Urban power distribution network simplified calculation method and system based on multi-level equivalent modeling

Abstract

The invention discloses an urban power distribution network simplified calculation method and system based on multi-level equivalent modeling, and relates to the technical field of power distribution network digital simulation, and the method comprises the steps: defining the boundary of a power distribution network based on the data of the power distribution network; generating an equivalence model based on the complex hierarchy, equivalence principle and boundary of the power distribution network; adjusting the electrical characteristics and the grid structure of the equivalent model, and generating a power distribution network model; the method for generating the equivalent model comprises the following specific steps: acquiring a sensitive area and a plurality of subareas of the power distribution network; constructing a single equivalent component; the method comprises the steps of obtaining a high level based on a Thevenin equivalent circuit model, an equivalence principle and a boundary, obtaining a middle level based on a Ward equivalent model, the equivalence principle and the boundary, obtaining a low level based on a three-phase admittance matrix, the equivalence principle and the boundary, generating an equivalent model based on a sensitive area, partitions, a single equivalent component, the high level, the middle level and the low level, the problem that an existing power distribution network modeling method is poor in model precision due to the fact that the number of nodes is large is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital simulation of distribution networks, and in particular, to a simplified calculation method and system for urban distribution networks based on multi-level equivalent modeling. Background Art

[0002] Urban distribution networks are an important part of urban power systems, responsible for transmitting electricity from substations to end-users. Traditional distribution network modeling methods often require detailed modeling of all nodes and connections from high-voltage substations to user meters, and the number of nodes often exceeds 104 levels. Although such methods are accurate, they also bring significant performance bottlenecks, especially when performing power flow calculations. Due to the large number of nodes, the power flow calculation time of traditional modeling methods is often too long, and a single calculation may exceed 5 minutes, seriously affecting the real-time operation control of the distribution network. Currently, equivalent modeling methods are usually used to simplify the model, such as REI equivalence. Although the model can be simplified, when it comes to the areas with a high proportion of photovoltaic power sources, due to the characteristics of photovoltaic power sources themselves, the characteristics of distributed energy access, and the limitations of the modeling method itself, the model accuracy is poor. The specific reasons are as follows:

[0003] 1) The output power of photovoltaic power sources is affected by natural factors, with intermittency and volatility, and the MPPT algorithms equipped with them also have differences and uncertainties. Traditional equivalent modeling methods are difficult to accurately capture these characteristics and often deal with them based on simplified assumptions, resulting in the accumulation of deviations.

[0004] 2) Photovoltaic and other distributed energy sources are scattered and connected to different positions of the distribution network in large numbers, interacting with traditional loads, changing the network power flow and voltage characteristics. Traditional equivalent modeling methods have problems such as excessive simplification and unreasonable assumptions when dealing with this complex access and source-load interaction relationship, and cannot accurately reflect the actual operating state.

[0005] 3) Traditional modeling methods have their own limitations, such as inconsistent model assumptions with reality, difficulty in determining equivalent parameters, and insufficient dynamic characteristics. It is difficult to truly reflect the actual operating characteristics of the distribution network, especially weak in the simulation of dynamic processes, resulting in reduced accuracy and increased errors. Summary of the Invention

[0006] In order to solve the problem that the existing distribution network modeling method has poor model accuracy due to a large number of nodes and low calculation efficiency, the present invention provides a simplified calculation method for urban distribution network based on multi-level equivalent modeling. The method includes: obtaining distribution network data and defining the boundary of the distribution network based on the distribution network data; generating an equivalent model based on the complex levels, equivalent principles and the boundary of the distribution network; adjusting the electrical characteristics and grid structure of the equivalent model to generate a distribution network model. The complex levels include high level, middle level and low level, and the equivalent principles include node equivalent principle and line equivalent principle.

[0007] The specific steps of generating the equivalent model include: obtaining the sensitive area and several partitions of the distribution network; constructing a single equivalent component; obtaining the high level based on the Thevenin equivalent circuit model, the equivalent principle and the boundary, obtaining the middle level based on the Ward equivalent model, the equivalent principle and the boundary, obtaining the low level based on the three-phase admittance matrix, the equivalent principle and the boundary, and generating the equivalent model based on the sensitive area, the partitions, the single equivalent component, the high level, the middle level and the low level.

[0008] This method can improve the model accuracy and the efficiency of processing data by defining the boundary of the distribution network, obtaining the area that needs to be modeled in detail through the sensitive area, and dividing the distribution network through partitions, and is more adaptable to the diverse needs of urban distribution networks; by formulating differential equivalent principles and multi-level equivalent modeling to simplify the distribution network model and applying it to the main line simulation, the number of nodes and the amount of calculation are greatly reduced, the complexity of the distribution network power flow calculation is reduced, the calculation speed is significantly improved, and the requirements of real-time scheduling and control are met. By simplifying the unnecessary details in the distribution network, the utilization rate of grid resources can be optimized; by simplifying the electrical characteristics and geometric structure, simplifying the substation area, main line voltage, etc. of the equivalent model, optimizing the simulation calculation process, improving the simulation efficiency and real-time response ability of the distribution network, adapting to the large-scale distribution network modeling requirements, and ensuring the calculation accuracy and resource utilization rate.

[0009] This method defines the boundary conditions, which can include power source boundary, load boundary, equipment capacity boundary, etc. In a complex distribution network, multiple substations serve as the power source boundary, and the equivalent impedance of their outgoing lines can be calculated through the setting current of the protection equipment at the head of the line. For the load boundaries of multiple substations, the data collected by the TTU of the substation is used to define them. This refined processing of the boundary conditions enables accurate division of the boundaries of different regions when facing a complex grid structure, provides accurate input data for subsequent equivalent modeling, thereby improving the model accuracy and ensuring that the equivalent model can correctly reflect the operating state of the actual power grid.

[0010] This method simplifies the model using the equivalent principle, which can be various methods such as node equivalence method, line equivalence method, and equipment equivalence method. In a complex distribution network, for the main 10kV feeders, the node equivalence method can be used for simplification; for the distribution transformer area lines, the line equivalence method can be used to simplify the calculation. This flexible selection of the equivalent principle enables targeted equivalent modeling according to the characteristics of different parts of the power grid, effectively handling the complex connection relationships between multiple substations and multiple distribution transformer areas, thereby improving the model accuracy.

