A method for site selection and capacity determination of distributed generation for distribution network loss reduction

Abstract

The present invention discloses a distributed power source location and capacity determination method for distribution network loss reduction, comprising first establishing a radial distribution network loss model based on the Newton-Raphson power flow algorithm; then establishing a location and capacity determination optimization model for distributed power sources in the distribution network with the optimization goal of minimizing power loss; finally, proposing a solution method based on a zebra optimization algorithm to solve the established location and capacity determination optimization model and complete the configuration of distributed power sources in the distribution network. By applying the zebra optimization algorithm, the present invention can solve the optimization model with a strong global search capability and high convergence, complete the optimal location selection and capacity configuration of a single or multiple distributed power sources, and achieve loss reduction in the distribution network.

Description

Technical Field

[0001] The present invention relates to a method for selecting a site and determining the capacity of a power source, in particular to a method for selecting a site and determining the capacity of a distributed power source for reducing the loss of a distribution network, and belongs to the technical field of reducing the loss of a distribution network. Background Art

[0002] At present, the operation mode of the distribution network is mainly based on large units and large power grids. This mode of power supply has poor flexibility and low reliability, and it is difficult to meet the power demand of the terminal load of the distribution network. It is also susceptible to fault disturbances, which can easily cause large-scale power outages or even paralysis of the system. In contrast, distributed power sources that supply power close to the user end have the advantages of small footprint, high power supply reliability, and flexible power generation methods. They can solve the problem of power supply for remote loads and loads in remote areas to make up for the defects of large power grids. After the distributed power source is connected to the distribution network, it will have a certain impact on the system operation mode and network loss by injecting appropriate active and reactive power into the system, which is closely related to its access location and capacity. Reasonable configuration of the type, location and capacity of distributed power sources is conducive to reducing network losses, improving power supply reliability, and improving the utilization efficiency of wind and solar energy.

[0003] In the prior art, a method for site selection and sizing of distributed power sources based on the HELM stability criterion is disclosed in the patent with publication number CN115207962A. The method includes establishing a site selection and sizing model of distributed power sources in the distribution network, using the HELM method to calculate the power flow of the distribution network containing distributed power sources as the power flow constraint condition of the distributed power source site selection and sizing optimization model, and using the HELM method to calculate the sensitivity of each order of node voltage as the inequality constraint condition and voltage stability index of the distributed power source site selection and sizing model; using a genetic algorithm to solve and determine the installation location and installation quantity PDGi of the distributed power source according to the objective function and the constraint condition. In the prior art, there are two common solutions to the site selection and sizing problem of distributed power sources: analytical method and meta-heuristic method; among them, the analytical method quantifies the impact of distributed power source configuration on the power system by establishing a mathematical model, and the common ones include improved analytical method, iterative analytical method and efficient analytical method. However, the analytical method is difficult to solve the site selection and sizing problem of multiple distributed power sources. For this reason, a site selection and sizing optimization model of distributed power sources can be established, and a meta-heuristic method can be used to solve the problem. Currently commonly used metaheuristic methods include the improved grey wolf algorithm, the firefly optimization algorithm, the hybrid genetic particle swarm algorithm, etc., but the existing methods still have certain limitations in global search capabilities and iterative convergence. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed power source site selection and sizing method for distribution network loss reduction in order to solve at least one of the above-mentioned technical problems. The method first establishes a distribution network loss model, then establishes a distributed power source site selection and sizing model, and finally proposes a solution method based on the zebra optimization algorithm to achieve distribution network loss reduction by reasonably configuring distributed power sources.

[0005] The present invention achieves the above-mentioned purpose through the following technical scheme: a distributed power source site selection and capacity determination method for distribution network loss reduction, the distributed power source site selection and capacity determination method comprising the following steps:

[0006] S1. Establish a radial distribution network loss model based on the Newton-Raphson power flow algorithm to quantify the power loss of the distribution network;

[0007] S2. Taking minimizing power loss as the optimization goal, establish a site selection and capacity optimization model for distributed generation in the distribution network;

[0008] S3. A solution method based on the zebra optimization algorithm is proposed, and the site selection and sizing optimization model established in step S2 is solved to complete the configuration of distributed power sources in the distribution network.

