative process, each new harmony vector is generated based on three rules: (i) memory library value, (ii) fine-tuning, and (iii) random selection. The iterative update formula is as follows:

[0080]

[0081] Where: is a random number uniformly distributed in the range [0,1]; is the value space of the i-th variable.

[0082] Each variable value obtained by accessing the memory bank is checked to determine whether it should be fine-tuned. The PAR parameter is the probability of fine-tuning. The fine-tuning probability equation can be described as:

[0083]

[0084] Where: is a random number uniformly distributed in the range [0, 1]. is a constant whose value is (-1, 1).

[0085] The improved harmony search algorithm (IHS) achieves efficient solutions to complex optimization problems by dynamically adjusting key parameters and incorporating the physical constraints of the distribution network. Its core is to leverage historical solution information stored in the harmony memory (HM), combining random exploration with local fine-tuning mechanisms to gradually approach the global optimal solution. In its implementation, the algorithm generates a new solution vector using the aforementioned update formula, where the value of each variable is selected based on the memory memory probability (HMCR): components of historical solutions are randomly selected from the harmony memory with the probability HMCR; otherwise, new values ​​are randomly generated from the solution space. This mechanism preserves the heuristic information of high-quality solutions while avoiding premature entrapment in local optimality through randomness.

[0086] After generating an initial solution, the algorithm further refines the solution using the fine-tuning probability equations described above. For continuous variables (such as line impedance or load factor), a random perturbation is applied to the current value using the probabilistic PAR. The perturbation amplitude is controlled by the bandwidth (BW), specifically by adding or subtracting a random number multiplied by the BW from the original value. For discrete variables (such as line switch status), the probabilistic PAR is used to flip the current state (for example, from 0 to 1 or vice versa). This fine-tuning strategy enables the algorithm to dynamically balance global exploration and local exploitation—initially, a larger BW and higher PAR are used to enhance solution diversity, while later, the perturbation range is gradually narrowed to improve convergence accuracy.

[0087] In distribution network planning scenarios, algorithmic constraint handling is particularly critical. For example, a radial topology requires a tree-like structure with no loops. To this end, the algorithm verifies the topological feasibility of a new solution after generating it. If the current line configuration results in loops or islands, the minimum spanning tree algorithm is used to forcibly modify the network structure to ensure that radial constraints are met. At the same time, voltage safety constraints and line capacity limits are implemented through penalty terms in the objective function. If a solution causes voltage violations or line overload, its fitness value will be significantly increased, guiding the algorithm to automatically avoid infeasible areas.

[0088] Step 104: When the convergence accuracy or the number of iterations of the optimal solution does not meet the preset iteration termination conditions, the optimization parameters of the improved harmony search optimization logic are dynamically adjusted, and the distribution network planning calculation model is solved by the adjusted improved harmony search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, and the optimal solution in all iteration cycles is obtained.

[0089] It should be noted that the improved parameter correction and update mechanism of the harmony search algorithm is the core of its performance optimization. By dynamically adjusting HMCR (harmony memory considering rate), PAR (pitch adjustment rate) and BW (pitch adjustment bandwidth), the algorithm can adaptively balance global exploration and local development capabilities, thereby effectively coping with the complexity of the high-dimensional and multi-peak solution space in distribution network planning.

[0090] The dynamic adjustment of HMCR takes into account the spatiotemporal correlation characteristics of the distribution network node load and introduces a "topological similarity feedback mechanism". If the topological overlap between the current solution and the historical optimal solution is higher than the threshold, the HMCR decay rate is increased to accelerate local development. The specific dynamic adjustment steps are as follows:

[0091] Every N iterations ( ) Calculate the topological overlap between the current solution and the historical optimal solution:

[0092]

[0093]

[0094] Where: It is a binary variable, and when it is 1, it means The solution of the route is consistent with the optimal solution, and it is inconsistent when it is 0; is the total number of lines, is the topological overlap.

[0095] Then, according to the preset HMCR adjustment formula, combined with the comparison results of the topological overlap and the topological overlap critical threshold, the HMCR parameters are dynamically adjusted. The HMCR adjustment formula is as follows:

[0096]

[0097] Where: is the maximum number of iterations, is the baseline attenuation step length, The higher the value, the faster the decay. is the critical threshold of topological overlap that triggers HMCR accelerated decay. When the topological overlap between the current solution and the historical optimal solution reaches this value, it means that the risk of the algorithm falling into local optimality increases significantly. Its value is generally , through the above formula, when the threshold is exceeded, the algorithm is forced to jump out of the current search area by accelerating the attenuation of HMCR (increasing the probability of random exploration), reducing the risk of the algorithm falling into local optimality.