[0011] This method adopts a multi-level equivalent modeling method, dividing the distribution network into high-level, middle-level, and low-level. For a complex distribution network containing multiple substations and multiple distribution transformer areas, this hierarchical method can model the grid structures and characteristics of different levels respectively, effectively improving the model accuracy. The high-level is based on the Thevenin equivalent model, which can equivalent the entire substation and its nearby complex network into an equivalent power source and impedance, simplifying the calculation complexity of the substation; the middle-level is based on the Ward equivalent model, which can equivalent the power grid near the distribution transformer area. In this way, when dealing with multiple distribution transformer areas, the complex network of each distribution transformer area and its surrounding can be simplified into the corresponding equivalent model, thereby reducing the overall calculation complexity while retaining the key electrical characteristics; the low-level is based on the three-phase admittance matrix, which equivalent a complex radial network (such as a feeder or a distribution transformer area) into a multi-port admittance matrix, significantly reducing the number of nodes in the overall model. The three-phase admittance matrix (in the form of a 3×3 sub-matrix) naturally supports phase coupling (such as mutual inductance between phases and unbalanced loads), can accurately equivalent the three-phase unbalanced characteristics of the original network, and since single-phase loads and asymmetric lines are common in the distribution network and the traditional single-phase equivalent model has a large error, the three-phase admittance matrix is a more practical choice.

[0012] The three-phase admittance matrix is a mathematical tool that describes the electrical coupling relationships between all nodes in a three-phase power network. Essentially, it is an extended form of the single-phase admittance matrix in a three-phase system. It accurately represents the linear relationship between three-phase voltages and currents in matrix form and is the core basis for analyzing problems such as power flow, short circuit, and stability in the distribution network.

[0013] In summary, through equivalent modeling and simplified calculation, this method can perform efficient simulation under limited resources, enhance the computational adaptability of large-scale systems, handle large-scale distribution network simulations, and is particularly suitable for complex distribution networks containing multiple substations and multiple distribution transformer areas.

[0014] Furthermore, the specific steps for obtaining the sensitive area and several partitions of the distribution network include: based on the distribution network data, obtaining the sensitivity of voltage to power, and obtaining the sensitive area based on the sensitivity; clustering the distribution network data based on preset parameters to obtain the partitions.

[0015] Combining sensitivity analysis with a clustering algorithm for zoning in multi-level equivalent modeling can calculate the sensitivity of voltage to power through sensitivity analysis, determine the areas that require detailed modeling (high-sensitivity areas), and further optimize the zoning based on electrical distance or load density through the clustering algorithm.

[0016] Furthermore, the specific steps for constructing a single equivalent component include: generating the single equivalent component based on state enumeration and Monte Carlo simulation;

[0017] The calculation method for generating the single equivalent component is:

[0018] ;

[0019] where represents the equivalent parameter, represents the state, represents the set of states, represents the state probability, represents the state parameter value.

[0020] The single equivalent component is used to simplify complex power system models through state probability and parameter calculations, improving the analysis efficiency.

[0021] Furthermore, the method further includes: obtaining the number of simulation times of the single equivalent component, and obtaining the reliability of the equivalent model based on the number of simulation times;

[0022] The calculation formula for obtaining the reliability is:

[0023] ;

[0024] where represents the reliability, represents the number of simulation times, represents the th simulation fault indication function, represents an integer greater than or equal to 1.

[0025] Performing reliability analysis on the equivalent model to measure its reliability and ensure the performance of the model.

[0026] Furthermore, the method further includes: obtaining the energy data of distributed energy resources, constructing a dynamic characteristic probability model based on the energy data, and obtaining the output characteristics of distributed energy based on the dynamic characteristic probability model;

[0027] The calculation formula for the dynamic characteristic probability model is:

[0028] ;

[0029] Among them, represents the output power of distributed energy resources, represents the mean value of distributed energy resources, represents the variance of distributed energy resources, represents the probability distribution of the output power of distributed energy.

[0030] DERs (such as photovoltaics and energy storage) are usually modeled as static negative loads, without considering their dynamics and randomness. In the low-level modeling of this method, DERs are modeled as dynamic loads, and a probability model is introduced to describe the randomness of their power output (such as photovoltaics being affected by weather). Combining multi-level modeling, the dynamic characteristics of DERs are gradually transmitted to the middle level and high level to solve the problems of DERs fluctuations and complex topologies.

[0031] Furthermore, the method further includes: obtaining a detailed model of the distribution network, comparing the detailed model with the equivalent model to obtain an error value, determining whether the error value exceeds a preset threshold, and if so, adjusting the first model parameters of the equivalent model, obtaining a first model based on the first model parameters, and updating the equivalent model to the first model;

[0032] The calculation formula for the error value is:

[0033] ;

[0034] Among them, represents the error value, and respectively represent the voltage vectors of the equivalent model and the detailed model.

[0035] Compare the power flow and voltage distribution of the equivalent model and the detailed model. When the error exceeds the preset threshold, adjust the equivalent model parameters and re-verify, which is used to verify the difference between the equivalent model and the detailed model and verify the accuracy of the model.

[0036] Furthermore, the specific steps for adjusting the electrical characteristics and grid structure of the equivalent model include:

[0037] Simplify the distribution parameters of the feeder in the equivalent model based on the lumped parameter model, process the load dynamics of the equivalent model based on the average value model, optimize the grid structure of the equivalent model based on the minimum spanning tree algorithm, calculate the line impedance of the equivalent model based on Carson's equation, and optimize the second model parameters of the equivalent model based on the genetic algorithm;

[0038] The specific steps for generating a distribution network model include:

[0039] A three-phase power flow model is obtained based on the Newton-Raphson algorithm. Based on the adjusted equivalent model and the three-phase power flow model, the distribution network model is obtained.

[0040] By simplifying the electrical characteristics, a simplified simulation model for rapid calculation is constructed; the geometric structure of the power grid is simplified, and a genetic algorithm optimization algorithm is used to accelerate the simulation process to achieve simulation calculation optimization; by combining the three-phase power flow model and the average value model, the simulation accuracy and efficiency are improved.