[0009] As a further solution of the present invention: In S1, a radial distribution network loss model based on the Newton-Raphson power flow algorithm is established, and the power loss of the distribution network is quantified according to the calculation results of the Newton-Raphson power flow algorithm.

[0010] As a further solution of the present invention: the establishment of a radial distribution network loss model specifically includes:

[0011] The power flow model of the radial distribution network is established as follows:

[0012]

[0013] Where: V i and V j is the voltage between nodes i and j, δ i and δ j is the voltage phase angle between nodes i and j, Y ij is the element Y in the node admittance matrix ij The value of , N is the number of nodes, θ ij is element Y ij The argument of

[0014] After l iterations, the voltage phase angle and amplitude are updated as follows:

[0015]

[0016] The Newton-Raphson power flow algorithm determines the convergence condition for each bus by determining the point at which the difference between the active power and the reactive power drops below the tolerance level defined in the following equation;

[0017]

[0018] Where: and Respectively represent the actual active and reactive power of node i;

[0019] The matrix form of the Newton-Raphson power flow algorithm is as follows:

[0020]

[0021] The power loss of the network is calculated using the following formula:

[0022]

[0023] As a further solution of the present invention: In S2, taking minimizing power loss as the optimization goal, considering power balance constraints, node voltage constraints and distributed power supply capacity constraints, an optimization model for the site selection and sizing of distributed power supplies in the distribution network is established to optimize the optimal location and optimal capacity of the distributed power supplies in the distribution network.

[0024] As a further solution of the present invention: the model structure of the site selection and capacity optimization model is as follows:

[0025] (1) Objective function

[0026] Taking the minimization of power loss of the distribution network as the optimization goal, the specific objective function is set as follows:

[0027]

[0028] Where: n C is the total number of branches in the system, R i is the resistance of branch i, I i is the current of the branch;

[0029] (2) Constraints

[0030] For the power flow model of the radial distribution network established by S1, the system operation needs to meet the following constraints:

[0031] Power balance constraints:

[0032]

[0033] Where: P Grid and Q Grid are the active and reactive power input into the distribution network, and are the active and reactive outputs of distributed generation, N DG is the total number of distributed generation, N l is the total number of load nodes, P d and Q d are the active and reactive loads of the system, Pl oss and Q loss are the active and reactive losses of the system respectively;

[0034] Node voltage constraints:

[0035] The voltage of each node in the distribution network has upper and lower limits. The voltage amplitude of each node should be kept within the specified range of 0.9-1.05 per unit, as shown in the following formula:

[0036]

[0037] Where: V Ci is the voltage at load node i, and Its upper and lower limits;

[0038] Distributed power generation capacity constraints:

[0039] The capacity of distributed power generation has upper and lower limits. According to the actual situation, the maximum value of power capacity DG i,max Not exceeding the total system load, minimum value DG i,min is 0;

[0040] DG i,min ≤DG i ≤DG i,max .

[0041] As a further solution of the present invention: in S3, the zebra optimization algorithm defines each zebra as a vector, representing a feasible solution to the optimization problem;

[0042] Initially, N feasible solutions are randomly selected in the search space, and the set of initial feasible solutions (zebra group) constitutes a matrix:

[0043]

[0044] Where: X represents the zebra group, X i represents an individual zebra, the number of zebras in the population is N, m represents the number of decision variables, and in the distributed generation location and capacity optimization model, represents the decision of each node in the distribution network to access the distributed generation, x i,j is the recommended value of the jth decision variable by the i-th zebra;

[0045] According to the objective function value corresponding to each feasible solution, the objective function vector is obtained:

[0046]

[0047] The zebra optimization algorithm iteratively updates the solution in two stages:

[0048] The first stage imitates the foraging behavior of zebras to ensure the iterative convergence of the solution algorithm:

[0049]

[0050] In the first stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is PZ represents the optimal individual in the process of foraging for zebras, that is, the solution with the minimum value of the existing objective function. Its jth element is PZ j ; The variable r is a random number in the range [0,1], and the value of I is randomly taken as an integer 1 or 2;

[0051] Through the update in the first stage, all solutions are brought closer to the existing optimal feasible solution, ensuring that the iteration can converge as quickly as possible.

[0052] The second stage imitates the behavior of zebras in resisting predators to ensure the global search capability of the solution algorithm.

