Power distribution network simulation method and device, computer equipment and readable storage medium

Abstract

The invention relates to a power distribution network simulation method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring an equivalent model corresponding to each distributed photovoltaic connected to a target power distribution network; according to the power related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental influence data of the target power distribution network, clustering the distributed photovoltaics to obtain at least one cluster; for any clustering cluster, determining equivalent data corresponding to the clustering cluster according to the power related data corresponding to each distributed photovoltaic in the clustering cluster; the equivalent data is used for performing simulation processing on the target power distribution network. By adopting the method, effective simulation of the high-proportion distributed photovoltaic grid-connected power distribution network can be realized.

Description

Technical Field

[0001] This application relates to the field of power system simulation technology, and in particular to a power distribution network simulation method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] In the construction of new power systems, the high proportion of distributed photovoltaic grid connection is driving the evolution of distribution networks into complex networks with deep integration of power generation, grid, load and storage.

[0003] Traditional averaging modeling methods effectively improve simulation speed in low-proportion photovoltaic (PV) grid connection scenarios by simplifying the model structure. However, they exhibit significant limitations when faced with high-proportion distributed PV grid connection: First, the surge in system dynamic characteristics leads to an exponential increase in model complexity, resulting in excessive computational burden and a sharp drop in simulation efficiency. Second, averaging processing struggles to accurately characterize the dynamic characteristics of PV clusters and fails to reflect the true operating state of the system. Third, traditional methods are not adaptable enough to topology changes and cannot support real-time simulation requirements, necessitating solutions. Summary of the Invention

[0004] Therefore, it is necessary to provide a power distribution network simulation method, device, computer equipment, and readable storage medium to address the above-mentioned technical problems, which can effectively simulate power distribution networks with a high proportion of distributed photovoltaic grid connection.

[0005] Firstly, this application provides a power distribution network simulation method, including:

[0006] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0007] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0008] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0009] In one embodiment, based on the power-related data output by the equivalent models corresponding to different distributed photovoltaic (PV) systems and the environmental impact data of the target distribution network, the distributed PV systems are clustered to obtain at least one cluster, including:

[0010] For any distributed photovoltaic system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed photovoltaic system;

[0011] The electrical distance between different distributed photovoltaic systems is determined based on the equivalent active power and equivalent reactive power.

[0012] Based on each electrical distance, each distributed photovoltaic system is clustered to obtain at least one cluster.

[0013] In one embodiment, determining the electrical distance between different distributed photovoltaic systems based on active power and reactive power includes:

[0014] Determine the sensitivity data corresponding to distributed photovoltaic power based on active power and reactive power;

[0015] Based on the sensitivity data corresponding to different distributed photovoltaic systems, the electrical distance between different distributed photovoltaic systems is determined.

[0016] In one embodiment, the distributed photovoltaic systems are clustered according to their electrical distances to obtain at least one cluster, including:

[0017] Based on the electrical distances, select a target number of cluster centers from each distributed photovoltaic system;

[0018] Based on the similarity between each distributed photovoltaic (PV) and the cluster center, the other distributed PVs are classified to obtain at least one cluster; among them, the other distributed PVs are distributed PVs that are not cluster centers.

[0019] In one embodiment, power-related data includes equivalent active power and equivalent reactive power; equivalent data includes equivalent active power and equivalent reactive power; correspondingly, based on the power-related data corresponding to each distributed photovoltaic power generation unit in the cluster, the equivalent data corresponding to the cluster is determined, including:

[0020] The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster.

[0021] The reactive power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent reactive power corresponding to the cluster.

[0022] In one embodiment, the power-related data includes equivalent voltage; the equivalent data includes equivalent impedance; accordingly, based on the power-related data corresponding to each distributed photovoltaic in the cluster, the equivalent data corresponding to the cluster is determined, including:

[0023] Based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster, the equivalent impedance corresponding to the cluster is determined.

[0024] In one embodiment, the equivalent impedance corresponding to a cluster is determined based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster, including:

[0025] The first impedance corresponding to the cluster is determined based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster.

[0026] The first voltage difference between the cluster and the grid connection point is determined based on the second impedance and equivalent active power corresponding to the cluster.

[0027] The second voltage difference between the cluster and the grid connection point is determined based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster.

[0028] With the goal of making the first voltage difference equal to the second voltage difference, the second impedance corresponding to the cluster is determined, and the first impedance and the second impedance are used as the equivalent impedance corresponding to the cluster. Secondly, this application also provides a power distribution network simulation device, including:

[0029] The acquisition module is used to acquire the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0030] The first determining module is used to cluster each distributed photovoltaic (PV) system based on the power-related data output by the equivalent model corresponding to different distributed PV systems and the environmental impact data of the target distribution network, thereby obtaining at least one cluster.