[0041] The three-phase power flow model has the following key uses:

[0042] Handling unbalanced loads: Urban distribution networks are often unbalanced due to single-phase loads (such as residential and commercial electricity) or distributed energy sources (such as photovoltaic). The three-phase power flow model can accurately analyze the voltage, current, and power flow of each phase, solve the unbalanced problem, and ensure the stable operation of the power grid.

[0043] Improving simulation accuracy: Compared with the single-phase model, the three-phase model takes into account the inter-phase coupling and mutual inductance effects, providing a more realistic simulation of the power grid behavior. This is crucial for power flow calculation, voltage distribution analysis, and fault detection in complex urban power grids.

[0044] Supporting real-time scheduling: By quickly calculating the three-phase power flow, the model supports real-time scheduling decisions, such as load distribution, voltage regulation, and DERs (distributed energy resources) management, to optimize the power grid efficiency.

[0045] Enhancing fault analysis capabilities: The three-phase model can simulate common problems in urban power grids such as single-phase grounding faults and unbalanced faults, providing accurate data for fault location and restoration.

[0046] Adapting to complex topologies: Urban distribution networks have complex structures such as multi-branches and loop networks. The three-phase power flow model combined with the simplified equivalent model can efficiently handle these topologies and reduce the computational complexity.

[0047] Furthermore, the method further includes:

[0048] Updating the three-phase power flow model and the equivalent model based on a preset cloud computing-based simulation framework and objective function;

[0049] The objective function is:

[0050] ;

[0051] Wherein, represents the objective function, and both represent weights, represents accuracy, represents speed.

[0052] Using the cloud computing framework, the model can be updated in real time on the cloud. The objective function comprehensively considers accuracy and speed. By combining the cloud computing framework and the genetic algorithm, the real-time performance and scalability of the model are enhanced. Therefore, this method is more suitable for complex distribution networks containing multiple substations and multiple distribution areas.

[0053] For a complex distribution network containing multiple substations and multiple distribution areas, the computational load may still be relatively large. The simulation framework based on cloud computing can utilize the powerful computing power of cloud computing to update the model in real time on the cloud. In this way, complex computational tasks can be distributed to multiple computing nodes on the cloud, quickly completing the simulation calculation of large-scale distribution networks, improving the computational efficiency, and meeting the simulation requirements of complex distribution networks.

[0054] Furthermore, the method further includes:

[0055] Obtaining real-time power grid data, constructing a state space model, and updating the boundary and the equivalent model based on the state space model and the real-time power grid data;

[0056] The calculation formula for updating the equivalent model is: ;

[0057] Where represents the power adjustment amount, represents the proportional gain, represents the integral gain, represents the actual measured power, represents the simulated power;

[0058] The calculation formula for the state space model is:

[0059] ;

[0060] Where represents the system state at time represents the system state at time represents the real-time power grid data input at time represents the output power grid data at time represents the state matrix, represents the input matrix, represents the output matrix, represents the direct matrix.

[0061] During the dynamic simulation process, the equivalent model is adjusted according to the real-time feedback results. Through the real-time feedback mechanism, the equivalent method is continuously optimized. A feedback mechanism is constructed to monitor the simulation results in real time. According to the feedback, the boundary conditions and the equivalent model are adjusted to ensure that the entire power grid model is always consistent with the actual operating state. A closed-loop control system is adopted to adjust the equivalent model according to the real-time measurement data. And a state space model is established to represent the feedback process. The existing improved LSTM is used to predict the load change, improving the forward-looking and adaptability of the feedback mechanism to adapt to the complexity and dynamics of the urban power grid.

[0062] The operating state of the distribution network is dynamically changing, especially in a complex distribution network containing multiple substations and multiple substations. The dynamic feedback mechanism can adjust the equivalent model according to the real-time feedback results. For example, when the load of a certain substation area changes greatly or the outgoing line power of a certain substation fluctuates, through the closed-loop control system and the state space model, the parameters of the equivalent model are adjusted in real time to ensure that the entire power grid model is consistent with the actual operating state. This dynamic adjustment ability enables the model to adapt to the dynamic changes of various parts in the complex distribution network, ensuring the accuracy and reliability of the calculation results.

[0063] The present invention also provides a simplified calculation system for an urban distribution network based on multi-level equivalent modeling. The system further includes:

[0064] Boundary unit: used to obtain distribution network data and define the boundary of the distribution network based on the distribution network data;

[0065] Equivalent model unit: used to generate an equivalent model based on the complex level, equivalent principle and the boundary of the distribution network;

[0066] The complex level includes a high level, a middle level and a low level. The equivalent principle includes a node equivalent principle and a line equivalent principle;

[0067] The equivalent model unit specifically includes:

[0068] Partition unit: used to obtain the sensitive area and several partitions of the distribution network;

[0069] Component unit: used to construct a single equivalent component;

[0070] Level unit: used to obtain the high level based on the Thevenin equivalent circuit model, the equivalent principle and the boundary, obtain the middle level based on the Ward equivalent model, the equivalent principle and the boundary, and obtain the low level based on the three-phase admittance matrix, the equivalent principle and the boundary;

[0071] Generation unit: used to generate the equivalent model based on the sensitive area, the partition, the single equivalent component, the high level, the middle level and the low level;

[0072] Simplified model unit: used to adjust the electrical characteristics and grid structure of the equivalent model to generate a distribution network model.

[0073] The principle and effect of this system are similar to those of this method, and corresponding elaboration will not be carried out for this system.

[0074] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0075] 1. By defining the boundary of the distribution network and obtaining the area that needs to be modeled in detail through the sensitive area, and dividing the distribution network through zoning, the accuracy of the model and the efficiency of processing data can be improved, and it can better meet the diverse needs of urban distribution networks; by the equivalent principle and multi-level equivalent modeling, the distribution network model is simplified and applied to the main line simulation, significantly reducing the number of nodes and the amount of calculation, reducing the complexity of the distribution network power flow calculation, significantly improving the calculation speed, meeting the requirements of real-time scheduling and control, and optimizing the utilization rate of grid resources by simplifying the unnecessary details in the distribution network; by simplifying the electrical characteristics and geometric structure, optimizing the simulation calculation process, improving the simulation efficiency and real-time response ability of the distribution network, meeting the needs of large-scale distribution network modeling, and ensuring the calculation accuracy and resource utilization rate.

