Distributed power supply locating and sizing planning method and device based on improved Hemma optimization algorithm

Abstract

The invention discloses a distributed power supply locating and sizing planning method and device based on an improved Hemma optimization algorithm, relates to the technical field of power distribution network planning, and is used for solving the technical problem of low efficiency of distributed power supply locating and sizing planning caused by the existing distributed power supply locating and sizing planning method. The method comprises the steps that when distributed power supply locating and sizing planning needs to be carried out, locating and sizing constraint conditions are set by taking network active loss and voltage deviation minimization as targets, and a distributed power supply locating and sizing multi-target model is constructed; and based on a K-means clustering algorithm and a multi-objective optimization mechanism, performing iterative solution on the distributed power supply locating and sizing multi-objective model by adopting an improved Hemma optimization algorithm, and generating a distributed power supply locating and sizing planning scheme. According to the invention, the K-means clustering algorithm is utilized to improve the sorting efficiency, and the improved Hemma optimization algorithm is combined to guide the solutions to approach to different target values, so that the efficiency of obtaining the distributed power supply locating and sizing planning scheme is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and particularly relates to a distributed power source siting and sizing planning method and device based on an improved hippopotamus optimization algorithm. Background Art

[0002] Distributed power sources generally refer to a decentralized and non-centralized power generation method, usually referring to small-scale, environmentally compatible power generation devices with a power of several kilowatts to several hundred kilowatts, which are used to meet the specific requirements of power systems and users, such as peak shaving, power supply for remote users or residential areas, and can save power supply and distribution investment and improve power supply reliability.

[0003] When a large number of distributed power sources randomly connect to or disconnect from the distribution network, it will have an impact on system reliability, relay protection, power quality, and system losses, and will increase the difficulty of system load forecasting, making the distribution network planning process more difficult. Therefore, system operation planners must re-evaluate the impact brought by distributed power sources to implement new load forecasting methods and appropriate optimization algorithms, and give the best location and capacity of DG (Distributed Generation).

[0004] Most of the existing distributed power source siting and sizing planning methods, when using intelligent optimization algorithms (such as genetic algorithms or particle swarm algorithms) to solve, even for a network with only 50 nodes, it often takes more than 30 minutes to complete a complete siting and sizing optimization calculation; for a distribution network with a scale of 200 nodes, the calculation time is more likely to be as long as several hours. This slow planning speed is mainly due to: the algorithm needs to repeatedly perform power flow calculations to evaluate each candidate scheme, and each power flow calculation itself takes several seconds; at the same time, the intelligent algorithm needs to perform thousands or even tens of thousands of iterative evaluations to find the optimal solution, resulting in low efficiency of distributed power source siting and sizing planning. Summary of the Invention

[0005] The present invention provides a distributed power source siting and sizing planning method and device based on an improved hippopotamus optimization algorithm, which is used to solve the technical problem that the existing distributed power source siting and sizing planning methods result in low efficiency of distributed power source siting and sizing planning.

[0006] A distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm provided by the first aspect of the present invention includes:

[0007] Responding to a planning request, with the goal of minimizing network active power loss and voltage deviation, setting siting and sizing constraint conditions, and constructing a distributed power source siting and sizing multi-objective model;

[0008] Based on the K - means clustering algorithm and the multi - objective optimization mechanism, an improved hippopotamus optimization algorithm is used to iteratively solve the multi - objective model for the siting and sizing of distributed power sources, and a siting and sizing planning scheme for distributed power sources is generated;

[0009] The multi - objective optimization mechanism includes a non - dominated sorting mechanism and a sine - power chaotic mapping mechanism; Based on the K - means clustering algorithm and the multi - objective optimization mechanism, using the improved hippopotamus optimization algorithm to iteratively solve the multi - objective model for the siting and sizing of distributed power sources and generate a siting and sizing planning scheme for distributed power sources, including:

[0010] Based on the sine - power chaotic mapping mechanism, a chaotic sequence is generated;

[0011] Using the chaotic sequence to generate an initial hippopotamus population, and based on the multi - objective model for the siting and sizing of distributed power sources, according to the decision vectors corresponding to each hippopotamus individual in the initial hippopotamus population, the fitness vectors corresponding to each hippopotamus individual are output; The fitness vector includes the network active power loss target value and the voltage deviation target value; The decision vector includes the installation location of the distributed power source and the capacity of the distributed power source;

[0012] Using the K - means clustering algorithm to cluster each hippopotamus individual according to each fitness vector, and output multiple clusters;

[0013] Using the non - dominated sorting mechanism to analyze each cluster, and generate the initial non - dominated sorting set at the current moment;

[0014] Screening the initial non - dominated sorting set at the current moment and the target non - dominated sorting set at the historical moment to determine the target non - dominated sorting set at the current moment;

[0015] Among the network active power loss target values of each fitness vector in the target non - dominated sorting set at the current moment, select the decision vector corresponding to the minimum network active power loss target value as the first vector element;

[0016] Among the voltage deviation target values of each fitness vector in the target non - dominated sorting set at the current moment, select the decision vector corresponding to the minimum voltage deviation target value as the second vector element;

[0017] According to the first vector element and the second vector element, construct the multi - objective dominant - position hippopotamus at the current moment;

[0018] Using the multi - objective dominant - position hippopotamus at the current moment and the multi - objective dominant - position hippopotamuses at multiple historical moments to update the initial hippopotamus population, generate an intermediate hippopotamus population, and count the update times in real - time;

[0019] Use the target non-dominated sorting set at the current moment as the target non-dominated sorting set at the new historical moment;

[0020] Based on the K-means clustering algorithm and the non-dominated sorting mechanism, use the distributed power source location and capacity multi-objective model to generate a new target non-dominated sorting set at the current moment according to the target non-dominated sorting set at the new historical moment and the intermediate hippopotamus population;

[0021] Determine whether the update times reach the preset times threshold;

[0022] If so, use the new target non-dominated sorting set at the current moment as the Pareto optimal solution set.