[0098] Furthermore, if Figure 2 As shown, the solution provided in this embodiment may further include the following steps:

[0099] Step 1001: Collect historical load data of each node in the distribution network, and then determine the load similarity between different nodes based on the historical load data;

[0100] Step 1002: Clustering the nodes according to load similarity to obtain several load areas;

[0101] Step 1003: Calculate the similarity entropy value corresponding to each load area based on the load similarity of each node in the same load area, and then normalize each similarity entropy value to use the normalized similarity entropy value as the initial value of the memory bank value probability.

[0102] It should be noted that steps 1001 to 1003 are the process of determining the initial values ​​of the HMCR parameters. The specific process example includes:

[0103] a. Collect the historical 24-hour load curves of each node and use the DTW dynamic time warping algorithm to calculate the similarity between the curves;

[0104] b. Divide the nodes into K regions with similar load patterns through spectral clustering and calculate the similarity entropy value within the region:

[0105]

[0106] Where: is the node proportion of the kth region; is the similarity entropy value within the region.

[0107] c. Set the initial HMCR based on the entropy normalization result:

[0108]

[0109] Where: is the initial value of the probability of taking values ​​from the memory bank, Represents the maximum similarity entropy value, is the lower limit of the initial HMCR value, is the maximum adjustment range of the load similarity entropy to the initial value, and its value range is , ensure that the initial HMCR value is between.

[0110] The lower the entropy value, the more concentrated the load pattern. The higher the initial HMCR value (up to 0.9), the more helpful it is for the algorithm to prioritize the use of known high-quality solutions in similar areas. In the early stages of the dynamic adjustment algorithm, a higher HMCR value (close to ) can prompt the algorithm to prioritize the selection of historical high-quality solutions from the harmony library and use existing experience to accelerate convergence; as the number of iterations increases, HMCR gradually decreases to , enhance the random search capability to avoid falling into local optimality.

[0111] The dynamic adjustment of PAR parameters can be achieved through the following formula, which is as follows:

[0112]

[0113] Where: and are the maximum and minimum fine-tuning probabilities, respectively.

[0114] It should be noted that the nonlinear change characteristics of PAR make the algorithm maintain a high fine-tuning probability (close to ), allowing the solution to be disturbed to a greater extent, thereby jumping out of the local extreme value area; as the iteration deepens, the PAR value gradually approaches , reduce the perturbation intensity to fine-tune the quality of the solution. 、 For example, the PAR value drops rapidly to around 0.3 in the early stages, transitions smoothly in the middle stages, and stabilizes around 0.1 in the later stages. This adjustment strategy is particularly important in distribution network planning, because the discrete nature of line configuration (0 / 1 decision making) requires the algorithm to try multiple topology combinations with high PAR values ​​in the early stages, and then focus on local optimization to reduce redundant operations in the later stages.

[0115] The dynamic adjustment of BW parameters can be achieved through the following formula:

[0116]

[0117] Where: is the absolute value of the maximum voltage deviation of the entire network corresponding to the current solution (per unit value); and Fine-tune the bandwidth for maximum and minimum tones respectively, This is the maximum voltage deviation threshold for the entire network. Its value setting strictly follows the national standard "Power Quality Supply Voltage Deviation" (GB / T12325-2008) to ensure that the algorithm output solution must meet engineering specifications. It can generally be set to 0.05 by default.

[0118] It should be noted that the adjustment of the pitch fine-tuning bandwidth (BW) takes into account that voltage quality is the core constraint indicator in distribution network planning, and the traditional IHS algorithm adopts a fixed bandwidth attenuation strategy, which cannot respond to voltage limit problems that occur during the optimization process. Therefore, this embodiment adopts a dynamic bandwidth adjustment mechanism driven by voltage sensitivity. When the voltage deviation does not exceed the specified limit, the linear decreasing characteristic of BW directly affects the fine-tuning range of the solution: the initial larger bandwidth (close to ) supports wide-area search for continuous variables (such as line capacity or load rate), covering more potential high-quality solutions; later smaller bandwidth (close to ) is used to fine-tune the numerical accuracy of the solution. For discrete variables, the attenuation of BW indirectly affects the aggressiveness of the fine-tuning strategy. In the high BW stage, it is more inclined to try state flipping, while in the low BW stage, it tends to maintain the current configuration. When the voltage deviation exceeds the specified limit, BW increases linearly with the voltage deviation, and the maximum recovery is .

[0119] The distribution network planning calculation model is solved cyclically and iteratively through the adjusted improved harmony search optimization logic until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, and the final optimal solution is obtained.