[0031] The second determining module is used to determine the equivalent data corresponding to any cluster based on the power-related data of each distributed photovoltaic in the cluster; the equivalent data is used to simulate the target distribution network.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0033] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0034] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0035] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0038] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0039] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0041] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0042] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0043] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0044] The aforementioned power distribution network simulation method, apparatus, computer equipment, and readable storage medium acquire equivalent models corresponding to each distributed photovoltaic (PV) system connected to the target power distribution network. Based on the power-related data output from the equivalent models of different distributed PV systems and the environmental impact data of the target power distribution network, the distributed PV systems are clustered to obtain at least one cluster. For any cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed PV system within the cluster. This equivalent data is used for simulation processing of the target power distribution network. In this process, the distributed PV grid-connected system is first simplified into an equivalent model, laying the foundation for subsequent equivalent modeling work. This simplification step significantly reduces the initial complexity of the model without losing key information. Subsequently, clustering allows multiple distributed PV nodes to be equivalent to a small number of manageable PV power plant equivalent models, significantly improving the simulation speed while ensuring simulation accuracy. This method not only effectively solves the model complexity problem faced by distributed PV grid-connected systems with high-proportion connections but also provides an efficient and accurate modeling tool for the dynamic characteristic analysis of power distribution networks. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a power distribution network simulation method in one embodiment;

[0047] Figure 2 This is a flowchart illustrating the cluster determination steps in one embodiment;

[0048] Figure 3 This is a flowchart illustrating the steps for determining equivalent data in one embodiment;

[0049] Figure 4 This is a flowchart illustrating the steps for determining the equivalent impedance in one embodiment;

[0050] Figure 5 This is a structural block diagram of a power distribution network simulation device in one embodiment;

[0051] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] Before describing the embodiments of this application, it should be noted that a high proportion of distributed photovoltaic (PV) grid connection typically refers to a stage where the distributed PV grid-connected penetration rate reaches 30% to 50% or higher. At this stage, PV installed capacity increases significantly, and the connection method shifts from localized centralized grid connection to multi-regional centralized and distributed grid connection. Therefore, in addition to the proportion of power generation to total load, the number and distribution of connection points are also very important; more connection points mean higher decentralization and flexibility of PV power generation.

[0054] In distribution network systems with high penetration of distributed photovoltaic (PV) power, the traditional radial structure undergoes significant changes due to the integration of numerous grid-connected PV systems into multiple PV nodes. Each grid-connected system contains a large number of power electronic components, whose rapid dynamic characteristics conflict with the long-term operational characteristics of the power grid, leading to increased system time-scale complexity. Simultaneously, the intermittency and volatility of PV power generation exacerbate system stochasticity, further increasing operational complexity.

[0055] From the perspective of grid connection points, the integration of a high proportion of photovoltaic (PV) power significantly impacts voltage quality and stability. The rapid response of inverters causes voltage fluctuations, harmonics degrade power quality, intermittent PV power threatens voltage stability, and numerous inverters alter system impedance characteristics, weakening voltage regulation capabilities. From a network architecture perspective, PV integration alters the unidirectional power flow pattern, potentially triggering reverse power flow. Changes in operating modes during short-circuit faults increase system uncertainty and complexity. Traditional power flow and short-circuit analysis methods may face convergence issues due to enhanced system nonlinearity, PV output fluctuations, reverse power flow, and dynamic parameter changes. With the integration of a large number of distributed PV systems, the workload of averaging modeling increases exponentially, leading to a greater complexity of the entire network model. Therefore, this application provides an equivalent modeling method based on clustering, aiming to reduce model complexity and further accelerate simulation speed.

[0056] In one exemplary embodiment, such as Figure 1 As shown, a power distribution network simulation method is provided. Taking the application of this method to a simulation terminal as an example, the method includes the following steps:

[0057] S110, obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network.

[0058] The target distribution network can be a distribution network that can be connected to distributed photovoltaic (PV) systems. In this embodiment, the target distribution network usually refers to a distribution network with a large number of externally connected distributed PV systems, that is, a high proportion of distributed PV systems are connected to the distribution network.

[0059] In one alternative embodiment, in order to improve the operating efficiency of the simulation terminal, the equivalent model corresponding to each distributed photovoltaic can be constructed by a dedicated equivalent model construction device, and the simulation terminal only needs to obtain the equivalent model corresponding to different distributed photovoltaics.

[0060] Since the characteristics of distributed photovoltaic (PV) grid connection are divided into dynamic characteristics under normal conditions and transient characteristics under external faults, both of which are related to the inverter's control objectives, the inverter's output is the primary consideration when constructing the equivalent model. Based on this, equivalent models for different distributed PV systems can be constructed in the following ways:

[0061] In the control strategy for normal inverter operation, the inverter's output active and reactive power remain stable through the combined action of the outer and inner voltage loops, while simultaneously ensuring DC-side voltage stability. Therefore, the system's output characteristics under normal operation can be described as the active current (i) output at the grid connection point. d ) and reactive current (i q The relationship between the power and the set constant power reference value is shown in the following formula.