[0023] Optionally, the multi-objective optimization function corresponding to the distributed power source location and capacity multi-objective model is specifically:

[0024] ;

[0025] ;

[0026] ;

[0027] Among them, X represents the vector of control variables; f loss and f volt respectively represent the network active power loss and voltage deviation; T represents the time of one cycle; t represents the moment; b represents the total number of branches; j represents the branch; represents the current of branch j at moment t; r j represents the resistance of branch j; v represents the number of nodes; i represents the node; U N represents the rated voltage; represents the voltage of node i at moment t.

[0028] Optionally, the location and capacity constraint conditions include power balance constraint, node voltage constraint, branch current constraint, and reverse power flow constraint.

[0029] Optionally, it further includes:

[0030] If the update times do not reach the preset times threshold, then jump to execute the step of selecting the decision vector corresponding to the minimum network active power loss target value as the first vector element among the network active power loss target values of each fitness vector in the target non-dominated sorting set at the current moment, until the update times reach the preset times threshold;

[0031] Use the new target non-dominated sorting set at the current moment determined when the update times reach the preset times threshold as the Pareto optimal solution set.

[0032] Optionally, the sine power chaotic mapping mechanism is specifically as follows:

[0033] ;

[0034] where z h represents the h-th chaotic sequence; z h+1 represents the (h + 1)-th chaotic sequence; and are random numbers between 0 and 1; is the Gaussian perturbation of the standard normal distribution; the initial chaotic sequence z1 takes a random number between 0 and 1, and mod is the modulo operation.

[0035] A distributed power source siting and sizing planning device provided in the second aspect of the present invention is applied to the distributed power source siting and sizing planning method based on the improved hippopotamus optimization algorithm, and includes:

[0036] A construction module, configured to respond to a planning request, aiming at minimizing the network active power loss and voltage deviation, set the siting and sizing constraint conditions, and construct a distributed power source siting and sizing multi-objective model;

[0037] A solving module, configured to iteratively solve the distributed power source siting and sizing multi-objective model by using the improved hippopotamus optimization algorithm based on the K-means clustering algorithm and the multi-objective optimization mechanism, and generate a distributed power source siting and sizing planning scheme.

[0038] A computer device provided in the third aspect of the present invention includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of the distributed power source siting and sizing planning method based on the improved hippopotamus optimization algorithm as described in any one of the above.

[0039] A computer-readable storage medium provided in the fourth aspect of the present invention stores a computer program thereon. When the computer program is executed, the steps of the distributed power source siting and sizing planning method based on the improved hippopotamus optimization algorithm as described in any one of the above are implemented.

[0040] A computer program product provided in the fifth aspect of the present invention includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the distributed power source siting and sizing planning method based on the improved hippopotamus optimization algorithm as described in any one of the above.

[0041] From the above technical solutions, it can be seen that the present invention has the following advantages:

[0042] The above solution of the present invention provides a method for distributed power source siting and sizing planning based on an improved hippopotamus optimization algorithm. When distributed power source siting and sizing planning is required, with the goal of minimizing network active power loss and voltage deviation, siting and sizing constraint conditions are set, and a multi-objective model for distributed power source siting and sizing is constructed. Based on the K-means clustering algorithm and the multi-objective optimization mechanism, the improved hippopotamus optimization algorithm is used to iteratively solve the multi-objective model for distributed power source siting and sizing, and a distributed power source siting and sizing planning scheme is generated. Based on the above solution, the present invention uses the K-means clustering algorithm to improve the sorting efficiency, and combines the improved hippopotamus optimization algorithm to guide the solution to approach different target values, thereby improving the efficiency of obtaining the distributed power source siting and sizing planning scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of the steps of a method for distributed power source siting and sizing planning based on an improved hippopotamus optimization algorithm provided in Embodiment 1 of the present invention;

[0045] Figure 2 It is a schematic flow diagram of a method for distributed power source siting and sizing planning based on an improved hippopotamus optimization algorithm provided in Embodiment 1 of the present invention;

[0046] Figure 3 It is a structural block diagram of a device for distributed power source siting and sizing planning based on an improved hippopotamus optimization algorithm provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Embodiments of the present invention provide a method and device for distributed power source siting and sizing planning based on an improved hippopotamus optimization algorithm, which are used to solve the technical problem that the existing distributed power source siting and sizing planning methods result in low efficiency of distributed power source siting and sizing planning.

[0048] In order to make the invention objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] Please refer to Figure 1 , Figure 1 which is the step flow chart of a distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm provided in the first embodiment of the present invention.