[0076] 2. This method adopts a multi-level equivalent modeling method, which divides the distribution network into high-level, middle-level and low-level. For a complex distribution network containing multiple substations and multiple distribution areas, this hierarchical method can model the grid structures and characteristics of different levels respectively, effectively improving the model accuracy. The high-level is based on the Thevenin equivalent model, which can equivalent the complex network near the entire substation and its vicinity to an equivalent power source and impedance, simplifying the calculation complexity of the substation; the middle-level is based on the Ward equivalent model, which can equivalent the power grid near the distribution area. In this way, when dealing with multiple distribution areas, the complex network around each distribution area can be simplified to the corresponding equivalent model, thereby reducing the overall calculation complexity while retaining the key electrical characteristics.

[0077] 3. Combining sensitivity analysis with the clustering algorithm for zoning in multi-level equivalent modeling, the sensitivity of voltage to power can be calculated through sensitivity analysis to determine the area that needs to be modeled in detail (high-sensitivity area), and the zoning can be further optimized according to the electrical distance or load density through the clustering algorithm.

[0078] 4. In the low-level modeling, DERs are modeled as dynamic negative loads, and a probability model is introduced to describe the randomness of their power output (such as the influence of weather on photovoltaic power generation). Combining with multi-level modeling, the dynamic characteristics of DERs are gradually transmitted to the middle-level and high-level to solve the problems of DERs fluctuations and complex topologies.

[0079] 5. By simplifying the electrical characteristics, a simplified simulation model for fast calculation is constructed; the geometric structure of the power grid is simplified, and a genetic algorithm optimization algorithm is used to accelerate the simulation process to achieve simulation calculation optimization; by combining the three-phase power flow model and the average value model, the simulation accuracy and efficiency are improved.

[0080] 6. Using the cloud computing framework, the model can be updated in real time on the cloud. The objective function comprehensively considers accuracy and speed. By combining the cloud computing framework and the genetic algorithm, the real-time performance and scalability of the model are enhanced. Therefore, this method is more suitable for complex distribution networks containing multiple substations and multiple distribution areas.

[0081] 7. During the dynamic simulation process, the equivalent model is adjusted according to the real-time feedback results. Through the real-time feedback mechanism, the equivalent method is continuously optimized; a feedback mechanism is constructed to monitor the simulation results in real time. According to the feedback, the boundary conditions and the equivalent model are adjusted to ensure that the entire power grid model is always consistent with the actual operation state. A closed-loop control system is adopted to adjust the equivalent model according to the real-time measurement data; and a state space model is established to represent the feedback process. The existing improved LSTM is used to predict the load change to improve the foresight and adaptability of the feedback mechanism and adapt to the complexity and dynamics of the urban power grid. Description of the Drawings

[0082] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not limit the embodiments of the present invention;

[0083] Figure 1 It is a schematic flow chart of a simplified calculation method for an urban distribution network based on multi-level equivalent modeling in the present invention;

[0084] Figure 2 It is a schematic overall flow chart of a simplified calculation method for an urban distribution network based on multi-level equivalent modeling in the present invention. Detailed Embodiments

[0085] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0086] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0087] Embodiment 1

[0088] Reference Figure 1 AndFigure 2 , this embodiment provides a simplified calculation method for urban distribution networks based on multi-level equivalent modeling. The method includes:

[0089] Obtain distribution network data and define the boundaries of the distribution network based on the distribution network data. In this embodiment, the boundary conditions may include power source boundaries, load boundaries, equipment boundaries, etc.

[0090] For example, represent the distribution network as G=(V,E) based on graph theory. V represents the set of nodes, which may include substations, load points, etc. E represents the set of edges, which may include cables, lines, etc. Nodes can also be grouped according to geographical location, load density, and electrical distance based on the K-means clustering algorithm. The calculation method of the K-means clustering algorithm can be:

[0091] ; (1)

[0092] Among them, represents the number of clusters, represents an integer greater than or equal to 1, represents the th cluster, represents a data point, represents the th cluster center, that is, the average eigenvector of the nodes within the cluster.

[0093] In this embodiment, the method may further include data preprocessing, such as through Z-score processing:

[0094] ; (2)

[0095] Among them, represents the standard score, represents a data point, represents the mean value, represents the standard deviation.

[0096] In this embodiment, the method may further include boundary verification and boundary adjustment:

[0097] The boundary verification process can be:

[0098] ; (3)

[0099] ; (4)

[0100] Among them, and respectively represent the active power and reactive power of node , represents the voltage amplitude of node , represents the voltage magnitude of the node of the node represents the node node voltage phase angle difference of the node represents the real part of the admittance matrix represents the imaginary part of the admittance matrix and both represent integers greater than or equal to 1 represents the number of nodes

[0101] The adjustment process of the boundary can be as follows:

[0102] ; (5)

[0103] wherein represents the adjusted active power represents the active power before adjustment

[0104] The voltage constraint is:

[0105] ; (6)

[0106] wherein represents the minimum voltage magnitude represents the maximum voltage magnitude

[0107] The power constraint is:

[0108] ; (7)

[0109] wherein represents the minimum active power represents the maximum active power

[0110] Generate an equivalent model based on the complex hierarchy, equivalent principle, and the said boundary of the distribution network

[0111] Adjust the electrical characteristics and grid structure of the equivalent model to generate a distribution network model; in this embodiment, the electrical characteristics include impedance, voltage drop, etc.

[0112] The complex hierarchy includes a high level, a middle level, and a low level, and the equivalent principle includes a node equivalent principle and a line equivalent principle

[0113] The specific steps for generating the equivalent model include:

[0114] Obtain the sensitive area and several partitions of the distribution network

[0115] Construct a single equivalent component

[0116] Obtain the high level based on the Thevenin equivalent circuit model, the equivalence principle, and the boundary; for example, the Thevenin equivalent circuit model can be:

[0117] ; (8)

[0118] where, represents the equivalent voltage, represents the equivalent impedance, represents the self-admittance parameter of the node.