[0053] As a further solution of the present invention: the second stage imitates the behavior of zebras to resist predators. When facing large predators, they adopt an escape strategy. Each member avoids the predator near his own position. The corresponding model is P ％ ≤0.5.

[0054] As a further solution of the present invention: the second stage imitates the behavior of zebras to resist predators. When facing small predators, they adopt an aggressive strategy, and other members of the group move towards the attacked individual. The corresponding model is P ％ >0.5;

[0055]

[0056] If the updated position reduces the value of the objective function, the new position is accepted;

[0057]

[0058] In the second stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is AZ represents the attacked individual, which appears randomly in the current population, and its hth element is AZ j ; t represents the number of iterations, T is the maximum number of iterations, and the constant R is 0.01; define P％ It is used to represent the probability of two situations occurring, and is a random number in the range of [0,1].

[0059] In the second stage, the existing solutions are updated with equal probability according to two different strategies, each containing a random factor, thus ensuring the global search capability of the algorithm;

[0060] Each iteration of the zebra optimization algorithm will update the results according to the above two stages, during which the smaller solution of the objective function will be retained until the maximum number T is reached;

[0061] Through two-stage search iterations, the optimization problem is solved with higher convergence and stronger global search capability.

[0062] The beneficial effects of the present invention are:

[0063] 1) The present invention establishes a network loss model to quantitatively characterize the power loss of the distribution network, clarifies the impact of distributed power configuration on power loss, and provides a theoretical basis for reducing the loss of the distribution network; with the goal of minimizing power loss, an optimization model for the site selection and sizing of distributed power sources in the distribution network is established, which can realize the site selection and sizing of single or multiple distributed power sources in the distribution network, and provide a method reference for the loss reduction planning of the distribution network; the model is solved by the zebra optimization algorithm, which can solve the optimization problem of site selection and sizing of distributed power sources in the distribution network with strong global search ability and high convergence;

[0064] 2) The present invention takes minimizing the power loss of the distribution network as the optimization goal, establishes a site selection and capacity optimization model for distributed power sources, and solves it through the zebra optimization algorithm. By applying this method, the optimization model can be solved with strong global search capability and high convergence, and the optimal location selection and capacity configuration of single or multiple distributed power sources can be completed, thereby reducing the power loss of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention;

[0066] Figure 2 A schematic diagram of an iterative process of a zebra optimization algorithm according to an embodiment of the present invention;

[0067] Figure 3 Schematic diagram of the IEEE 33-node power distribution network structure of an embodiment of the present invention;

[0068] Figure 4 It is an iterative convergence curve of the zebra optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Embodiment 1, as Figure 1 to Figure 2 As shown, this embodiment provides a distributed power source site selection and capacity determination method for reducing power distribution network losses, and the distributed power source site selection and capacity determination method includes the following steps:

[0071] S1. Establish a radial distribution network loss model based on the Newton-Raphson power flow algorithm to quantify the power loss of the distribution network;

[0072] S2. Taking minimizing power loss as the optimization goal (here, minimizing power loss refers to minimizing power loss of the distribution network), an optimization model for site selection and capacity determination of distributed generation in the distribution network is established;

[0073] S3. A solution method based on the zebra optimization algorithm is proposed, and the site selection and sizing optimization model established in step S2 is solved to complete the configuration of distributed power sources in the distribution network.

[0074] Embodiment 2: In addition to all the technical features of Embodiment 1, this embodiment also includes:

[0075] A radial distribution network loss model based on the Newton-Raphson power flow algorithm is established, and the power loss of the network is quantified according to the calculation results of the Newton-Raphson power flow algorithm.

[0076] The establishment of radial distribution network loss model includes:

[0077] The power flow model of the radial distribution network is established as follows:

[0078]

[0079] Where: V i and V j is the voltage between nodes i and j, δ i and δ j is the voltage phase angle between nodes i and j, Y ij is the element Y in the node admittance matrix ij The value of , N is the number of nodes, θ ij is element Y ij The argument of

[0080] After l iterations, the voltage phase angle and amplitude are updated as follows:

[0081]

[0082] The Newton-Raphson power flow algorithm determines the convergence condition for each bus by determining the point at which the difference between the active power and the reactive power drops below the tolerance level defined in the following equation;

[0083]

[0084] Where: and Respectively represent the actual active and reactive power of node i;

[0085] The matrix form of the Newton-Raphson power flow algorithm is as follows:

[0086]

[0087] The power loss of the network can be calculated using the following formula:

[0088]

[0089] Taking minimizing power loss as the optimization goal, considering power balance constraints, node voltage constraints and distributed power capacity constraints, an optimization model for the site selection and sizing of distributed power sources in distribution networks is established to optimize the optimal location and optimal capacity of distributed power sources in distribution networks.