[0120] The above is a detailed description of an embodiment of a distribution network planning method based on improved harmonic search provided by this embodiment. The solution provided by this application is based on the combination of improved harmonic search and distribution network planning scenarios. By dynamically adjusting the memory library value probability (HMCR), fine-tuning probability (PAR) and pitch fine-tuning bandwidth (BW), it can quickly locate the local optimal solution in the early stage of the search, and effectively avoid falling into the local optimal solution in the later stage, thereby significantly improving the convergence speed. Secondly, in terms of optimization effect, the IHS algorithm performs well in minimizing costs and improving system reliability, especially in complex distribution network planning problems, and can find better solutions and show stronger optimization capabilities. In addition, the IHS algorithm reduces redundant operations in the calculation process by dynamically adjusting control parameters, significantly reduces calculation time, and improves calculation efficiency. In comparison with other optimization algorithms, the IHS algorithm not only demonstrates the optimality of understanding quality, but also shows high stability and robustness, providing an efficient and reliable optimization solution for distribution network planning. At the same time, in the optimization link of IHS parameters, this application also dynamically adjusts the memory library value probability (HMCR) and tone fine-tuning bandwidth (BW) in combination with specific distribution network planning scenarios, achieving the goal of "the algorithm search direction is consistent with the physical laws of the power grid", further improving the optimization effect of the algorithm in distribution network planning scenarios.

[0121] The following is a detailed description of an embodiment of a distribution network planning device based on improved harmony search provided by this application, specifically as follows:

[0122] See also Figure 3 , the present application provides a distribution network planning device based on improved harmony search, comprising:

[0123] The historical data acquisition unit 201 is used to acquire historical operation data, wherein the historical operation data includes: historical load power data and historical electricity price data;

[0124] The model building unit 202 is used to build a distribution network planning calculation model, wherein the distribution network planning calculation model includes an objective function and constraints, and the objective function is used to achieve an optimization goal of minimizing costs;

[0125] The model solving unit 203 is used to solve the distribution network planning calculation model based on historical operation data and in combination with the preset improved harmony search optimization logic to obtain a solution corresponding to the current iteration cycle;

[0126] The model iteration optimization control unit 204 is used to dynamically adjust the optimization parameters of the improved harmony search optimization logic when the convergence accuracy or number of iterations of the optimal solution does not meet the preset iteration termination conditions, and then solve the distribution network planning calculation model through the adjusted improved harmony search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or number of iterations meets the preset iteration termination conditions, and the optimal solution in all iteration cycles is obtained, wherein the optimization parameters include: memory bank value probability, fine-tuning probability and pitch fine-tuning bandwidth.

[0127] In addition, if Figure 4 As shown, the present application also provides an embodiment of a distribution network planning terminal based on improved harmony search, the implementation types of the terminal include but are not limited to: personal computers, industrial computers, servers and embedded intelligent devices, the main components of the terminal include: memory 33 and processor 31, wherein the memory 33 and processor 31 can be connected via a communication bus 34;

[0128] The memory 33 is used to store program codes, and the program codes are used to implement a distribution network planning method based on improved harmony search as provided in the above embodiment;

[0129] The processor 31 is used to read and execute program codes.

[0130] In a fourth aspect, the present application provides a computer-readable storage medium storing program code, which is used to be read and executed by a processor to implement a distribution network planning method based on improved harmony search as provided in the above embodiment.

[0131] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0133] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein, for example, can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0134] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0138] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A distribution network planning method based on improved harmony search, characterized in that: include: Acquiring historical operation data, wherein the historical operation data includes: historical load power data and historical electricity price data; Constructing a distribution network planning calculation model, wherein the distribution network planning calculation model includes an objective function and constraints, the objective function is used to achieve an optimization goal of minimizing cost, and the constraints include: a second-order cone power flow constraint, an electrical quantity safety constraint, and a distribution network radiation constraint; Based on the historical operation data, the distribution network planning calculation model is solved in combination with a preset improved harmony search optimization logic to obtain a solution corresponding to the current iteration cycle; When the convergence accuracy or the number of iterations of the optimal solution does not meet the preset iteration termination conditions, the optimization parameters of the improved harmony search optimization logic are dynamically adjusted, and the distribution network planning calculation model is solved by the adjusted improved harmony search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, the optimal solution in all iteration cycles is obtained, wherein the optimization parameters include: memory bank value probability, fine-tuning probability and pitch fine-tuning bandwidth; The expression of the objective function is specifically: ； Where, is the comprehensive cost of distribution network planning; It is the sum of the return on investment and the equipment depreciation rate. is the total investment of the newly built i-th line; is the i-th transmission line to be selected in the optimization problem, is the unit electricity price; is the maximum load utilization time, is the active power loss of the ith line, is the overload penalty coefficient, The load exceeds the total load demand of the power system. is the penalty value, Indicates a branch set.