[0050] A distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm provided by the present invention includes:

[0051] Step 101: Respond to a planning request, aiming at minimizing the network active power loss and voltage deviation, set the siting and sizing constraint conditions, and construct a multi-objective model for distributed power source siting and sizing.

[0052] The siting and sizing constraint conditions include power balance constraint, node voltage constraint, branch current constraint, and power flow reverse constraint.

[0053] It should be noted that for the multi-objective optimization function corresponding to the distributed power source siting and sizing multi-objective model, both the location and capacity of the distributed power source connected to the distribution network will have many impacts on the distribution network. The location of the distributed power source and the capacity of the distributed power source will affect the voltage value and the magnitude and distribution of the current. Among them, both the two objectives of the system operation network active power loss and voltage deviation can reflect the operation status when the distributed power source is connected to the distribution network. Therefore, a multi-objective function is jointly constructed to analyze them. The multi-objective optimization function corresponding to the distributed power source siting and sizing multi-objective model is specifically:[[]]

[0054] ;

[0055] ;

[0056] ;

[0057] Among them, X represents the vector of control variables, that is, the decision vector, including the installation location of the distributed power source and the capacity of the distributed power source; f loss and f volt respectively represent the network active power loss and voltage deviation; T represents the time of a cycle; t represents the moment; b represents the total number of branches; j represents the branch; represents the current of branch j at moment t; r j represents the resistance of branch j; v represents the number of nodes; i represents the node; U N represents the rated voltage; represents the voltage of node i at moment t.

[0058] Furthermore, after the distributed power source is connected to the system, it needs to satisfy the power balance constraint (through the installation location of the distributed power source and the capacity of the distributed power source, the current value and voltage value ), and the specific formula is as follows:

[0059] ;

[0060] Among them, and respectively represent the active power output and reactive power output of the distributed power source at node i; and respectively represent the active power load and reactive power load of node i; l represents the neighbor nodes of node i; 、 and respectively represent the conductance, susceptance and phase angle difference between node i and neighbor node l; is the voltage of node i; is the voltage of neighbor node l.

[0061] Furthermore, for the node voltage constraint, the voltage of each node in the distribution network should satisfy:

[0062] ;

[0063] Among them, U min and U max respectively represent the lower voltage limit and the upper voltage limit.

[0064] Furthermore, for the branch current constraint, the current of each branch in the distribution network should satisfy:

[0065] ;

[0066] Among them, I j,max represents the upper current limit of branch j.

[0067] Furthermore, for the power reverse flow constraint, after the distributed power source is connected to the distribution network, the power it generates should be consumed locally and should not be reversed to the upper-level power grid. The specific formula is as follows:

[0068] ;

[0069] Among them, b f represents the set of branches connecting to the substation; represents the current of branch j at time t.

[0070] Step 102: Based on the K-means clustering algorithm and the multi-objective optimization mechanism, use the improved hippopotamus optimization algorithm to iteratively solve the multi-objective model of distributed power source siting and sizing, and generate a distributed power source siting and sizing planning scheme.

[0071] The multi-objective optimization mechanism includes the non-dominated sorting mechanism and the sine-powered chaos mapping mechanism, that is, the SPM (Sine-Powered Map) chaos mapping.

[0072] The K-means clustering algorithm is the K-means clustering algorithm.

[0073] It should be noted that the traditional hippopotamus optimization algorithm is in the form of single-objective optimization. To achieve multi-objective optimization, the fitness function is extended from a scalar to a vector form, that is, the fitness value output by the present invention is in vector form, which is composed of the network active power loss and the voltage deviation as the corresponding objective values. The specific fitness function is as follows:

[0074] ;

[0075] where F is the fitness function, and f m represents the m-th objective value, and the objective values include the network active power loss objective value and the voltage deviation objective value.

[0076] Specifically, step 102 may include the following sub-steps:

[0077] Step S21: Generate a chaotic sequence based on the sine power chaotic mapping mechanism;

[0078] It should be noted that in the traditional hippopotamus optimization algorithm, the generation of the initial population depends on the generation of random numbers. Although this method can ensure the randomness of the hippopotamus population to a certain extent, its randomness has limitations, which may lead to insufficient population diversity. To solve this problem, the present invention introduces the SPM chaotic mapping to improve the quality of the initial population. Its mathematical formula is as follows:

[0079] The sine power chaotic mapping mechanism is specifically:

[0080] ;

[0081] ;

[0082] where z h represents the h-th chaotic sequence; z h+1 represents the (h + 1)-th chaotic sequence; and are random numbers from 0 to 1; is the Gaussian perturbation of the standard normal distribution; the initial chaotic sequence z1 takes a random number from 0 to 1, and mod is the modulo operation; is the i-th hippopotamus individual (the i-th solution) in the initial hippopotamus population, representing the decision vector; X min and X max represent the lower and upper limits of the control variables in the vector, respectively.

[0083] Step S22: Generate an initial hippopotamus population using a chaotic sequence, and based on the distributed generation siting and sizing multi-objective model, output the fitness vector corresponding to each hippopotamus individual according to the decision vector corresponding to each hippopotamus individual in the initial hippopotamus population; the fitness vector includes the network active power loss target value and the voltage deviation target value; the decision vector includes the installation location of the distributed generation and the capacity of the distributed generation.

