Simulation calculation model construction method for active power distribution network

Abstract

The invention discloses a method for constructing a simulation calculation model of an active power distribution network, and belongs to the technical field of power grid engineering. The problem that an existing simulation method is low in accuracy is solved, a mathematical model is established according to the structural characteristics and operation rules of the active power distribution network, simulation is conducted through real events, and the actual situation of the active power distribution network is accurately reflected; rich basis is provided for parameterization processing of a mathematical model by using information and data of a real event, global optimization is performed on parameters of the mathematical model by using a genetic algorithm, optimal control on the performance of the active power distribution network is realized, the optimal performance under specific parameters can be reflected, different parameter settings can be adapted, and the optimal control on the performance of the active power distribution network is realized. Therefore, the method plays a role in a wider scene; and by comparing simulation results of the mathematical model under different parameters, an optimal parameter combination is screened out, so that an active power distribution network simulation calculation model with practical value is formed, and a user is assisted to better understand and predict behaviors of the active power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid engineering, and in particular to a method for constructing a simulation calculation model of an active power distribution network. Background Art

[0002] With the development of society, the power system has higher and higher requirements for stability and reliability. Active distribution network refers to a distribution network that has a large number of distributed power sources and bidirectional power flow, also known as active distribution network; active distribution network is one of the important components of the power system, and its operating status is directly related to the normal operation of society; for example, by integrating a variety of distributed energy systems, it can not only receive electricity from the traditional central power grid, but also feed the locally generated electricity back to the power grid, thereby providing support during peak load of the power grid, reducing the pressure on the main power grid, and enhancing the stability of the entire power system; secondly, active distribution network can reduce the loss of long-distance transmission, thereby reducing energy costs, helping to reduce dependence on external energy, and enhancing the flexibility and reliability of the power system; therefore, how to effectively control the performance of active distribution network is an important task to ensure the safety and stability of the power system.

[0003] However, in the prior art, although the simulation software can simulate the behavior of the circuit, due to the difference between the simulation environment and the actual environment, the simulation results may be deviated in actual applications, thereby causing failures or accidents in the power system.

[0004] Therefore, it does not meet the existing needs, and we propose a method for constructing an active distribution network simulation calculation model. Summary of the invention

[0005] The purpose of the present invention is to provide a method for constructing an active distribution network simulation calculation model. By establishing a mathematical model according to the structural characteristics and operating laws of the active distribution network and using real events for simulation, the actual situation of the active distribution network is accurately reflected; the information and data of the real events are used to provide a rich basis for parameterization processing of the mathematical model, and the parameters of the mathematical model are globally optimized using a genetic algorithm to achieve optimal control of the performance of the active distribution network, so that it can not only reflect the optimal performance under specific parameters, but also adapt to different parameter settings, so as to play a role in a wider range of scenarios; and then by comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is screened out, thereby forming an active distribution network simulation calculation model with practical value, thereby assisting users to better understand and predict the behavior of the active distribution network, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for constructing an active distribution network simulation calculation model includes the following steps:

[0008] Step 1: Obtain the structural characteristics and operating rules of the active distribution network, and establish a mathematical model that can reflect the operating status of the active distribution network;

[0009] Step 2: Obtain information and data of real events in the active distribution network, and use discrete event simulation method to map the real events into a mathematical model for simulation;

[0010] Step 3: Analyze the information and data of real events to obtain actual constraints, and parameterize the mathematical model by combining physical constraints, power electronics characteristics and control strategies;

[0011] Step 4: Use genetic algorithm to globally optimize the parameters of the mathematical model to achieve optimal control of the performance of the active distribution network;

[0012] Step 5: By comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is screened out to form an active distribution network simulation calculation model with practical value.

[0013] Furthermore, the step 1 of obtaining the structural characteristics and operating rules of the active distribution network specifically includes the following steps:

[0014] Collect the basic structure and components of the active distribution network. The basic structure includes: the active distribution network adopts a closed-loop design and open-loop operation structure; the components include: the active distribution network is composed of overhead lines, cables, distribution transformers, switchgear, and reactive compensation capacitors; collect the operating laws of the active distribution network, including: the bidirectional flow of power flow and fault current and the regulation of distributed power sources.