2. A distribution network planning method based on improved harmony search according to claim 1, characterized in that: The expression of the second-order cone power flow constraint is specifically: ； ； ； ； ； ； Where, and denote the active power and reactive power injected into node i, respectively. and They represent the active power flow and reactive power flow from node i to node j, and represents the square of the current in branch ij and the square of the voltage at node i, and represents the conductance and susceptance from node i to ground, and They represent the active power demand and reactive power demand of load node i respectively, M is a constant, represents a branch set, Represents a collection of nodes, Represents the set of nodes powered by the upper power grid.

3. A distribution network planning method based on improved harmony search according to claim 1, characterized in that: The expression of the electrical quantity safety constraint is specifically: ； ； ； Where, represents the voltage value of node i; and Indicates the upper and lower limits of the branch current of branch ij; and represents the upper and lower limits of the voltage at node i, represents a branch set, Represents a collection of nodes, Represents the set of nodes powered by the upper power grid.

4. A distribution network planning method based on improved harmony search according to claim 1, characterized in that: The dynamic adjustment formula of the optimization parameters is specifically: ； ； ； Where, is the probability of memory bank value in iteration cycle t, and are the upper and lower bounds of the probability of taking values ​​from the memory bank, is the topological overlap rate between the optimal solution of the current iteration cycle and the historical optimal solution, is the baseline attenuation step length, is the critical threshold of topological overlap, is the fine-tuning probability of iteration period t, and are the upper and lower bounds of the fine-tuning probability, is the pitch tuning bandwidth of iteration period t, and are the upper and lower limits of the pitch fine-tuning bandwidth, is the maximum number of iterations, is the absolute value of the maximum voltage deviation corresponding to the optimal solution of the current iteration cycle, It is the maximum voltage deviation threshold of the entire network.

5. A distribution network planning method based on improved harmony search according to claim 1, characterized in that: Also includes: Collecting historical load data of each node in the distribution network, and then determining the load similarity between different nodes based on the historical load data; Clustering the nodes according to the load similarity to obtain a number of load areas; According to the load similarity of each node in the same load area, the similarity entropy value corresponding to each load area is calculated respectively, and then each similarity entropy value is normalized to use the normalized similarity entropy value as the initial value of the memory bank value probability.

6. A distribution network planning device based on improved harmony search, characterized in that: include: A historical data acquisition unit, configured to acquire historical operation data, wherein the historical operation data includes: historical load power data and historical electricity price data; A model building unit, configured to build a distribution network planning calculation model, wherein the distribution network planning calculation model includes an objective function and constraints, wherein the objective function is used to achieve an optimization goal of minimizing cost, and the constraints include: a second-order cone power flow constraint, an electrical quantity safety constraint, and a distribution network radiation constraint; A model solving unit, configured to solve the distribution network planning calculation model based on the historical operation data and in combination with a preset improved harmonic search optimization logic to obtain a solution corresponding to a current iteration cycle; A model iteration optimization control unit is used to dynamically adjust the optimization parameters of the improved harmony search optimization logic when the convergence accuracy or the number of iterations of the optimal solution does not meet the preset iteration termination conditions, and then solve the distribution network planning calculation model through the adjusted improved harmony search optimization logic to obtain the solution corresponding to the next iteration cycle, until the convergence accuracy or the number of iterations meets the preset iteration termination conditions, and obtain the optimal solution in all iteration cycles, wherein the optimization parameters include: memory bank value probability, fine-tuning probability and pitch fine-tuning bandwidth; The expression of the objective function is specifically: ； Where, is the comprehensive cost of distribution network planning; It is the sum of the return on investment and the equipment depreciation rate. is the total investment of the newly built i-th line; is the i-th transmission line to be selected in the optimization problem, is the unit electricity price; is the maximum load utilization time, is the active power loss of the ith line, is the overload penalty coefficient, The load exceeds the total load demand of the power system. is the penalty value, Indicates a branch set.

7. A distribution network planning terminal based on improved harmony search, characterized in that: include: memory and processor; The memory is used to store program code, and the program code is used to implement the distribution network planning method based on improved harmony search according to any one of claims 1 to 5; The processor is configured to read and execute the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and the program code is used to be read and executed by a processor to implement a distribution network planning method based on improved harmony search as described in any one of claims 1 to 5.

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