[0084] It should be noted that each hippopotamus individual carries a decision vector, which includes two decision variables, namely the installation location of the distributed generation and the capacity of the distributed generation. Based on the installation location of the distributed generation and the capacity of the distributed generation, the current value and voltage value are determined. The distributed generation siting and sizing multi-objective model is used to calculate the network active power loss target value and the voltage deviation target value corresponding to each hippopotamus individual according to the current value and voltage value corresponding to each hippopotamus individual, so as to form a fitness vector.

[0085] Step S23: Use the K-means clustering algorithm to cluster each hippopotamus individual according to each fitness vector, and output multiple clusters.

[0086] It should be noted that when updating the archive, the K-means clustering algorithm is used to group the candidate solutions (each hippopotamus individual). By calculating similarity measures such as Euclidean distance, the solutions are divided into K clusters, and the solutions within each cluster are similar in terms of the target value. The goal of the K-means algorithm is to minimize the sum of squared errors within the clusters, and its mathematical expression is:

[0087] ;

[0088] where C k represents the k-th cluster after clustering; is the i-th solution, that is, the i-th decision vector; represents the center of the k-th cluster, and the calculation formula is as follows:

[0089] ;

[0090] By the above method, the solutions are divided into multiple clusters according to the similarity of the target values, that is, the K-means clustering algorithm is used to cluster each hippopotamus individual according to each fitness vector, and output multiple clusters, so as to quickly identify solution groups with similar characteristics and improve the screening efficiency.

[0091] Step S24: Use the non-dominated sorting mechanism to analyze each cluster and generate the initial non-dominated sorting set at the current moment.

[0092] It should be noted that after K-means clustering, in order to ensure that the finally selected solutions are both diverse and close to the Pareto front, the non-dominated sorting method (non-dominated sorting mechanism) is adopted. The specific steps are as follows:

[0093] 1) Put the solutions in each cluster after clustering into an unsorted set.

[0094] 2) Traverse all solutions, and find those solutions that are not dominated by any other solution to form the first level front1 (i.e., the initial non-dominated sorting set at the current moment). For each subsequent level front L, find those solutions that are only dominated by the solutions in . Continue the above steps until all solutions are assigned to a certain level.

[0095] Step S25: Screen the initial non-dominated sorting set at the current moment and the target non-dominated sorting set at the historical moment to determine the target non-dominated sorting set at the current moment;

[0096] It should be noted that the initial non-dominated sorting set at the current moment includes multiple solutions that are not dominated by any other solution, that is, the selected hippopotamus individuals. Finally, screen the initial non-dominated sorting set at the current moment and the target non-dominated sorting set at the historical moment to determine the target non-dominated sorting set at the current moment; for example, assume that the initial non-dominated sorting set at the current moment includes hippopotamus individual A, hippopotamus individual B, and hippopotamus individual C, and the target non-dominated sorting set at the historical moment includes hippopotamus individual D, hippopotamus individual E, and hippopotamus individual F. Compare the target values corresponding to any two hippopotamus individuals (including two objective function values, that is, the network active power loss target value and the voltage deviation target value). If the network active power loss target values corresponding to hippopotamus individual A and hippopotamus individual D are equal, and the voltage deviation target values corresponding to hippopotamus individual A and hippopotamus individual D are not equal, and the voltage deviation target value corresponding to hippopotamus individual A is less than the voltage deviation target value corresponding to hippopotamus individual D, then hippopotamus individual D is excluded, and hippopotamus individual A is retained in the target non-dominated sorting set at the current moment; if the target values corresponding to hippopotamus individual A and hippopotamus individual D are not equal, then both hippopotamus individual A and hippopotamus individual D are retained in the target non-dominated sorting set at the current moment, and then the target values corresponding to the retained hippopotamus individuals continue to be compared with the target values corresponding to other hippopotamus individuals until the target values corresponding to all solutions (hippopotamus individuals) are compared, so as to obtain the target non-dominated sorting set at the current moment.

[0097] Step S26: Among the network active power loss target values of each fitness vector in the target non-dominated sorting set at the current moment, select the decision vector corresponding to the minimum network active power loss target value as the first vector element;

[0098] Step S27: Among the voltage deviation target values of each fitness vector in the target non-dominated sorting set at the current moment, select the decision vector corresponding to the minimum voltage deviation target value as the second vector element;

[0099] It should be noted that in the traditional Hippopotamus Optimization Algorithm, the search process in the first stage relies on the globally optimal hippopotamus as the dominant hippopotamus to guide other solutions to approach it. However, in multi-objective optimization problems, there is no single globally optimal solution, but a set of Pareto optimal solutions. Therefore, the dominant hippopotamus is extended to a vector with the same number as the number of objectives, and each vector element represents the optimal value of a different objective. In the present invention, among the voltage deviation objective values of the fitness vectors corresponding to each hippopotamus individual in the current moment's objective non-dominated sorting set, the decision vector corresponding to the minimum network active power loss objective value is selected as the first vector element, and among the voltage deviation objective values of the fitness vectors corresponding to each hippopotamus individual in the current moment's objective non-dominated sorting set, the decision vector corresponding to the minimum voltage deviation objective value is selected as the second vector element. For example, the current moment's objective non-dominated sorting set includes hippopotamus individual A and hippopotamus individual B. Both hippopotamus individual A and hippopotamus individual B have corresponding network active power loss objective values and voltage deviation objective values. If the network active power loss objective value corresponding to hippopotamus individual A is less than the network active power loss objective value corresponding to hippopotamus individual B, then the decision vector corresponding to hippopotamus individual A is selected as the first vector element. Similarly, the second vector element can be obtained.