[0062] ;

[0063] In the formula, This indicates the preset active power reference value; Indicates the preset reactive power reference value; i d Indicates the active current (direct-axis component) output at the grid connection point; i q This represents the reactive current (quadrature axis component) output at the grid connection point. This indicates the output voltage at the grid connection point.

[0064] If the inverter enters a current-limiting state under normal conditions due to a low grid voltage, it needs to increase its output current to maintain the same power output as the voltage decreases. If the inverter's current limit is reached, the active power output will begin to decrease. The inverter's output characteristic is no longer a constant power characteristic. At this point, the inverter's output active power decreases due to the lower grid connection point voltage. Its output characteristic is shown in the following formula, which is called the current-limiting characteristic under normal conditions:

[0065] ;

[0066] In the formula, i q Indicates the reactive current (quadrature-axis component) output at the grid connection point; i d This represents the active current (direct-axis component) output at the grid connection point; I represents the output current of the inverter. The angle of the output current.

[0067] If a low-voltage ride-through occurs, and current-limiting mode is entered during reactive current compensation, i q A large output will cause i d The decrease in the active current (i) at this time d ) and reactive current (i q The characteristics are shown in the following formula, which is called the current limiting characteristic under low voltage ride-through.

[0068] ;

[0069] In the formula, i q This represents the reactive current (quadrature axis component) output at the grid connection point. Indicates the maximum allowable output current threshold; I N Indicates the rated current; U T This indicates the rated voltage.

[0070] In this embodiment, the distributed photovoltaic grid-connected system can be treated as equivalent to the DC side and AC side, focusing only on the input and output of the model, and no longer concerned with the specific internal circuits, working principles and control strategies.

[0071] In the equivalent model, the inputs to the DC equivalent model are the external conditions of the photovoltaic array and the maximum power point voltage (U) of the photovoltaic node. m ) Current (I m The specifications of the photovoltaic array are shown in the following mathematical model:

[0072] ;

[0073] In the formula, U dc Indicates DC side voltage; N P and N S P represents the number of distributed photovoltaic cells connected in parallel and series in the photovoltaic array, respectively. i,max Indicates the maximum output power of a single photovoltaic node; I m U represents the maximum current of a photovoltaic node (i.e., distributed photovoltaic power); m Indicates the maximum voltage of the photovoltaic node;

[0074] There is a connection U between DC and AC models dc Furthermore, the control parameters in the inverter and the parameter values ​​in the filter are all related to the output of the grid-connected system. Therefore, for any distributed photovoltaic system, the corresponding equivalent model formula is as follows:

[0075] ;

[0076] In the formula, U dc Indicates DC side voltage; U pcc I pcc i represents the input voltage and current at the grid connection point, respectively. d i q These represent the direct-axis and quadrature-axis output currents of the grid-connected inverter, respectively; S represents light intensity; T represents temperature; R represents resistance; C represents capacitance; L represents inductance; k ip k ii k vp k vi These represent the inverter control parameters; Indicates the output voltage at the grid connection point; U g Pdc represents the grid voltage; Pdc represents the DC power at the grid connection point.

[0077] S120. Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, the distributed photovoltaics are clustered to obtain at least one cluster.

[0078] Environmental impact data may include light intensity, temperature, and capacity.

[0079] For example, in this embodiment, the power-related data output by the equivalent models corresponding to different distributed photovoltaics, as well as the environmental impact data of the target distribution network, can be input into a pre-trained clustering model to obtain at least one cluster.

[0080] S130, for any cluster, determine the equivalent data corresponding to the cluster based on the power-related data of each distributed photovoltaic in the cluster.

[0081] The equivalent data is used to simulate the target distribution network.

[0082] For example, in this embodiment, a formula between power-related data and equivalent data can be predetermined, and the power-related data can be substituted into the corresponding formula to obtain the equivalent data corresponding to the cluster.

[0083] In the aforementioned power distribution network simulation method, equivalent models corresponding to each distributed photovoltaic (PV) system connected to the target power distribution network are obtained. Based on the power-related data output by the equivalent models of different distributed PV systems and the environmental impact data of the target power distribution network, each distributed PV system is clustered to obtain at least one cluster. For any cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed PV system within the cluster. The equivalent data is used for simulation processing of the target power distribution network. In this process, the distributed PV grid-connected system is first simplified into an equivalent model, laying the foundation for subsequent equivalent modeling work. This simplification step greatly reduces the initial complexity of the model without losing key information. Subsequently, clustering can transform multiple distributed PV nodes into equivalent models of a small number of manageable PV power plants, significantly improving the simulation speed while ensuring simulation accuracy. This method not only effectively solves the model complexity problem faced by distributed PV grid-connected systems with high-proportion connections but also provides an efficient and accurate modeling tool for the dynamic characteristic analysis of power distribution networks.