[0119] Obtain the middle level based on the Ward equivalent model, the equivalence principle, and the boundary; for example, the Ward equivalent model can be:

[0120] ; (9)

[0121] where, represents the equivalent admittance matrix of the external system, represents the admittance matrix of the internal system, represents the mutual admittance matrix between the nodes of the external system and the nodes of the internal system, represents the mutual admittance matrix between the nodes of the internal system and the nodes of the external system, which is the transpose matrix of represents the admittance matrix of the external system.

[0122] Obtain the low level based on the three-phase admittance matrix, the equivalence principle, and the boundary; for example, the calculation formula of the three-phase admittance matrix can be:

[0123] ; (10)

[0124] where, represents the injection current of phase of node , and both represent any one of the three phases, represents different three-phase combinations, represents the element of the three-phase admittance matrix, represents the voltage of phase of node , represents the number of nodes.

[0125] Generate the equivalent model based on the sensitive area, the partition, the single equivalent component, the high level, the middle level, and the low level.

[0126] For example, use the sensitive area and the low level to construct the area that requires the highest-precision simulation, identify the boundary nodes of this area, which are the nodes directly connected to the external network (i.e., the medium-level Ward equivalent area); the medium level is the area between the core detailed area (low level) and the external main network (high level), construct an intermediate buffer area, identify the internal nodes and boundary nodes of the medium-level area, and the boundary nodes are divided into two categories: internal boundary nodes, which are the nodes connected to the boundary nodes of the low-level area, and external boundary nodes, which are the nodes connected to the high-level Thevenin equivalent points; the high level is the entire upstream system outside the medium-level area. For the convenience of integrating with the admittance matrix model, the Thevenin equivalent can be converted to the Norton equivalent. Combine the three-layer model through the boundary nodes. The low-level boundary nodes and the internal boundary nodes of the Ward equivalent are the same physical nodes, and they can be regarded as connection nodes. The external boundary nodes of the Ward equivalent are the acting points of the high-level Norton equivalent; stack the detailed admittance matrix of the low level, the Ward equivalent admittance matrix of the medium level, and the Norton admittance converted from the high level according to the node relationships they describe on the corresponding rows and columns. The self-admittance of the shared boundary nodes is the sum of the contributions from multiple parts; inject current sources. The injected current of the internal nodes of the low level comes from the load / power model inside this area, the injected current of the connection nodes comes from the local load / power that the node may be connected to (if any), plus the contributions of the currents of this node in the low level and the medium level (usually already included in the matrix equation, and the explicit current source term may be zero). The injected current of the external boundary nodes of the Ward is equal to the current source of the high-level Norton equivalent, so as to integrate the three levels to obtain a three-level integrated model.

[0127] Combine a single equivalent component, identify the key variable factors affecting the equivalent model (such as switch state combinations, typical DG output levels, typical load levels, key component fault states), form a discrete state set, each state represents a specific configuration or operating point of the system, assign the probability of its occurrence to each state, for each state, calculate a deterministic equivalent parameter in this state, and weight-average the equivalent parameters in all states according to their occurrence probabilities to obtain a single, static expected equivalent parameter, compress the dynamic three-level complex equivalent model that depends on a large number of time-varying / stochastic states into a static, single equivalent parameter in the sense of expectation, and this parameter itself can be used as a single equivalent component with a fixed parameter (such as an equivalent impedance, an equivalent admittance, a Norton circuit with an equivalent current source + admittance), replacing the previous three-level integrated model that needs to be maintained and calculated, so as to obtain the distribution network model. Among them, the specific steps for obtaining the sensitive area and several partitions of the distribution network include:

[0128] Based on the distribution network data, use sensitivity analysis to obtain the sensitivity of voltage to power, and obtain the sensitive area based on the sensitivity.

[0129] Cluster the distribution network data based on preset parameters to obtain the partition. For example, group nodes according to geographical location, load density, and electrical distance based on the K-means clustering algorithm.

[0130] Among them, the specific steps for constructing a single equivalent component include:

[0131] Generate the single equivalent component based on state enumeration and Monte Carlo simulation;

[0132] The calculation method for generating the single equivalent component is:

[0133] ; (11)

[0134] Among them, represents the equivalent parameter, represents the state, represents the set of states, represents the state probability of, represents the state parameter value of.

[0135] Among them, the specific steps for adjusting the electrical characteristics and grid structure of the equivalent model include:

[0136] Simplify the distributed parameters of the feeder in the equivalent model based on the lumped parameter model; the lumped parameter model can be:

[0137] ; (12)

[0138] Among them, represents the equivalent series impedance, represents the equivalent shunt capacitance, represents the impedance per unit length, represents the capacitance per unit length, represent the hyperbolic sine function and hyperbolic tangent function respectively, represents the propagation constant, represents the line length.

[0139] In this embodiment, the feeder mainly refers to a long feeder, and the distributed parameters may include series resistance, series inductance, shunt capacitance, and shunt conductance, etc.

[0140] Process the load dynamics of the equivalent model based on the Averaged-Value Model (AVM). Load Dynamics refers to the characteristics of load power (active / reactive) or current changing with time; its calculation formula can be:

[0141] ; (13)

[0142] Among them, represents the average current, represents the integration period, represents the instantaneous current.

[0143] Optimize the grid structure of the equivalent model based on the minimum spanning tree algorithm; its calculation formula can be:[[]]

[0144] ; (14)

[0145] Among them, represents the minimum spanning tree, represents the edge in the edge set, represents function, represents the edge set, represents the weight of the edge, which can be impedance, distance.

[0146] Calculate the line impedance of the equivalent model based on Carson's equation, which can accurately reflect the electrical characteristics of the line: Modify the traditional line impedance formula, incorporate the influence of factors such as earth conductivity and frequency, make the impedance calculation closer to the actual situation, quantify the electromagnetic coupling effect between conductors, and apply to: unbalanced three-phase lines, multi-circuit lines on the same tower, and distribution networks with densely arranged cables; Support the precise simplification of the equivalent model: Provide parameters for the π-type / T-type equivalent circuit, and can flexibly handle: overhead lines (three-phase, multi-circuit on the same tower, bundled conductors), underground cables (multi-core cables, influence of metal sheaths), and hybrid lines (connections between overhead lines and cable segments); Improve the accuracy of distribution network analysis: Accurate impedance values ensure the accuracy of voltage amplitude and phase angle in power flow calculations, especially in the case of long feeders or high loads, more realistically reflect the current distribution during faults, improve the reliability of protection setting, and the mutual impedance calculated by Carson's equation can be used in the sequence impedance matrix (positive sequence, negative sequence, zero sequence) to analyze asymmetric faults (such as single-phase ground short circuits) or load imbalance problems.