[0100] Step S28: Construct the multi-objective dominant hippopotamus at the current moment according to the first vector element and the second vector element;

[0101] It should be noted that the process of constructing the multi-objective dominant hippopotamus at the current moment can be expressed as:

[0102] ;

[0103] ;

[0104] Among them, X D represents the dominant hippopotamus; X D,m represents the m-th dominant hippopotamus, that is, the vector element, including: when m takes the value of 1, it is the first vector element; when m takes the value of 2, it is the second vector element.

[0105] Step S29: Update the initial hippopotamus population using the multi-objective dominant hippopotamus at the current moment and the multi-objective dominant hippopotamuses at multiple historical moments to generate an intermediate hippopotamus population, and statistically count the update times in real time;

[0106] It should be noted that the process of position update for female and juvenile hippos in the initial hippo population is the same as the existing update process, while male hippos in the initial hippo population will randomly select a dominant hippo (i.e., randomly select a dominant hippo from the multi-objective dominant hippos at the current moment and the multi-objective dominant hippos at multiple historical moments) for position update. The specific formula is as follows:

[0107] ;

[0108] where, X M,i represents the i-th male hippo; and are learning factors that control the speed at which the hippo approaches the dominant hippo and the intensity of random perturbation; the choice function represents randomly selecting a dominant hippo from the multi-objective dominant hippos at the current moment and the multi-objective dominant hippos at multiple historical moments.

[0109] Step S210: Take the objective non-dominated sorting set at the current moment as the objective non-dominated sorting set at the new historical moment;

[0110] Step S211: Based on the K-means clustering algorithm and non-dominated sorting mechanism, use the distributed power source location and capacity multi-objective model to generate a new objective non-dominated sorting set at the current moment according to the objective non-dominated sorting set at the new historical moment and the intermediate hippo population;

[0111] Step S212: Determine whether the number of updates reaches the preset number threshold;

[0112] Step S213: If so, take the new objective non-dominated sorting set at the current moment as the Pareto optimal solution set.

[0113] Step S214: Generate a distributed power source location and capacity planning scheme based on the Pareto optimal solution set.

[0114] It should be noted that in the traditional hippo optimization algorithm, the output result is a global optimal solution. However, in the context of multi-objective optimization, there is no single global optimal solution, but rather a set of solution sets called Pareto optimal solutions. Therefore, it is necessary to adjust the algorithm so that it can output the Pareto optimal solution set.

[0115] After the improved hippo optimization algorithm finishes solving, the output Pareto optimal solutions include:

[0116] 1) The decision variable matrix, an n×s matrix, where n is the number of Pareto optimal solutions and s is the dimension of the decision variable. The decision variable matrix Specifically:

[0117] ;

[0118] Among them, is the matrix element in the nth row and sth column of the decision variable matrix, representing the decision variable.

[0119] 2) Objective function value matrix , an n×m matrix:

[0120] ;

[0121] Among them, is the matrix element in the nth row and mth column of the objective function value matrix, representing the objective function value, that is, the network active power loss target value and the voltage deviation target value; m is the dimension of the objective function value matrix.

[0122] Through the above improvement, the multi-objective hippopotamus optimization algorithm can effectively output the Pareto optimal solution set, providing comprehensive reference information for decision-makers. Finally, planners select appropriate solutions (i.e., the installation locations and capacities of distributed power sources) from the Pareto optimal solution set according to the actual situation to obtain the distributed power source siting and sizing planning scheme.

[0123] Optionally, it further includes:

[0124] If the number of updates has not reached the preset number threshold, then jump to execute the step of selecting the decision vector corresponding to the minimum network active power loss target value as the first vector element among the network active power loss target values of each fitness vector in the current moment's objective non-dominated sorting set, until the number of updates reaches the preset number threshold;

[0125] Take the newly determined current moment's objective non-dominated sorting set when the number of updates reaches the preset number threshold as the Pareto optimal solution set.

[0126] It should be noted that if the number of updates has not reached the preset number threshold, then jump to execute step S26 until the number of updates reaches the preset number threshold. Take the newly determined current moment's objective non-dominated sorting set when the number of updates reaches the preset number threshold as the Pareto optimal solution set, and finally select appropriate solutions from the Pareto optimal solution set according to the requirements as the distributed power source siting and sizing planning scheme.

[0127] As a comparison of technical effects, it can be referred to in combination with the existing technology. As the penetration rate of renewable energy in the distribution network continues to increase, the problem of the location and capacity determination of distributed power sources has become the key to optimizing the operation of the power system. Although many methods have been proposed, there are still significant deficiencies in the existing technology in terms of multi-objective collaborative optimization and computational efficiency. Although existing research has proposed multi-objective models with objectives such as network loss and voltage quality, the dimensional differences between the objectives are large, and the weight allocation depends on manual experience, resulting in it being difficult for the optimization results to take into account the global optimality. Current mainstream optimization algorithms, such as genetic algorithms and particle swarm algorithms, are prone to falling into local optima or premature convergence when solving complex non-linear models. The hippopotamus optimization algorithm is a novel meta-heuristic optimization algorithm. Its unique balance mechanism of exploration and exploitation enables it to quickly and effectively obtain effective solutions in the search space. However, this algorithm is a single-objective optimization algorithm and cannot be directly applied to the solution of multi-objective models. This patent proposes a multi-objective location and capacity determination method based on an improved hippopotamus optimization algorithm. By introducing the SPM chaotic mapping, the K-means clustering algorithm, the non-dominated sorting mechanism, and the multi-objective dominant hippopotamus, the convergence of the algorithm, the diversity of the solution set, and the practicality of the model are significantly improved, effectively overcoming the limitations of the existing technology.