[0084] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the process of clustering each distributed photovoltaic (PV) system with power-related data output from the equivalent models corresponding to different distributed PV systems and environmental impact data of the target distribution network to obtain at least one cluster is refined.

[0085] See Figure 2 The steps for determining clusters shown include:

[0086] S210: For any distributed photovoltaic system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed photovoltaic system.

[0087] S220 determines the electrical distance between different distributed photovoltaic systems based on equivalent active power and equivalent reactive power.

[0088] Electrical distance refers to the minimum safe clearance between live parts or between a live part and a grounded component during live-line work, to ensure that flashover discharge does not occur under the maximum working voltage or overvoltage, thereby ensuring the safety of operators.

[0089] In one alternative implementation, the equivalent active power and equivalent reactive power can be input into a pre-trained distance determination model to obtain the electrical distance between different distributed photovoltaic systems.

[0090] In another alternative implementation, the sensitivity data corresponding to the distributed photovoltaic system can be determined based on the active power and reactive power; and the electrical distance between different distributed photovoltaic systems can be determined based on the sensitivity data corresponding to different distributed photovoltaic systems.

[0091] For example, considering that the voltage of photovoltaic node i is affected by the active and reactive power of photovoltaic node j, a voltage-active power sensitivity index S is defined. ij,vp Voltage-Reactive Power Sensitivity Index S ij,vq It can be determined using the following formula:

[0092] ;

[0093] In the formula, S ij,vp and S ij,vq These represent the active power sensitivity index and reactive power sensitivity index for distributed photovoltaic systems, respectively; P j Q j V represents the active power and reactive power of photovoltaic node j (i.e., distributed photovoltaic j), respectively; i This represents the amplitude of photovoltaic node i (i.e., distributed photovoltaic i).

[0094] Furthermore, the electrical distance between different distributed photovoltaic systems is determined based on the following formula:

[0095] ;

[0096] In the formula, l ij r represents the electrical distance between photovoltaic node i and photovoltaic node j; ix and r jx , i and j are the electrical distances between photovoltaic nodes i and j and the remaining photovoltaic nodes x in the network, respectively; n represents the total number of photovoltaic nodes.

[0097] Where, r ix It can be determined based on the following formula:

[0098] ;

[0099] In the formula, r ij S represents the electrical distance between photovoltaic node i and photovoltaic node j; ij,vp and S ij,vq These represent the active power sensitivity index and reactive power sensitivity index for distributed photovoltaic systems, respectively; s ij To simultaneously consider the voltage-power combined sensitivity between two photovoltaic nodes, taking into account both active and reactive power, s ii This represents the overall sensitivity of photovoltaic node i itself. When r... ij When =0, that is, s ij =s ii , indicating that the distance between photovoltaic node i and photovoltaic node j is 0; when r ij When ≠0, the larger its absolute value, the greater the electrical distance between the two photovoltaic nodes.

[0100] S230, based on each electrical distance, cluster each distributed photovoltaic system to obtain at least one cluster.

[0101] In one alternative implementation, a target number of cluster centers can be selected from each distributed photovoltaic (PV) ...

[0102] For example, the sum of electrical distances between each distributed photovoltaic (PV) unit and other distributed PV units can be determined, and the target number of distributed PV units with the smallest sum can be used as initial cluster centers. Then, the other distributed PV units are classified based on the DAP algorithm to obtain at least one cluster.

[0103] For example, the DAP algorithm does not require initial selection of cluster centers. Each distributed photovoltaic power station is considered an initial cluster center at the initial sampling points, and the attraction matrix and membership matrix are iteratively updated after data is read. Therefore, two information matrices are defined: the attraction matrix R and the membership matrix A. Their update formulas can be as follows:

[0104] ;

[0105] ;

[0106] In the formula, R(i,k) characterizes the suitability of photovoltaic node k as the cluster center of photovoltaic grid-connected unit i. k' represents other initial cluster centers besides photovoltaic grid-connected unit k. A(i,k) reflects the suitability of photovoltaic node i selecting photovoltaic node k as the cluster center, and R(k,k) reflects the suitability of photovoltaic node k selecting itself as the cluster center. S(i,k) is the similarity matrix, which represents the similarity between distributed photovoltaic grid-connected unit i and distributed photovoltaic grid-connected unit k. It can be calculated from the Euclidean distance of the clustering index, and for example, it can be determined based on the following formula:

[0107] ;

[0108] In the formula, B i and B k are the parameter vectors for photovoltaic node i and photovoltaic node k, respectively, and N is the number of distributed photovoltaic grid-connected units in the network.

[0109] During the clustering process, attractiveness and belongingness are transmitted through suitability and appropriateness, and these two pieces of information are updated according to an iterative formula, thereby automatically determining the cluster center power station. The iterative formula is shown below:

[0110] ;

[0111] In the formula, m is the number of iterations, and λ is the damping coefficient introduced to adjust its convergence speed and stability.