[0147] Therefore, applying Carson's equation to the equivalent model can correct the earth return effect, improve the impedance calculation accuracy; quantify multi-conductor coupling, support the analysis of unbalanced systems; provide key parameters for the equivalent circuit (π-type / T-type), ensure that the model can not only simplify calculations but also retain actual physical characteristics, and is more suitable for the planning, operation, and fault analysis of distribution networks.

[0148] Optimize the second model parameters of the equivalent model based on the genetic algorithm; in this embodiment, both the first model parameters and the second model parameters can include series parameters (longitudinal impedance): resistance, inductance, and series impedance, etc.; shunt parameters (transverse admittance): capacitance and susceptance, conductance, and shunt admittance, etc.

[0149] The specific steps for generating the distribution network model include:

[0150] Obtain a three-phase power flow model based on the Newton-Raphson algorithm. Based on the adjusted equivalent model and the three-phase power flow model, use existing fusion methods (such as the current injection method, voltage constraint method) to fuse the equivalent model and the three-phase power flow model to obtain the distribution network model.

[0151] The three-phase power flow model can be:

[0152] ; (15)

[0153] ; (16)

[0154] ; (17)

[0155] ; (18)

[0156] Where and respectively represent the active power and reactive power of node , and respectively represent the voltages of node node , represents the voltage phase angle difference between node node , represents the real part of the admittance matrix, represents the imaginary part of the admittance matrix, and both represent integers greater than or equal to 1, and both represent three phases, represents different three phases, represents the number of nodes.

[0157] Embodiment 2

[0158] Based on Embodiment 1, in this embodiment, the method further includes:

[0159] Obtain the number of simulations of the single equivalent component, and obtain the reliability of the equivalent model based on the number of simulations;

[0160] The calculation formula for obtaining the reliability is:

[0161] ; (19)

[0162] Where represents the reliability, represents the number of simulation times, represents the fault indication function of the th simulation, where

[0163] Embodiment 3

[0164] Based on the above embodiments, in this embodiment, the method further includes:

[0165] Obtain the energy data of distributed energy resources, construct a dynamic characteristic probability model based on the energy data, and obtain the distributed energy output characteristics based on the dynamic characteristic probability model;

[0166] The calculation formula of the dynamic characteristic probability model is:

[0167] ; (20)

[0168] where represents the output power of distributed energy resources, represents the mean value of distributed energy resources, represents the variance of the energy resources of the distributed partition unit, represents the probability distribution of the distributed energy output power.

[0169] The distributed energy output characteristics refer to the power output law shown by distributed energy resources (DER, such as photovoltaic, wind power, energy storage, micro gas turbines, etc.) during operation. Its core characteristics are time-varying, random, and controllable, and specifically include: time-scale characteristics, short-term fluctuations (seconds to minutes), intra-day variations (hourly level), seasonal patterns (monthly to annual), probability statistical characteristics, probability distribution, spatio-temporal correlation (output complementarity or regional aggregation effect between multiple DERs), controllable parameters, and adjustable characteristics such as the energy storage charge and discharge rate and the gas turbine ramp-up ability. The distributed energy output characteristics can be embedded into the equivalent model by using existing technologies.

[0170] Embodiment 4

[0171] Based on the above embodiments, in this embodiment, the method further includes:

[0172] Obtain the detailed model of the distribution network, compare the detailed model with the equivalent model to obtain an error value, determine whether the error value exceeds a preset threshold, and if so, adjust the first model parameters of the equivalent model, and obtain a first model based on the first model parameters, and update the equivalent model to the first model;

[0173] In this embodiment, the detailed model is a complete power grid model including all nodes and parameters.

[0174] The calculation formula for the error value is as follows:

[0175] ; (21)

[0176] Wherein, represents the error value, and respectively represent the voltage vectors of the equivalent model and the detailed model.

[0177] Embodiment 5

[0178] Based on the above embodiments, in this embodiment, the method further includes: updating the three-phase power flow model and the equivalent model based on a preset cloud computing simulation framework and objective function;

[0179] The objective function is:

[0180] ; (22)

[0181] Wherein, represents the objective function, and both represent weights, represents the accuracy, represents the speed.

[0182] In this embodiment, the simulation framework can be an existing cloud computing simulation framework that can realize real-time update of the model in the cloud.

[0183] Embodiment 6

[0184] Based on the above embodiments, in this embodiment, the method further includes:

[0185] Obtaining real-time power grid data, constructing a state space model, and updating the boundary and the equivalent model based on the state space model and the real-time power grid data to achieve equivalent adjustment in dynamic simulation;

[0186] The calculation formula for updating the equivalent model is:

[0187] ; (23)

[0188] Wherein, represents the power adjustment amount, represents the proportional gain, represents the integral gain, represents the actually measured power, represents the simulated power;

[0189] The calculation formula for the state space model is:

[0190] ; (24)

[0191] Among them, represents the system state at time represents the system state at time represents the real-time power grid data input at time represents the output power grid data at time represents an n×n state matrix, where n is the number of nodes, represents an n×m input matrix, where m is the dimension of the input variables, represents a p×n output matrix, where p is the dimension of the output variables, represents a through matrix.

[0192] In this embodiment, the method further includes:

[0193] Based on the real-time power grid data, Bayesian method, and particle swarm optimization algorithm, adjust the model parameters to achieve model optimization. The particle swarm optimization algorithm can be:

[0194] ; (25)

[0195] Among them, represents the particle velocity at time represents the particle velocity at time represents the inertia weight factor, and both represent learning factors, and both represent random numbers between [0, 1], represents the local optimal solution, represents the global optimal solution, represents the particle position at time

[0196] It is also possible to analyze the model stability based on the Lyapunov stability method, and its calculation formula can be:

[0197] ; (26)

[0198] Among them, represents the energy function, represents the time derivative. If the above conditions are met, it means the model is stable.