[0090] The model structure of the site selection and capacity optimization model is as follows:

[0091] (1) Objective function

[0092] Taking minimizing power loss as the optimization goal, the specific objective function is set as follows:

[0093]

[0094] Where: n C is the total number of branches in the system, R i is the resistance of branch i, I i is the current of the branch;

[0095] (2) Constraints

[0096] For the power flow model of the radial distribution network established by S1, the system operation needs to meet the following constraints:

[0097] Power balance constraints:

[0098]

[0099] Where: P Grid and Q Grid are the active and reactive power input into the distribution network, and are the active and reactive outputs of distributed generation, N DG is the total number of distributed generation, N l is the total number of load nodes, P d and Q d are the active and reactive loads of the system, P loss and Q loss are the active and reactive losses of the system respectively;

[0100] Node voltage constraints:

[0101] The voltage of each node in the distribution network has upper and lower limits. The voltage amplitude of each node should be kept within the specified range of 0.9-1.05 per unit, as shown in the following formula:

[0102]

[0103] Where: V Ci is the voltage at load node i, and Its upper and lower limits;

[0104] Distributed power generation capacity constraints:

[0105] The capacity of distributed power generation has upper and lower limits. According to the actual situation, the maximum value of power capacity DG i,max Not exceeding the total system load, minimum value DG i,min is 0;

[0106] DG i,min ≤DG i ≤DG i,max

[0107] The zebra optimization algorithm imitates the individual behavior of zebras living in groups to solve optimization problems. It has strong global search capabilities and high iterative convergence speed, which is conducive to avoiding the problem of falling into local optimality due to discrete decision variables. It is suitable for solving distributed power generation site selection and sizing problems for distribution network loss reduction.

[0108] In S3, the zebra optimization algorithm defines each zebra as a vector, which represents a feasible solution to the optimization problem;

[0109] Randomly select N feasible solutions in the search space, and the set of initial feasible solutions (zebra group) constitutes a matrix:

[0110]

[0111] Where: X represents the zebra group, X irepresents an individual zebra, the number of zebras in the population is N, m represents the number of decision variables, and in the distributed generation location and capacity model, represents the decision of each node in the distribution network to access the distributed generation, x i,j is the recommended value of the jth decision variable by the i-th zebra;

[0112] According to the objective function value corresponding to each feasible solution, the objective function vector is obtained:

[0113]

[0114] The zebra optimization algorithm iteratively updates the solution in two stages:

[0115] The first stage imitates the foraging behavior of zebras to ensure the iterative convergence of the solution algorithm:

[0116]

[0117] In the first stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is PZ represents the optimal individual in the process of foraging for zebras, that is, the solution with the minimum value of the existing objective function. Its jth element is PZ j ; The variable r is a random number in the range [0,1], and the value of I is randomly taken as an integer 1 or 2;

[0118] Through the update in the first stage, all solutions are brought closer to the existing optimal feasible solution, ensuring that the iteration can converge as quickly as possible.

[0119] The second stage imitates the behavior of zebras in resisting predators to ensure the global search capability of the solution algorithm.

[0120] The second stage imitates the behavior of zebras in resisting predators. When facing large predators, they adopt an escape strategy. Each member avoids the predator near his own position. The corresponding model is P ％ ≤0.5.

[0121] The second stage imitates the behavior of zebras in resisting predators. When facing small predators, they adopt an aggressive strategy. Other members of the group move toward the attacked individual. The corresponding model is P s >0.5;

[0122]

[0123] If the updated position reduces the value of the objective function, the new position is accepted;

[0124]

[0125] In the second stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is AZ represents the attacked individual, which appears randomly in the current population, and its jth element is AZ j ; t represents the number of iterations, T is the maximum number of iterations, and the constant R is 0.01; define P ％ It is used to represent the probability of two situations occurring, and is a random number in the range of [0,1].