[0015] Furthermore, the step 1 of establishing a mathematical model capable of reflecting the operating state of the active distribution network specifically includes the following steps:

[0016] Determine the basic assumptions and simplified conditions of the mathematical model. According to the actual operation of the active distribution network, determine the basic assumptions and simplified conditions of the mathematical model; establish the corresponding mathematical model according to the structural characteristics of the active distribution network; including: describing the mathematical expressions of each component in the active distribution network, and the connection relationship between each device; define all necessary variables and parameters in the mathematical model, the variables and parameters include: voltage, current and power electrical quantities, as well as network structure parameters.

[0017] Furthermore, the step 2 of obtaining the information and data of the real events of the active distribution network specifically includes the following steps:

[0018] Obtain information about real events in the active distribution network: collect test areas of real events, obtain geographical information, test objectives and test results of the areas, and determine the number of lines, power supply types and line wattage required for the test areas;

[0019] Obtaining the operating data of the active distribution network: real-time collection of the operating data of the active distribution network, including voltage, current and power parameters; cleaning and normalizing the operating data, deleting erroneous data and duplicate data, and supplementing the missing data using the average method; using data analysis technology to process and analyze the collected data to identify the operating rules and potential problems of the active distribution network.

[0020] Furthermore, in step 2, a discrete event simulation method is used to map the real event into a mathematical model for simulation, which specifically includes the following steps:

[0021] The event scheduling method in the discrete event simulation method is used to map real events into the mathematical model, define various actual events in the distribution network, and set a unique identifier for each event; establish an event table in the mathematical model to record all predefined events and their occurrence times. When the simulation starts, the time control module in the simulation system scans the event table to determine the next event to occur, so that the simulation clock is advanced to the occurrence time of the event; when the next event is determined, the mathematical model calls the event processing module corresponding to the event to execute the corresponding event processing program; after the event processing is completed, the state of the simulation system is updated, and the next event to occur is determined; repeat the above simulation steps until the simulation of the real event predetermined in the event table is completed.

[0022] Furthermore, after each event is executed, the event needs to be removed from the event table until all events in the event table are executed.

[0023] Furthermore, the step 3 of analyzing the information and data of the real event to obtain the actual constraint conditions specifically includes the following steps:

[0024] The operating data of each device in the real event under normal conditions is obtained, and the operating parameter threshold of each device is obtained by extracting the average value method as the actual physical constraint condition.

[0025] Furthermore, the step three combines physical constraints, power electronics characteristics and control strategies, and specifically includes the following steps:

[0026] Parameterize the mathematical model using physical constraints: Set and adjust the model parameters based on the actual physical characteristics of the active distribution network, such as the grid topology, equipment parameters, etc., to ensure that the physical constraints of the model are consistent with the operating status of the actual distribution network;

[0027] Parameterize the mathematical model using the characteristics of power electronics: obtain the voltage, current and power parameters of the active distribution network, build an electrical characteristic model that can characterize the electrical characteristics of the active distribution network under different conditions, and obtain the electrical parameter characteristics of the active distribution network through the electrical characteristic model; and adjust the parameters of the mathematical model based on the electrical parameter characteristics of the active distribution network to optimize the performance of the mathematical model;

[0028] Parameterize the mathematical model using the control strategy: select the appropriate control strategy according to the control objective, and parameterize the control strategy based on the mathematical model, including: determining the initial value of the control parameter, the adjustment range, and the selection of the optimization algorithm.

[0029] Furthermore, in step 4, the genetic algorithm is used to perform global optimization on the parameters of the mathematical model, which specifically includes the following steps:

[0030] Encoding and decoding: encode the mathematical model parameters in binary, and then convert the optimized binary string back to the actual parameter value; initialize the population: randomly generate an initial population, each individual represents a possible solution, and the size of the population and the number of generations of evolution are set according to the complexity of the problem and computing resources; fitness evaluation: calculate the fitness of each individual; selection, crossover and mutation: select excellent individuals according to fitness for crossover to generate new individuals; iterative update: repeat the selection, crossover and mutation process until the preset number of iterations is reached or the predetermined stopping condition is met, and the individual finally obtained is the optimal solution; simulation verification: perform simulation verification in the mathematical model to evaluate the impact of the optimized parameter configuration on the performance, and verify the effectiveness of the genetic algorithm by comparing the performance indicators before and after optimization.