[0199] Adopting a dynamic feedback mechanism and a scheme for automatically correcting the model, it can be adjusted immediately according to the actual operating state of the power grid to optimize the real-time operation control of the distribution network.

[0200] The equivalent method of the present invention ensures that the core characteristics of power grid operation are retained while reducing the amount of calculation through precise boundary condition definition, dynamic adjustment and correction. This makes the calculation results of the model closer to the actual power grid operation state, thereby improving the accuracy and reliability of the simulation results.

[0201] Core features include:

[0202] Retention of key electrical characteristics: In the equivalent modeling process, the key electrical characteristics of the power grid, such as impedance and voltage drop, are retained. In the process of electrical characteristic simplification, although the distributed parameters of the long feeder are simplified by the centralized parameter model, this simplification is based on reasonable engineering approximation, and the simplified model is ensured to accurately reflect the actual electrical characteristics of the power grid through computational optimization algorithms (such as parallel computing, multi-threaded processing, etc.). At the same time, in the equivalent model verification process, by comparing the power flow and voltage distribution of the equivalent model and the detailed model, when the error exceeds the preset threshold, the equivalent model parameters will be adjusted and re-verified, thereby ensuring that the core electrical characteristics of the power grid operation are retained.

[0203] Accuracy of equivalent modeling: In the equivalent modeling process, both the high-level Thevenin equivalent model and the mid-level Ward equivalent model are modeled based on accurate electrical parameters such as the admittance matrix and voltage phase difference of the power grid. These models can accurately describe the core characteristics of the power grid such as the voltage-power relationship and power flow distribution. For example, the equivalent voltage V th and equivalent impedance Z th It is accurately extracted from the node admittance matrix and can truly reflect the electrical characteristics of the power grid at the node.

[0204] Dynamic characteristic modeling: The distribution network has dynamic characteristics during operation, such as changes in load, fluctuations in the output of distributed energy, etc. The present invention adjusts the equivalent model according to the real-time feedback results during the dynamic simulation process through feedback from dynamic simulation and equivalent methods. This dynamic simulation feedback mechanism can ensure that the equivalent model can reflect the dynamic operating characteristics of the power grid in real time. For example, by establishing a state space model to represent the feedback process, taking real-time measurement data as input, and adjusting the parameters of the equivalent model, the model can accurately capture the dynamic changes of the power grid and retain the dynamic core characteristics of the power grid operation.

[0205] Attention to core equipment and lines: During the equivalent modeling process, although some power grid structures are simplified, the present invention focuses on the core equipment and lines in the power grid, such as the voltage distribution of the main line, etc. For these key parts, a more accurate modeling method is adopted to ensure that while simplifying the model, the core characteristics of the power grid operation are retained, such as the voltage quality and power transmission characteristics of the main line, etc.

[0206] The model of the present invention has stronger adaptability in the high - proportion photovoltaic distribution area, can effectively reduce calculation errors, and provide more accurate simulation results, especially suitable for the simulation of distribution networks with high - proportion distributed energy such as photovoltaic. The specific reasons are as follows:

[0207] Modeling of the dynamic characteristics of distributed energy resources:

[0208] Regarding the dynamic characteristics of distributed energy such as photovoltaic, the equivalent method in this embodiment particularly considers its probability model. For example, the output power of photovoltaic power sources is intermittent and volatile due to natural factors. The present invention adopts a dynamic characteristic probability model to describe the output power of distributed energy resources, including parameters such as its mean and variance. This probability model can more accurately reflect the actual operation characteristics of distributed energy. Considering these characteristics in the equivalent modeling process enables the equivalent model to better adapt to the distribution network with high - proportion distributed energy such as photovoltaic.

[0209] Compatibility between the equivalent model and distributed energy:

[0210] The equivalent modeling method of this embodiment allows new energy power sources and energy storage devices to be connected in the distribution area and the main distribution lines. In the equivalent model, through reasonable boundary condition definition and equivalent principles, the power grid structure around the distributed energy connection point can be reasonably equivalently processed. For example, for the distribution area lines simplified by the line equivalent method, the influence of distributed energy connection on the electrical characteristics of the lines is considered at the same time, enabling the equivalent model to be compatible with the existence of distributed energy and accurately reflect its impact on the power grid operation.

[0211] Adaptation to the source - load interaction relationship:

[0212] In the distribution network with high - proportion distributed energy such as photovoltaic, there is a complex source - load interaction relationship between distributed energy and traditional loads. This embodiment can better handle this source - load interaction relationship by calculating the sensitivity of voltage to power to determine the area that needs to be detailedly modeled. At the same time, using state enumeration and Monte Carlo simulation to generate a single equivalent component, the influence of different operating states of distributed energy on the power grid is considered. This consideration of the source - load interaction relationship enables the equivalent model to adapt to the changes in the power grid operation characteristics after high - proportion distributed energy access.

[0213] Error control and accuracy guarantee:

[0214] Aiming at the problem of large errors in traditional equivalent methods when high - proportion distributed energy such as photovoltaic energy is connected, in this embodiment, the error is controlled through the equivalent model verification and correction process. During the equivalent model verification process, the power flow and voltage distribution of the equivalent model are compared with those of the detailed model. When the error exceeds the preset threshold, the parameters of the equivalent model are adjusted and verified again. In addition, the model is corrected by methods such as the Bayesian method and the particle swarm optimization algorithm, which can effectively reduce the error and improve the accuracy of the equivalent model in the simulation of high - proportion distributed energy distribution networks.