[0128] Furthermore, when determining the location and capacity of distributed power sources, it is necessary to improve the voltage quality as much as possible and reduce network losses, etc. When the current solutions use algorithms to solve, they often convert to single-objective optimization in a weighted form. When multiple constraint conditions are contradictory, single-objective optimization cannot effectively weigh them and may fall into no solution or sub-optimal solutions.

[0129] To address the above problems, the present invention proposes a distributed power source location and capacity determination planning method based on an improved hippopotamus optimization algorithm. The objective function is defined as a multi-objective function, the traditional hippopotamus optimization algorithm is extended to a multi-objective optimization algorithm, the SPM chaotic mapping is introduced to improve the quality of the initial population, the archive and the external population are combined to ensure that excellent solutions will not be missing, the non-dominated sorting is combined, and the search strategy of the algorithm is adjusted to adapt to the characteristics of multi-objective problems. Finally, the multi-objective optimal configuration of distributed power sources can be realized. Specifically, please refer to Figure 2 , first, build a mathematical model for the location and capacity determination of distributed power sources. Subsequently, determine the upper and lower limits of the variables. Among them, the lower limit of the location variable is 1, the upper limit is the number of nodes, and the variable is an integer. The lower limit of the capacity determination is 0, the upper limit is determined according to the actual situation, and the variable is of continuous type. Then, set the parameters of the hippopotamus optimization algorithm, specifically the population size, the number of iterations, and the archive size. Finally, use the improved hippopotamus optimization algorithm to solve, generate the Pareto optimal solution set, and the planner selects a suitable solution according to the actual situation.

[0130] In summary, in a distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm proposed by the present invention, a multi-objective optimization model for distributed power source siting and sizing is constructed, and a reverse power flow constraint is introduced therein to ensure that local consumption can be achieved after the distributed power source is connected. Aiming at the fact that the traditional hippopotamus optimization algorithm cannot solve multi-objective optimization problems, the single objective is extended to a multi-objective form, an external archive and a non-dominated sorting mechanism are introduced, and the K-means clustering algorithm is used to improve the sorting efficiency. The solutions are divided into multiple clusters according to the similarity of the objective values, so as to improve the efficiency of non-dominated sorting. Combined with the search strategy in the first stage of the improved hippopotamus optimization algorithm, it is ensured that the solutions can approach different objectives, and the efficiency of obtaining the Pareto optimal set is improved. In addition, the SPM chaotic mapping is introduced to improve the quality of the initial population, accelerate the convergence efficiency of the algorithm, and improve the randomness and ergodicity of the population, strengthening the global search ability. Finally, the improved hippopotamus optimization algorithm is used to solve the distributed power source siting and sizing model, and a Pareto optimal set of distributed power source siting and sizing is generated. Compared with the single-objective optimization algorithm, the present invention can find a set of Pareto optimal solutions, comprehensively reflect the trade-off between different objectives, and planners can select the most suitable distributed siting and sizing scheme from the Pareto optimal solution set according to specific requirements. At the same time, realizing the multi-objective optimal configuration of distributed power sources can effectively reduce the network loss of the system and improve the voltage quality.

[0131] In an embodiment of the present invention, the present invention provides a distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm. When distributed power source siting and sizing planning is required, first, with the minimization of network active power loss and voltage deviation as the objectives, siting and sizing constraint conditions are set, and a multi-objective model for distributed power source siting and sizing is constructed; based on the K-means clustering algorithm and the multi-objective optimization mechanism, the improved hippopotamus optimization algorithm is used to iteratively solve the multi-objective model for distributed power source siting and sizing, and a distributed power source siting and sizing planning scheme is generated. Based on the above scheme, the present invention uses the K-means clustering algorithm to improve the sorting efficiency, and combines the improved hippopotamus optimization algorithm to guide the solutions to approach different objective values, thereby improving the efficiency of obtaining the distributed power source siting and sizing planning scheme.

[0132] Please refer to Figure 3 , Figure 3 which is the structural block diagram of a distributed power source siting and sizing planning device provided in the second embodiment of the present invention.