[0112] To capture the different dynamic characteristics of various distributed photovoltaic grid-connected units, the Dynamic Time Warping (DTW) distance is used instead of Euclidean distance to calculate similarity. Taking the grid-connected output active power P={p1,p2,...,pN} and reactive power Q={q1,q2,...,qN} as examples, the dynamic distance matrix G(P,Q) of the two sets of indicators is calculated as follows:

[0113] ;

[0114] In the formula, G(P,Q) represents the dynamic distance matrix of the two sets of indicators: active power and reactive power; Z(i,j) represents the local distance metric. The current distance depends not only on the Euclidean distance but also on the minimum cumulative distance in the three directions to its left, top, and upper left of the diagonal, as shown below:

[0115] ;

[0116] In the formula, Z(i,j) represents the local distance metric, a core parameter in the calculation of Dynamic Time Warping Distance (DTW).

[0117] The above embodiments provide a specific implementation method for determining at least one cluster. In this process, the electrical distance between different distributed photovoltaic systems makes the logic for determining the clusters more reasonable and reliable.

[0118] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the power-related data includes equivalent active power and equivalent reactive power; the equivalent data includes equivalent active power and equivalent reactive power; correspondingly, the process of determining the equivalent data corresponding to the cluster based on the power-related data corresponding to each distributed photovoltaic in the cluster is refined.

[0119] See Figure 3 The steps for determining the equivalent data shown include:

[0120] S310: The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster.

[0121] S320 performs a weighted summation of the reactive power corresponding to each distributed photovoltaic power in the cluster to obtain the equivalent reactive power corresponding to the cluster.

[0122] For example, the equivalent active power and equivalent reactive power can be determined by the following formula:

[0123] ;

[0124] In the formula, P eq Q eq P represents the equivalent active power and equivalent reactive power, respectively. i Q i Let N be the output active power and reactive power of the i-th distributed photovoltaic grid-connected unit in a certain sub-cluster, and N be the number of distributed photovoltaic grid-connected units in the subsystem.

[0125] The above embodiments provide a specific method for determining the equivalent active power and equivalent reactive power corresponding to the clusters, ensuring that the grid-connected active power and reactive power remain constant before and after equivalence, which facilitates subsequent simulation.

[0126] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the power-related data includes equivalent active power, equivalent reactive power, and equivalent voltage; the equivalent data includes equivalent impedance. Accordingly, the equivalent impedance corresponding to the cluster can be determined based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster.

[0127] See Figure 4The steps for determining the equivalent impedance shown include:

[0128] S410, determine the first impedance corresponding to the cluster based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster.

[0129] The preset impedance can be determined based on prior experience or through extensive testing; this application does not impose any limitations on it.

[0130] It should be noted that when distributed photovoltaic (PV) power is connected to the grid, a transformer is required for connection. Therefore, the equivalent impedance includes a first impedance and a second impedance. The first impedance can be the impedance from the distributed PV power to the transformer; the second impedance can be the impedance from the transformer to the grid. For example, the first impedance can be determined using the following formula:

[0131] ;

[0132] In the formula, Z eq S represents the impedance from the distributed photovoltaic system to the transformer. i For transformer capacity in non-cluster centers; S a Z represents the transformer capacity of the cluster center. i The preset impedance is the impedance of grid-connected unit i within a certain subsystem.

[0133] S420 determines the first voltage difference between the cluster and the grid connection point based on the second impedance and equivalent active power corresponding to the cluster.

[0134] The first voltage difference is the voltage difference between the xth photovoltaic equivalent power station and the grid connection point.

[0135] It is understandable that, in addition to equivalence of the distributed photovoltaic grid-connected system, it is also necessary to equivalence the lines and loads within each cluster. Based on the principle of homology equivalence, the photovoltaic grid-connected units within cluster Ai are equivalent to photovoltaic power stations.

[0136] For example, the first voltage difference can be determined based on the following formula:

[0137] ;

[0138] In the formula, Z eq，x P eq，x U represents the equivalent impedance and equivalent active power from the x-th cluster to the grid connection point, respectively; pcc Indicates the input voltage at the grid connection point; △U eq,x It is the voltage difference between the xth photovoltaic equivalent power station (i.e., the cluster) and the grid connection point, which is also the first voltage difference.

[0139] S430, based on the output voltage and equivalent active power of each distributed photovoltaic in the cluster, determines the second voltage difference between the cluster and the grid connection point.

[0140] The second voltage difference is the voltage difference between the xth photovoltaic equivalent power station and the grid connection point after considering the power weighted average.