[0215] Embodiment 7

[0216] Based on the above - mentioned embodiment, this embodiment also provides a simplified calculation system for urban distribution networks based on multi - level equivalent modeling. The system further includes:

[0217] Boundary unit: used to obtain distribution network data and define the boundary of the distribution network based on the distribution network data;

[0218] Equivalent model unit: used to generate an equivalent model based on the complex levels, equivalent principles and the boundary of the distribution network;

[0219] The complex levels include high levels, middle levels and low levels, and the equivalent principles include node equivalent principle and line equivalent principle;

[0220] The equivalent model unit specifically includes:

[0221] Partition unit: used to obtain the sensitive areas and several partitions of the distribution network;

[0222] Component unit: used to construct a single equivalent component;

[0223] Level unit: used to obtain the high level based on the Thevenin equivalent circuit model, the equivalent principle and the boundary, obtain the middle level based on the Ward equivalent model, the equivalent principle and the boundary, and obtain the low level based on the three - phase admittance matrix, the equivalent principle and the boundary;

[0224] Generation unit: used to generate the equivalent model based on the sensitive areas, the partitions, the single equivalent component, the high level, the middle level and the low level;

[0225] Simplified model unit: used to adjust the electrical characteristics and grid structure of the equivalent model to generate a distribution network model.

[0226] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0227] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A simplified calculation method for urban distribution networks based on multi-level equivalent modeling, characterized in that, The method includes: Obtain distribution network data and define the boundary of the distribution network based on the distribution network data; Generate an equivalent model based on the complex hierarchy, equivalent principle, and the boundary of the distribution network; Adjust the electrical characteristics and grid structure of the equivalent model to generate a distribution network model; The complex hierarchy includes a high level, a middle level, and a low level, and the equivalent principle includes a node equivalent principle and a line equivalent principle; The specific steps for generating the equivalent model include: Obtain the sensitive area and several partitions of the distribution network; Construct a single equivalent component; Obtain the high level based on the Thevenin equivalent circuit model, the equivalent principle, and the boundary, obtain the middle level based on the Ward equivalent model, the equivalent principle, and the boundary, obtain the low level based on the three-phase admittance matrix, the equivalent principle, and the boundary, and generate the equivalent model based on the sensitive area, the partitions, the single equivalent component, the high level, the middle level, and the low level.

2. The simplified calculation method for urban distribution network based on multi - level equivalent modeling according to claim 1, characterized in that, The specific steps for obtaining the sensitive area and several partitions of the distribution network include: Based on the distribution network data, obtain the sensitivity of voltage to power, and obtain the sensitive area based on the sensitivity; Cluster the distribution network data based on preset parameters to obtain the partitions.

3. The simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 2, wherein The specific steps for constructing a single equivalent component include: Generate the single equivalent component based on state enumeration and Monte Carlo method simulation; The calculation method for generating the single equivalent component is: ； Among them, represents an equivalent parameter, represents a state, represents a set of states, represents a state probability, represents a state parameter value.

4. A simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 3, characterized in that The method further includes: Obtain the number of simulations of the single equivalent component and obtain the reliability of the equivalent model based on the number of simulations; The calculation formula for obtaining the reliability is: ； in, Represents reliability, represents the number of simulations, Indicates The fault indication function of the simulation, Represents an integer greater than or equal to 1.

5. A simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 1, characterized in that The method further includes: Obtain the energy data of distributed energy resources, construct a dynamic characteristic probability model based on the energy data, and obtain the output characteristics of distributed energy based on the dynamic characteristic probability model; The calculation formula for the dynamic characteristic probability model is: ； Among them, represents the output power of distributed energy resources, represents the mean value of distributed energy resources, represents the variance of distributed energy resources, represents the probability distribution of the output power of distributed energy.

6. The simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 1, characterized in that The method further includes: Obtain the detailed model of the distribution network, compare the detailed model with the equivalent model to obtain an error value, determine whether the error value exceeds a preset threshold, if so, adjust the first model parameter of the equivalent model, obtain a first model based on the first model parameter, and update the equivalent model to the first model; The calculation formula for the error value is: ； Among them, represents the error value, and represent the voltage vectors of the equivalent model and the detailed model, respectively.

7. A simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 1, characterized in that The specific steps for adjusting the electrical characteristics and grid structure of the equivalent model include: Simplify the distributed parameters of the feeder in the equivalent model based on the lumped parameter model, process the load dynamics of the equivalent model based on the average value model, optimize the grid structure of the equivalent model based on the minimum spanning tree algorithm, calculate the line impedance of the equivalent model based on Carson's equation, and optimize the second model parameter of the equivalent model based on the genetic algorithm; The specific steps for generating a distribution network model include: Obtain a three-phase power flow model based on the Newton-Raphson algorithm, and obtain the distribution network model based on the adjusted equivalent model and the three-phase power flow model.

8. A simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 7, characterized in that, The method further includes: Update the three-phase power flow model and the equivalent model based on a preset cloud computing simulation framework and objective function; The objective function is: ； Among them, represents the objective function, and both represent weights, represents the precision, represents the speed.

9. A simplified calculation method for urban distribution network based on multi-level equivalent modeling according to claim 1, characterized in that The method further includes: Obtain real-time power grid data, construct a state space model, and update the boundary and the equivalent model based on the state space model and the real-time power grid data; The calculation formula for updating the equivalent model is: ； Among them, represents the power adjustment amount, represents the proportional gain, represents the integral gain, represents the actually measured power, represents the analog power; The calculation formula for the state space model is: ； Among them, represents the system state at time represents the system state at time represents the real-time power grid data input at time represents the output power grid data at time represents the state matrix, represents the input matrix, represents the output matrix, represents the direct-through matrix.

10. A simplified calculation system for urban distribution network based on multi-level equivalent modeling, characterized in that The system further includes: Boundary unit: used to obtain distribution network data and define the boundary of the distribution network based on the distribution network data; Equivalent model unit: used to generate an equivalent model based on the complex levels of the distribution network, the equivalent principle, and the boundary; The complex levels include high level, middle level, and low level, and the equivalent principle includes node equivalent principle and line equivalent principle; The equivalent model unit specifically includes: Partition unit: used to obtain the sensitive area and several partitions of the distribution network; Component unit: used to construct a single equivalent component; Level unit: used to obtain the high level based on the Thevenin equivalent circuit model, the equivalent principle, and the boundary, obtain the middle level based on the Ward equivalent model, the equivalent principle, and the boundary, and obtain the low level based on the three-phase admittance matrix, the equivalent principle, and the boundary; Generation unit: used to generate the equivalent model based on the sensitive area, the partitions, the single equivalent component, the high level, the middle level, and the low level; Simplified model unit: used to adjust the electrical characteristics and grid structure of the equivalent model to generate a distribution network model.

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