[0031] Furthermore, the active distribution network simulation calculation model with practical value is formed in step 5, which specifically includes the following steps:

[0032] Use different parameter combinations to conduct simulation experiments and record the simulation results for each combination; including key indicators such as computational efficiency and data accuracy; compare the simulation results for different parameter combinations and evaluate the performance of each combination in terms of computational efficiency and accuracy; based on the simulation results, select the parameter combination with the best performance in terms of computational efficiency and accuracy; further verify the selected optimal parameter combination to ensure its stability and reliability under various working conditions.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention establishes a mathematical model according to the structural characteristics and operation rules of the active distribution network, maps real events into the mathematical model for simulation, thereby accurately reflecting the actual situation of the active distribution network; analyzes the information and data of the real events to obtain actual constraints, and performs parameterization processing on the mathematical model, thereby providing a rich basis for subsequent parameterization processing of the mathematical model; uses a genetic algorithm to perform global optimization on the parameters of the mathematical model, and realizes optimal control of the performance of the active distribution network, so that it can not only reflect the optimal performance under specific parameters, but also adapt to different parameter settings, thereby playing a role in a wider range of scenarios; and then compares the simulation results of the mathematical model under different parameters, screens out the optimal parameter combination, thereby forming an active distribution network simulation calculation model with practical value, thereby assisting users to better understand and predict the behavior of the active distribution network, providing guidance for users in actual operations, and making the most appropriate decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flow chart of the method for constructing an active power distribution network simulation calculation model. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] In order to solve the technical problem that although the simulation software can simulate the behavior of the circuit, due to the difference between the simulation environment and the actual environment, the simulation results may be biased in actual application, thus causing failures or accidents in the power system, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0038] The method for constructing an active distribution network simulation calculation model includes the following steps:

[0039] Step 1: Obtain the structural characteristics and operating rules of the active distribution network, and establish a mathematical model that can reflect the operating status of the active distribution network; specifically, the following steps are included:

[0040] The basic structure and components of the active distribution network are collected. The basic structure includes: the active distribution network adopts a closed-loop design and open-loop operation structure; it aims to improve the flexibility of operation and the reliability of power supply. The open-loop operation can limit the short-circuit fault current, prevent the circuit breaker from exceeding the interrupting capacity and exploding, and control the scope of the fault to avoid the expansion of the fault power outage scope; the components include: the active distribution network is composed of overhead lines, cables, distribution transformers, switchgear, and reactive compensation capacitors; through the joint action of the above equipment, it is ensured that the power in the active distribution network can be effectively distributed to various users.

[0041] Collect the operating rules of the active distribution network, including: the bidirectional flow of power flow and fault current and the regulation of distributed power sources. The bidirectional flow of power flow and fault current is: the power flow and fault current in the active distribution network can flow in both directions, which is different from the unidirectional flow of the traditional distribution network. It may require corresponding adjustments and improvements to the power flow analysis and fault analysis methods of the distribution network; the regulation of distributed power sources is: the distributed power sources in the active distribution network actively adjust their reactive and active outputs, and coordinate and control the distribution network through modern communication means to achieve the optimized operation of the distribution network; through the above steps, we can fully understand the structural characteristics and operating rules of the active distribution network.

[0042] Determine the basic assumptions and simplified conditions of the mathematical model. According to the actual operation of the active distribution network, determine the basic assumptions and simplified conditions of the mathematical model; for example: assume that the load in the distribution network is constant, or ignore the influence of certain minor factors; establish a corresponding mathematical model according to the structural characteristics of the active distribution network; including: mathematical expressions describing each component in the active distribution network, such as transformers, lines, loads, etc., and the connection relationship between each device; define all necessary variables and parameters in the mathematical model. The variables and parameters include: voltage, current and power electrical quantities, as well as network structure parameters, such as: conductor cross-section, network wiring method, etc., so as to ensure the accuracy of obtaining the structural characteristics and operating laws of the active distribution network, and then ensure that strong data support is provided for building the mathematical model.