[0141] It should be noted that using power as the weight for weighted averaging can more accurately reflect the impact of each photovoltaic power station on the system voltage under specific disturbances. Based on this, the second voltage difference can be determined using the following formula:

[0142] ;

[0143] In the formula, △U x P represents the voltage difference between the x-th photovoltaic equivalent power station and the grid connection point after considering power-weighted averaging, which is also the second voltage difference; i Let be the output active power of the i-th distributed photovoltaic grid-connected unit in a certain sub-cluster; t represents the number of photovoltaic grid-connected units within the cluster, ΔU i This represents the voltage drop between the i-th distributed photovoltaic grid-connected unit (i.e., distributed photovoltaic) within the cluster and the equivalent model grid connection point (i.e., the grid connection point corresponding to the cluster).

[0144] S440, with the goal of making the first voltage difference equal to the second voltage difference, determine the second impedance corresponding to the cluster, and use the first impedance and the second impedance as the equivalent impedance corresponding to the cluster.

[0145] The voltage difference between the photovoltaic system and the grid connection point should theoretically be equal before and after the equivalent voltage difference, that is, the first voltage difference and the second voltage difference should be equal. Combining the above two formulas, we get the following formula:

[0146] ;

[0147] In the formula, Z eq,x The second impedance represents the impedance from the transformer to the power grid; t represents the number of photovoltaic grid-connected units within the cluster; ΔU i P represents the voltage drop between the i-th distributed photovoltaic grid-connected unit (i.e., distributed photovoltaic) within the cluster and the equivalent model grid connection point (i.e., the grid connection point corresponding to the cluster). i Let i be the output active power of the i-th distributed photovoltaic grid-connected unit in a certain sub-cluster;

[0148] Furthermore, it should be noted that load processing in the network needs to maintain consistency before and after equivalence. For example, the equivalent load can be determined using the following formula:

[0149] ;

[0150] In the formula, Seq,i represents the equivalent impedance of cluster i; Sload,i represents the system impedance of the i-th distributed photovoltaic in the cluster.

[0151] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0152] Based on the same inventive concept, this application also provides a distribution network simulation device for implementing the distribution network simulation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more distribution network simulation device embodiments provided below can be found in the limitations of the distribution network simulation method described above, and will not be repeated here.

[0153] In one exemplary embodiment, such as Figure 5 As shown, a power distribution network simulation device is provided, including: an acquisition module 510, a first determination module 520, and a second determination module 530, wherein:

[0154] The acquisition module 510 is used to acquire the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network.

[0155] The first determining module 520 is used to cluster each distributed photovoltaic (PV) system based on the power-related data output by the equivalent model corresponding to different distributed PV systems and the environmental impact data of the target distribution network, thereby obtaining at least one cluster.

[0156] The second determining module 530 is used to determine the equivalent data corresponding to any cluster based on the power-related data of each distributed photovoltaic in the cluster; the equivalent data is used to perform simulation processing on the target distribution network.

[0157] In one embodiment, the first determining module 520 includes a first acquiring unit, configured to acquire, for any distributed photovoltaic, the equivalent active power and equivalent reactive power output by the equivalent model corresponding to the distributed photovoltaic; a first determining unit, configured to determine the electrical distance between different distributed photovoltaics based on the equivalent active power and equivalent reactive power; and a second determining unit, configured to cluster each distributed photovoltaic based on each electrical distance to obtain at least one cluster.

[0158] In one embodiment, the first determining unit includes a first determining subunit, configured to determine the sensitivity data corresponding to the distributed photovoltaic system based on active power and reactive power; and a second determining subunit, configured to determine the electrical distance between different distributed photovoltaic systems based on the sensitivity data corresponding to different distributed photovoltaic systems.

[0159] In one embodiment, the second determining unit includes a selection subunit for selecting a target number of cluster centers from each distributed photovoltaic (PV) based on each electrical distance; and a third determining subunit for classifying each other distributed PV based on the similarity between each distributed PV and the cluster centers to obtain at least one cluster; wherein the other distributed PVs are distributed PVs that are not cluster centers.

[0160] In one embodiment, power-related data includes equivalent active power and equivalent reactive power; equivalent data includes equivalent active power and equivalent reactive power. Correspondingly, the second determining module 530 includes a third determining unit, used to perform a weighted summation of the active power corresponding to each distributed photovoltaic unit in the cluster to obtain the equivalent active power corresponding to the cluster; and a fourth determining unit, used to perform a weighted summation of the reactive power corresponding to each distributed photovoltaic unit in the cluster to obtain the equivalent reactive power corresponding to the cluster.

[0161] In one embodiment, the power-related data includes equivalent active power, equivalent reactive power, and equivalent voltage; the equivalent data includes equivalent impedance; accordingly, the second determining module 530 is specifically used to determine the equivalent impedance corresponding to the cluster based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster.