[0133] A distributed power source siting and sizing planning device provided by the present invention is applied to the above-mentioned distributed power source siting and sizing planning method based on an improved hippopotamus optimization algorithm, and includes:

[0134] A construction module 301, which is used to respond to a planning request, set site selection and capacity determination constraint conditions with the goal of minimizing network active power loss and voltage deviation, and construct a multi-objective model for distributed power source site selection and capacity determination;

[0135] A solution module 302, which is used to iteratively solve the multi-objective model for distributed power source site selection and capacity determination based on the K-means clustering algorithm and multi-objective optimization mechanism, and adopt an improved hippopotamus optimization algorithm to generate a planning scheme for distributed power source site selection and capacity determination;

[0136] The solution module 302 is specifically used to generate a chaotic sequence based on the sine power chaos mapping mechanism;

[0137] Use the chaotic sequence to generate an initial hippopotamus population, and based on the multi-objective model for distributed power source site selection and capacity determination, output the fitness vector corresponding to each hippopotamus individual according to the decision vector corresponding to each hippopotamus individual in the initial hippopotamus population; the fitness vector includes the target value of network active power loss and the target value of voltage deviation; the decision vector includes the installation location of the distributed power source and the capacity of the distributed power source;

[0138] Adopt the K-means clustering algorithm to cluster each hippopotamus individual according to each fitness vector, and output multiple clusters;

[0139] Adopt the non-dominated sorting mechanism to analyze each cluster, and generate the initial non-dominated sorting set at the current moment;

[0140] Screen the initial non-dominated sorting set at the current moment and the target non-dominated sorting set at the historical moment to determine the target non-dominated sorting set at the current moment;

[0141] Among the target values of network active power loss of each fitness vector in the target non-dominated sorting set at the current moment, select the decision vector corresponding to the minimum target value of network active power loss as the first vector element;

[0142] Among the target values of voltage deviation of each fitness vector in the target non-dominated sorting set at the current moment, select the decision vector corresponding to the minimum target value of voltage deviation as the second vector element;

[0143] Construct a multi-objective dominant hippopotamus at the current moment according to the first vector element and the second vector element;

[0144] Use the multi-objective dominant hippopotamus at the current moment and the multi-objective dominant hippopotamuses at multiple historical moments to update the initial hippopotamus population, generate an intermediate hippopotamus population, and count the update times in real time;

[0145] Take the target non-dominated sorting set at the current moment as the target non-dominated sorting set at the new historical moment;

[0146] Based on the K - means clustering algorithm and non - dominated sorting mechanism, a multi - objective model for distributed power source location and capacity determination is used to generate a new current - moment target non - dominated sorting set according to the target non - dominated sorting set and the intermediate hippopotamus population at the new historical moment;

[0147] Judge whether the number of updates reaches the preset number - of - times threshold;

[0148] If so, take the new current - moment target non - dominated sorting set as the Pareto optimal solution set;

[0149] Based on the Pareto optimal solution set, generate a distributed power source location and capacity determination planning scheme.

[0150] Furthermore, the multi - objective optimization function corresponding to the multi - objective model for distributed power source location and capacity determination is specifically:

[0151] ;

[0152] ;

[0153] ;

[0154] Among them, X represents the vector of control variables; f loss and f volt respectively represent the network active power loss and voltage deviation; T represents the time of one cycle; t represents the moment; b represents the total number of branches; j represents the branch; represents the current of branch j at moment t; r j represents the resistance of branch j; v represents the number of nodes; i represents the node; U N represents the rated voltage; represents the voltage of node i at moment t.

[0155] Furthermore, the location and capacity determination constraint conditions include power balance constraint, node voltage constraint, branch current constraint, and reverse power flow constraint.

[0156] In an optional device embodiment, it further includes:

[0157] The first module is used to, if the number of updates does not reach the preset number - of - times threshold, jump to execute the step of selecting the decision vector corresponding to the minimum network active power loss target value as the first vector element among the network active power loss target values of each fitness vector in the current - moment target non - dominated sorting set until the number of updates reaches the preset number - of - times threshold;

[0158] The second module is used to take the new current - moment target non - dominated sorting set determined when the number of updates reaches the preset number - of - times threshold as the Pareto optimal solution set.

[0159] Further, the sine - power chaos mapping mechanism is specifically as follows:

[0160] ;

[0161] where z h represents the h - th chaos sequence; z h+1 represents the (h + 1)-th chaos sequence; and are random numbers from 0 to 1; is the Gaussian perturbation of the standard normal distribution; the initial chaos sequence z1 takes a random number from 0 to 1, and mod is the modulo operation.

[0162] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0163] The embodiment of the present invention also provides a computer device, including a memory and a processor, and a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the distributed power source location and capacity determination planning method based on the improved hippopotamus optimization algorithm as in any one of the above embodiments.

[0164] The embodiment of the present invention also provides a computer - readable storage medium, on which a computer program / instructions are stored, and when the computer program / instructions are executed by the processor, the steps of the distributed power source location and capacity determination planning method based on the improved hippopotamus optimization algorithm as in any one of the above embodiments are implemented.

[0165] The embodiment of the present invention also provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by the processor, the steps of the distributed power source location and capacity determination planning method based on the improved hippopotamus optimization algorithm as in any one of the above embodiments are implemented.

[0166] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical or other forms.