[0126] In the second stage, the existing solutions are updated with equal probability according to two different strategies, each containing a random factor, thus ensuring the global search capability of the algorithm;

[0127] Each iteration of the zebra optimization algorithm will update the results according to the above two stages, during which smaller solutions to the objective function will be retained until the maximum number T is reached; through two-stage search iterations, the optimization problem is solved with higher convergence and stronger global search capabilities.

[0128] Embodiment three, as Figure 3 and Figure 4 As shown, this embodiment provides a distributed power generation site selection and capacity determination method for distribution network loss reduction, with the goal of minimizing power loss, and performs site selection and capacity determination for distributed photovoltaic power generation in the IEEE 33-node distribution network system. The network structure is as follows Figure 3 As shown, the active power and reactive power of the load are 4750kW and 2300kVar respectively, and the basic power loss of the system is 202.3kW. Based on the distribution network loss model and distributed generation site selection and sizing model constructed in steps 1 and 2, the distributed generation is configured with the minimization of system power loss as the optimization goal, and the population size of the zebra optimization algorithm (i.e., the number of initial feasible solutions) N = 100 and the maximum number of iterations T = 500 are set. In particular, for the case of deploying only a single distributed generation, the iterative convergence speed is faster, taking N = 20 and T = 50.

[0129] Table 1 shows the site selection and capacity determination results of distributed photovoltaic power generation in the distribution network and its loss reduction effect. The results show that the access of distributed power sources can significantly reduce the power loss of the distribution network, and the loss reduction effect of multiple distributed power sources access is more obvious than that of only a single distributed power source access.

[0130] Table 1 Site selection and capacity determination results and loss reduction effects of distributed photovoltaic power generation in distribution networks

[0131]

[0132] Figure 4The iterative convergence curve of the solution using the zebra optimization algorithm is shown. It can be seen from the figure that the actual number of iterative convergence times for solving the optimization problem in this embodiment is within 20 times, which reflects the outstanding advantage of the zebra optimization algorithm in convergence.

[0133] According to an embodiment of the present invention, a distributed power generation site selection and sizing method for distribution network loss reduction is proposed. By establishing a site selection and sizing model for distributed power generation in the distribution network and solving it using a zebra optimization algorithm, the optimal configuration of distributed power generation in the distribution network can be achieved and the power loss of the network can be significantly reduced.

[0134] Working principle: First, a radial distribution network loss model based on the Newton-Raphson power flow algorithm is established; then, with minimizing power loss as the optimization goal, a site selection and sizing model for distributed power sources in the distribution network is established; finally, a solution method based on the zebra optimization algorithm is proposed to solve the established optimization model and complete the configuration of distributed power sources in the distribution network.

[0135] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0136] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A distributed power generation site selection and capacity determination method for distribution network loss reduction, characterized in that: The distributed power generation site selection and capacity determination method comprises the following steps: S1. Establish a radial distribution network loss model based on the Newton-Raphson power flow algorithm to quantify the power loss of the distribution network; S2. Taking minimizing power loss as the optimization goal, establish a site selection and capacity optimization model for distributed generation in the distribution network; S3. A solution method based on the zebra optimization algorithm is proposed, and the site selection and sizing optimization model established in step S2 is solved to complete the configuration of distributed power sources in the distribution network.

2. The method for site selection and capacity determination of distributed power sources according to claim 1, characterized in that: In S1, a radial distribution network loss model based on the Newton-Raphson power flow algorithm is established, and the power loss of the distribution network is quantified according to the calculation results of the Newton-Raphson power flow algorithm.

3. The method for site selection and capacity determination of distributed power sources according to claim 2 is characterized in that: The establishment of the radial distribution network loss model specifically includes: The power flow model of the radial distribution network is established as follows: Where: V i and V j is the voltage between nodes i and j, δ i and δ j is the voltage phase angle between nodes i and j, Y ij is the element Y in the node admittance matrix ij The value of , N is the number of nodes, θ ij is element Y ij The argument of After l iterations, the voltage phase angle and amplitude are updated as follows: |V i | (l+1) =V i (l) +Δ|V i | The Newton-Raphson power flow algorithm determines the convergence condition for each bus by determining the point at which the difference between the active power and the reactive power drops below the tolerance level defined in the following equation; ΔP i =P i sp -P i Where: P i sp and Respectively represent the actual active and reactive power of node i; The matrix form of the Newton-Raphson power flow algorithm is as follows: The power loss of the network is calculated by the following formula:

4. The method for site selection and capacity determination of distributed power sources according to claim 3 is characterized in that: In S2, minimizing power loss is taken as the optimization goal, and the power balance constraint, node voltage constraint and distributed power supply capacity constraint are considered to establish a site selection and capacity determination optimization model for distributed power supplies in the distribution network, and optimize and determine the optimal location and optimal capacity of distributed power supplies in the distribution network.