[0162] In one embodiment, the second determining module 530 includes a fifth determining unit, configured to determine a first impedance corresponding to the cluster based on the transformer capacity and preset impedance of each distributed photovoltaic unit in the cluster; a sixth determining unit, configured to determine a first voltage difference between the cluster and the grid connection point based on the second impedance and equivalent active power of the cluster; a seventh determining unit, configured to determine a second voltage difference between the cluster and the grid connection point based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster; and an eighth determining unit, configured to determine the second impedance corresponding to the cluster with the goal of making the first voltage difference equal to the second voltage difference, and to use the first impedance and the second impedance as the equivalent impedance corresponding to the cluster.

[0163] Each module in the aforementioned power distribution network simulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0164] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power distribution network simulation method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0165] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0166] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0167] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0168] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0169] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0170] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0171] For any distributed photovoltaic system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed photovoltaic system;

[0172] The electrical distance between different distributed photovoltaic systems is determined based on the equivalent active power and equivalent reactive power.

[0173] Based on each electrical distance, each distributed photovoltaic system is clustered to obtain at least one cluster.

[0174] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0175] Determine the sensitivity data corresponding to distributed photovoltaic power based on active power and reactive power;

[0176] Based on the sensitivity data corresponding to different distributed photovoltaic systems, the electrical distance between different distributed photovoltaic systems is determined.

[0177] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0178] Based on the electrical distances, select a target number of cluster centers from each distributed photovoltaic system;

[0179] Based on the similarity between each distributed photovoltaic (PV) and the cluster center, the other distributed PVs are classified to obtain at least one cluster; among them, the other distributed PVs are distributed PVs that are not cluster centers.

[0180] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0181] The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster.

[0182] The reactive power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent reactive power corresponding to the cluster.

[0183] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0184] Based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster, the equivalent impedance corresponding to the cluster is determined.

[0185] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0186] The first impedance corresponding to the cluster is determined based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster.

[0187] The first voltage difference between the cluster and the grid connection point is determined based on the second impedance and equivalent active power corresponding to the cluster.

[0188] The second voltage difference between the cluster and the grid connection point is determined based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster.

[0189] With the goal of making the first voltage difference equal to the second voltage difference, the second impedance corresponding to the cluster is determined, and the first impedance and the second impedance are used as the equivalent impedance corresponding to the cluster.

[0190] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0191] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0192] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0193] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0194] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0195] For any distributed photovoltaic system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed photovoltaic system;

[0196] The electrical distance between different distributed photovoltaic systems is determined based on the equivalent active power and equivalent reactive power.

[0197] Based on each electrical distance, each distributed photovoltaic system is clustered to obtain at least one cluster.

[0198] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0199] Determine the sensitivity data corresponding to distributed photovoltaic power based on active power and reactive power;

[0200] Based on the sensitivity data corresponding to different distributed photovoltaic systems, the electrical distance between different distributed photovoltaic systems is determined.

[0201] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0202] Based on the electrical distances, select a target number of cluster centers from each distributed photovoltaic system;

[0203] Based on the similarity between each distributed photovoltaic (PV) and the cluster center, the other distributed PVs are classified to obtain at least one cluster; among them, the other distributed PVs are distributed PVs that are not cluster centers.

[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0205] The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster.

[0206] The reactive power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent reactive power corresponding to the cluster.

[0207] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0208] Based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster, the equivalent impedance corresponding to the cluster is determined.

[0209] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0210] The first impedance corresponding to the cluster is determined based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster.

[0211] The first voltage difference between the cluster and the grid connection point is determined based on the second impedance and equivalent active power corresponding to the cluster.

[0212] The second voltage difference between the cluster and the grid connection point is determined based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster.

[0213] With the goal of making the first voltage difference equal to the second voltage difference, the second impedance corresponding to the cluster is determined, and the first impedance and the second impedance are used as the equivalent impedance corresponding to the cluster.

[0214] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0215] Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network;

[0216] Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, each distributed photovoltaic is clustered to obtain at least one cluster.

[0217] For any given cluster, the equivalent data corresponding to the cluster is determined based on the power-related data of each distributed photovoltaic power generation unit in the cluster. The equivalent data is used to simulate the target power distribution network.

[0218] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0219] For any distributed photovoltaic system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed photovoltaic system;

[0220] The electrical distance between different distributed photovoltaic systems is determined based on the equivalent active power and equivalent reactive power.

[0221] Based on each electrical distance, each distributed photovoltaic system is clustered to obtain at least one cluster.

[0222] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0223] Determine the sensitivity data corresponding to distributed photovoltaic power based on active power and reactive power;

[0224] Based on the sensitivity data corresponding to different distributed photovoltaic systems, the electrical distance between different distributed photovoltaic systems is determined.

[0225] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0226] Based on the electrical distances, select a target number of cluster centers from each distributed photovoltaic system;

[0227] Based on the similarity between each distributed photovoltaic (PV) and the cluster center, the other distributed PVs are classified to obtain at least one cluster; among them, the other distributed PVs are distributed PVs that are not cluster centers.

[0228] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0229] The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster.