[0043] Step 2: Obtain information and data of real events in the active distribution network, and use discrete event simulation method to map the real events into a mathematical model for simulation; specifically, the following steps are included:

[0044] Obtain information about real events in the active distribution network: collect test areas of real events, obtain geographical information, test objectives and test results of the areas, and determine the number of lines, power supply types and line wattage required for the test areas;

[0045] Obtaining the operating data of the active distribution network: Using smart meters and sensor equipment to collect the operating data of the active distribution network in real time, including voltage, current and power parameters; cleaning and normalizing the operating data, deleting erroneous data and duplicate data, and using the average method to supplement the missing data, so as to ensure the accuracy and validity of the operating data, thereby ensuring the authenticity of the mathematical model when simulating real events; using data analysis technology to process and analyze the collected data to identify the operating laws and potential problems of the active distribution network; specifically, extracting key features from the processed operating data, such as voltage fluctuations, current changes, equipment status, etc., to identify the operating rules and potential problems of the active distribution network. The operating status of the power grid can be monitored, and potential voltage instability and current overload problems can be discovered and dealt with in a timely manner. Machine learning algorithms can then be used to classify and identify features. For example, cluster analysis can be used to identify different types of operating modes, and classification algorithms can be used to identify abnormal modes. This can effectively monitor the operating status of each power device in the active distribution network, and equipment failure or aging problems can be discovered in a timely manner to avoid power outages caused by equipment damage. Statistical analysis, time series analysis and other methods can be used to detect abnormal conditions in the active distribution network, such as overload and short circuit, so as to accurately predict the future operating trend of the active distribution network, so as to take measures in advance to deal with potential problems, provide emergency repair suggestions, and improve fault handling efficiency.

[0046] The event scheduling method in the discrete event simulation method is used to map real events into the mathematical model, and various actual events in the distribution network are defined, such as: the start, stop, failure, etc. of each device, and a unique identifier is set for each event so that it can be accurately identified and executed in the simulation model; an event table is established in the mathematical model to record all predefined events and their occurrence times, and each event record in the event table includes at least the occurrence time of the event and the identifier of the event; at the beginning of the simulation, the event table is scanned by the time control module in the simulation system to determine the next event to occur, so that the simulation clock is advanced to the occurrence time of the event; when the next event is determined, the mathematical model calls the event processing module corresponding to the event to execute the corresponding event processing program; after the event processing is completed, the state of the simulation system is updated, and the next event to occur is determined; the above simulation steps are repeated until the real event predetermined in the event table is simulated. end; after each event is executed, the event needs to be removed from the event table until all events in the event table are executed; specifically, the event scheduling method uses events as the basic unit of the analysis system; by defining events, the time sequence of event occurrence and changes in system status, and using events to drive the operation of the simulation mathematical model; the events in the mathematical model are stored in the "event table", and the first event that occurs is selected from the event table through the time control module in the simulation system; the simulation clock is reset, and the event processing module corresponding to the event is called for simulation, and after the simulation is completed, the simulation system status is updated, and then the next event to occur is determined; after the current event ends, the time control module is returned; the selection and processing of the scheduled events in the event table are repeated until the simulation ends; through the above steps, the actual events in the active distribution network can be accurately mapped to the simulation model to perform dynamic simulation and analysis of the power system.

[0047] The beneficial effects achieved by the above content are as follows: by constructing a mathematical model that can reflect the operating status of the active distribution network based on the structural characteristics and operating laws of the active distribution network, it not only accurately reflects the actual situation of the active distribution network, but also can map real events into the model for simulation through discrete event simulation methods, further improving the practicability of the mathematical model.

[0048] Step 3: Analyze the information and data of real events to obtain actual constraints, and parameterize the mathematical model in combination with physical constraints, power electronics characteristics and control strategies; specifically, the following steps are included:

[0049] In the modeling process, the actual constraints of physical phenomena need to be taken into account; the operating data of each device in the real event under normal conditions are obtained, and the operating parameter thresholds of each device are obtained by extracting the average value method as the actual physical constraints; for example, in an active distribution network, the constraints of current and voltage must meet the limitations of the actual equipment, such as the maximum current, voltage range, etc. The above constraints are introduced into the mathematical model in a parameterized manner to ensure that the output of the mathematical model conforms to the laws of physics;

[0050] Parameterize the mathematical model using physical constraints: Set and adjust the model parameters based on the actual physical characteristics of the active distribution network, such as the grid topology, equipment parameters, etc., to ensure that the physical constraints of the model are consistent with the operating status of the actual distribution network;