5. The method for site selection and capacity determination of distributed power sources according to claim 4 is characterized in that: The model structure of the site selection and capacity optimization model is as follows: (1) Objective function Taking minimizing power loss as the optimization goal, the objective function is set as follows: Where: n L is the total number of branches in the system, R i is the resistance of branch i, I i is the current of the branch; (2) Constraints For the power flow model of the radial distribution network established by S1, the system operation needs to meet the following constraints: Power balance constraints: Where: P Grid and Q Grid are the active and reactive power input into the distribution network, and are the active and reactive outputs of distributed generation, N DG is the total number of distributed generation, N l is the total number of load nodes, P d and Q d are the active and reactive loads of the system, P loss and Q loss are the active and reactive losses of the system respectively; Node voltage constraints: The voltage of each node in the distribution network has upper and lower limit constraints, and the voltage amplitude of each node is maintained within the specified range of 0.9-1.05 per unit, as shown in the following formula: Where: V Li is the voltage at load node i, and Its upper and lower limits; Distributed power generation capacity constraints: The capacity of distributed power generation has upper and lower limits. According to the actual situation, the maximum value of power capacity DG i,max Not exceeding the total system load, minimum value DG i,min is 0; DG i,min ≤DG i ≤DG i,max 。 6. The method for site selection and capacity determination of distributed power sources according to claim 1, characterized in that: In S3, the zebra optimization algorithm defines each zebra as a vector, representing a feasible solution to the optimization problem; Randomly select N feasible solutions in the search space, and the set of initial feasible solutions forms a matrix: Where: X represents the zebra group, X i represents an individual zebra, the number of zebras in the population is N, m represents the number of decision variables, and in the distributed generation location and capacity optimization model, represents the decision of each node in the distribution network to access the distributed generation, x i,j is the recommended value of the jth decision variable by the i-th zebra; According to the objective function value corresponding to each feasible solution, the objective function vector is obtained: The zebra optimization algorithm iteratively updates the solution in two stages: The first stage imitates the foraging behavior of zebras to ensure the iterative convergence of the solution algorithm: In the first stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is PZ represents the optimal individual in the process of foraging for zebras, that is, the solution with the minimum value of the existing objective function. Its jth element is PZ j ; The variable r is a random number in the range [0,1], and the value of I is randomly taken as an integer 1 or 2; Through the update in the first stage, all solutions are brought closer to the existing optimal feasible solution, ensuring that the iteration can converge; The second stage imitates the behavior of zebras in resisting predators to ensure the global search capability of the solution algorithm.

7. The method for site selection and capacity determination of distributed power sources according to claim 6, characterized in that: The second stage imitates the behavior of zebras in resisting predators. When facing large predators, they adopt an escape strategy. Each member avoids the predator near his own position. The corresponding model is P s ≤0.

5.

8. The method for site selection and capacity determination of distributed power sources according to claim 7 is characterized in that: The second stage imitates the behavior of zebras in resisting predators. When facing small predators, they adopt an aggressive strategy, and other members of the group move towards the attacked individual. The corresponding model is P s >0.5; If the updated position reduces the value of the objective function, the new position is accepted; In the second stage, the position of zebra i is updated to is the value of the jth element in the matrix, and the corresponding objective function is AZ represents the attacked individual, which appears randomly in the current population, and its jth element is AZ j ; t represents the number of iterations, T is the maximum number of iterations, and the constant R is 0.01; define P s It is used to represent the probability of two situations occurring, and is a random number in the range of [0,1]. In the second stage, the existing solutions are updated with equal probability according to two different strategies, each containing a random factor, thus ensuring the global search capability of the algorithm; Each iteration of the zebra optimization algorithm will update the results according to the above two stages, during which the smaller solution of the objective function will be retained until the maximum number T is reached; Through two-stage search iterations, the optimization problem is solved with convergence and global search capabilities.

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