[0230] The reactive power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent reactive power corresponding to the cluster.

[0231] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0232] Based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster, the equivalent impedance corresponding to the cluster is determined.

[0233] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0234] The first impedance corresponding to the cluster is determined based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster.

[0235] The first voltage difference between the cluster and the grid connection point is determined based on the second impedance and equivalent active power corresponding to the cluster.

[0236] The second voltage difference between the cluster and the grid connection point is determined based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster.

[0237] With the goal of making the first voltage difference equal to the second voltage difference, the second impedance corresponding to the cluster is determined, and the first impedance and the second impedance are used as the equivalent impedance corresponding to the cluster. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0238] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0239] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0240] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power distribution network simulation method, characterized in that, The method includes: Obtain the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network; Based on the power-related data output by the equivalent models corresponding to different distributed photovoltaics, and the environmental impact data of the target distribution network, the distributed photovoltaics are clustered to obtain at least one cluster. For any cluster, based on the power-related data corresponding to each distributed photovoltaic power generation in the cluster, the equivalent data corresponding to the cluster is determined; the equivalent data is used to perform simulation processing on the target distribution network.

2. The method according to claim 1, characterized in that, The distributed photovoltaic (PV) systems are clustered based on the power-related data output from the equivalent models corresponding to different distributed PV systems, and the environmental impact data of the target distribution network, to obtain at least one cluster, including: For any distributed photovoltaic (PV) system, obtain the equivalent active power and equivalent reactive power output from the equivalent model corresponding to the distributed PV system. The electrical distance between different distributed photovoltaic systems is determined based on the equivalent active power and the equivalent reactive power. Based on the electrical distances described, the distributed photovoltaic systems are clustered to obtain at least one cluster.

3. The method according to claim 2, characterized in that, Determining the electrical distance between different distributed photovoltaic systems based on the active power and the reactive power includes: Based on the active power and the reactive power, determine the sensitivity data corresponding to the distributed photovoltaic system; Based on the sensitivity data corresponding to different distributed photovoltaic systems, the electrical distance between different distributed photovoltaic systems is determined.

4. The method according to claim 2, characterized in that, The step of clustering the distributed photovoltaic systems according to their respective electrical distances to obtain at least one cluster includes: Based on the electrical distances described, a target number of cluster centers are selected from each of the distributed photovoltaic systems. Based on the similarity between each distributed photovoltaic (PV) and the cluster center, the other distributed PVs are classified to obtain at least one cluster; wherein, the other distributed PVs are distributed PVs that are not cluster centers.

5. The method according to any one of claims 1-4, characterized in that, The power-related data includes equivalent active power and equivalent reactive power; the equivalent data includes equivalent active power and equivalent reactive power; correspondingly, determining the equivalent data corresponding to the cluster based on the power-related data corresponding to each distributed photovoltaic in the cluster includes: The active power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent active power corresponding to the cluster. The reactive power corresponding to each distributed photovoltaic power in the cluster is weighted and summed to obtain the equivalent reactive power corresponding to the cluster.

6. The method according to any one of claims 1-4, characterized in that, The power-related data includes equivalent active power, equivalent reactive power, and equivalent voltage; the equivalent data includes equivalent impedance; correspondingly, determining the equivalent data corresponding to the cluster based on the power-related data of each distributed photovoltaic unit in the cluster includes: The equivalent impedance corresponding to the cluster is determined based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, as well as the equivalent active power and equivalent reactive power corresponding to the cluster.

7. The method according to claim 6, characterized in that, The step of determining the equivalent impedance corresponding to the cluster based on the transformer capacity and equivalent voltage corresponding to each distributed photovoltaic in the cluster, and the equivalent active power and equivalent reactive power corresponding to the cluster, includes: The first impedance corresponding to the cluster is determined based on the transformer capacity and preset impedance of each distributed photovoltaic in the cluster. Based on the second impedance corresponding to the cluster and the equivalent active power, determine the first voltage difference between the cluster and the grid connection point; Based on the output voltage and equivalent active power of each distributed photovoltaic unit in the cluster, determine the second voltage difference between the cluster and the grid connection point; With the goal of making the first voltage difference equal to the second voltage difference, the second impedance corresponding to the cluster is determined, and the first impedance and the second impedance are used as the equivalent impedance corresponding to the cluster.

8. A power distribution network simulation device, characterized in that, The device includes: The acquisition module is used to acquire the equivalent model corresponding to each distributed photovoltaic power station connected to the target distribution network; The first determining module is used to cluster each of the distributed photovoltaics according to the power-related data output by the equivalent model corresponding to different distributed photovoltaics and the environmental impact data of the target distribution network, so as to obtain at least one cluster. The second determining module is used to determine the equivalent data corresponding to any cluster based on the power-related data of each distributed photovoltaic in the cluster; the equivalent data is used to perform simulation processing on the target distribution network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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