[0167] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed power source location and capacity determination planning method based on an improved hippopotamus optimization algorithm, characterized in that Including: Responding to the planning request, aiming at minimizing the network active power loss and voltage deviation, setting the location and capacity constraints, and constructing a multi-objective model for distributed power source location and capacity determination; Based on the K-means clustering algorithm and the multi-objective optimization mechanism, using the improved hippopotamus optimization algorithm to iteratively solve the multi-objective model for distributed power source location and capacity determination, and generating a planning scheme for distributed power source location and capacity determination; The multi-objective optimization mechanism includes a non-dominated sorting mechanism and a sine-power chaotic mapping mechanism; the step of using the improved hippopotamus optimization algorithm to iteratively solve the multi-objective model for distributed power source location and capacity determination based on the K-means clustering algorithm and the multi-objective optimization mechanism, and generating a planning scheme for distributed power source location and capacity determination includes: Generating a chaotic sequence based on the sine-power chaotic mapping mechanism; Using the chaotic sequence to generate an initial hippopotamus population, and based on the multi-objective model for distributed power source location and capacity determination, outputting a fitness vector corresponding to each hippopotamus individual according to the decision vector corresponding to each hippopotamus individual in the initial hippopotamus population; the fitness vector includes the network active power loss target value and the voltage deviation target value; the decision vector includes the installation location of the distributed power source and the capacity of the distributed power source; Using the K-means clustering algorithm to cluster each hippopotamus individual according to each fitness vector, and outputting multiple clusters; Using the non-dominated sorting mechanism to analyze the multiple clusters, and generating an initial non-dominated sorting set at the current moment; Screening the initial non-dominated sorting set at the current moment and the target non-dominated sorting set at the historical moment to determine the target non-dominated sorting set at the current moment; Among the network active power loss target values of each fitness vector in the target non-dominated sorting set at the current moment, selecting the decision vector corresponding to the minimum network active power loss target value as the first vector element; Among the voltage deviation target values of each fitness vector in the target non-dominated sorting set at the current moment, selecting the decision vector corresponding to the minimum voltage deviation target value as the second vector element; Constructing a multi-objective dominant hippopotamus at the current moment according to the first vector element and the second vector element; Using the multi-objective dominant hippopotamus at the current moment and the multi-objective dominant hippopotamuses at multiple historical moments to update the initial hippopotamus population, generating an intermediate hippopotamus population, and counting the update times in real time; Taking the target non-dominated sorting set at the current moment as the target non-dominated sorting set at the new historical moment; Based on the K-means clustering algorithm and the non-dominated sorting mechanism, using the multi-objective model for distributed power source location and capacity determination to generate a new target non-dominated sorting set at the current moment according to the target non-dominated sorting set at the new historical moment and the intermediate hippopotamus population; Judging whether the update times reach the preset times threshold; If so, taking the new target non-dominated sorting set at the current moment as the Pareto optimal solution set; Generating a planning scheme for distributed power source location and capacity determination based on the Pareto optimal solution set.

2. The method for distributed power source siting and sizing planning based on the improved hippopotamus optimization algorithm according to claim 1, characterized in that, The multi-objective optimization function corresponding to the multi-objective model for distributed power source location and capacity determination is specifically: ； ； ； Among them, X represents the vector of control variables; f loss and f volt respectively represent the active power loss of the network and the voltage deviation; T represents the time of one cycle; t represents the moment; b represents the total number of branches; j represents the branch; represents the current of branch j at moment t; r j represents the resistance of branch j; v represents the number of nodes; i represents the node; U N represents the rated voltage; represents the voltage of node i at moment t.

3. The distributed power source siting and sizing planning method based on the improved hippopotamus optimization algorithm according to claim 1, wherein The above-mentioned site selection and capacity determination constraint conditions include power balance constraint, node voltage constraint, branch current constraint, and reverse power flow constraint.

4. The method for distributed power source location and capacity determination planning based on the improved hippopotamus optimization algorithm according to claim 1, characterized in that, It further includes: If the number of updates does not reach the preset number threshold, jump to execute the step of selecting the decision vector corresponding to the minimum network active power loss target value as the first vector element among the network active power loss target values of each fitness vector in the target non-dominated sorting set at the current moment, until the number of updates reaches the preset number threshold; Take the target non-dominated sorting set at the new current moment determined when the number of updates reaches the preset number threshold as the Pareto optimal solution set.

5. The method for distributed power source location and capacity determination planning based on an improved hippopotamus optimization algorithm according to claim 1, wherein The sine power chaotic mapping mechanism is specifically as follows: ； Among them, z h represents the h-th chaotic sequence; z h+1 represents the (h + 1)-th chaotic sequence; and are random numbers from 0 to 1; is the Gaussian perturbation of the standard normal distribution; the initial chaotic sequence z1 takes a random number from 0 to 1, and mod is the modulo operation.

6. A distributed power source location and capacity determination planning device based on an improved hippopotamus optimization algorithm, which is applied to the distributed power source location and capacity determination planning method based on the improved hippopotamus optimization algorithm described in claim 1, and is characterized in that, It includes: A construction module, which is used to respond to the planning request, aims to minimize the network active power loss and voltage deviation, set the site selection and capacity determination constraint conditions, and construct a multi-objective model for distributed power source site selection and capacity determination; A solution module, which is used to iteratively solve the multi-objective model for distributed power source site selection and capacity determination by using the improved hippopotamus optimization algorithm based on the K-means clustering algorithm and the multi-objective optimization mechanism, and generate a planning scheme for distributed power source site selection and capacity determination.

7. A computer device, characterized in that, It includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor is caused to execute the steps of the distributed power source site selection and capacity determination planning method based on the improved hippopotamus optimization algorithm according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the distributed power source site selection and capacity determination planning method based on the improved hippopotamus optimization algorithm according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the distributed power source site selection and capacity determination planning method based on the improved hippopotamus optimization algorithm according to any one of claims 1-5.

Images