[0051] Parameterize the mathematical model using the characteristics of power electronics: obtain the voltage, current and power parameters of the active distribution network, construct an electrical characteristic model that can characterize the electrical characteristics of the active distribution network under different conditions, and obtain the electrical parameter characteristics of the active distribution network through the electrical characteristic model; and adjust the parameters of the mathematical model based on the electrical parameter characteristics of the active distribution network to optimize the performance of the mathematical model; specifically, when constructing the model, the impact of distributed renewable energy needs to be considered; including: superimposing the typical curves of each load type according to the composition characteristics of the provincial power grid load to obtain the typical superimposed curve of the load; at the same time, superimposing the typical curves of each load type according to the composition of the distributed renewable energy The typical output curves of distributed renewable energy of different types are superimposed to obtain the typical output superposition curve of distributed renewable energy; the typical load curve of the system is corrected by presetting the proportion of the typical output superposition curve of distributed renewable energy in the typical load superposition curve, thereby more accurately reflecting the load changes in actual operation; the preset system load reference value is then used in combination with the preset partition ratio to generate the load curve of each partition; the load curve of each partition is then decomposed using the normal distribution to generate the load curve of each node in each partition, characterizing the power consumption characteristics of the load of each node; for example, by adjusting the parameters in the control strategy, the efficiency and stability of the active distribution network can be optimized;

[0052] Parameterize the mathematical model using the control strategy: select the appropriate control strategy according to the control target, for example: use power electronic devices such as intelligent soft switches and flexible ring network devices to decouple the control of active power and reactive power to improve the flexibility of power transmission; based on the mathematical model, parameterize the control strategy, including: determine the initial value of the control parameter, the adjustment range and the selection of the optimization algorithm; for example: use the improved genetic algorithm to solve to optimize the operating state of the system. Through the parameterization method, the parameters in the control strategy can be optimized to achieve better system response and control effect; specifically, in the active distribution network, the control strategy is the key to regulating the system behavior; for example: the ratio in the controller For example, the integral and differential parameters can be adjusted through parameterization methods to achieve better control effects; specifically, through parameterized models, the behavior of the simulation system under different conditions can be predicted, so as to perform fault diagnosis and prevention; during the simulation and testing process of the simulation system, the parameterization method can quickly adjust the model parameters to simulate different working conditions and improve the test efficiency and accuracy; in summary, parameterizing the model using physical constraints, power electronics characteristics and control strategies can improve the performance and reliability of the simulation system, and then a model that can reflect the actual operation of the active distribution network can be constructed, and the impact of distributed renewable energy can be considered, so as to better manage and optimize the power system.

[0053] The beneficial effects achieved by the above contents are as follows: by analyzing the information and data of real events in the active distribution network, the actual constraints are obtained, thus providing a rich basis for the subsequent parameterization of the mathematical model.

[0054] Step 4: Use genetic algorithm to globally optimize the parameters of the mathematical model to achieve optimal control of the performance of the active distribution network; specifically, the following steps are included:

[0055] Encoding and decoding: Binary encoding of mathematical model parameters. In the optimization of active distribution networks, encoding usually represents model parameters as binary strings. The fitness function calculates the pros and cons of each solution according to the objective function. The optimized binary string is then converted back to the actual parameter value. For example, voltage control parameters and configuration of reactive power compensation devices are converted into binary strings. Initialization of population: Randomly generate an initial population, each individual represents a possible solution, and the size of the population and the number of generations of evolution are set according to the complexity of the problem and the computing resources. Fitness evaluation: Calculate the fitness of each individual, that is, evaluate each according to the objective function. The performance of the solution, individuals with high fitness are more likely to be selected in the next generation; selection, crossover and mutation: select excellent individuals according to fitness for crossover to generate new individuals; the mutation operation randomly changes certain genes to increase the diversity of the population and prevent premature convergence; iterative update: repeat the selection, crossover and mutation process until the preset number of iterations is reached or the predetermined stopping condition is met, such as: the fitness is no longer significantly improved, and the individual finally obtained is the optimal solution; simulation verification: perform simulation verification in a mathematical model to evaluate the impact of the optimized parameter configuration on the performance, and verify the effectiveness of the genetic algorithm by comparing the performance indicators before and after optimization.

[0056] The beneficial effects achieved by the above content are: the parameters of the mathematical model are globally optimized through genetic algorithms in order to achieve optimal control of the performance of the active distribution network, which can not only reflect the optimal performance under specific parameters, but also adapt to different parameter settings, thereby playing a role in a wider range of scenarios.

[0057] Step 5: By comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is screened out to form an active distribution network simulation calculation model with practical value; specifically, the following steps are included:

[0058] Use different parameter combinations to conduct simulation experiments, and record the simulation results under each combination; including: key indicators such as computational efficiency and data accuracy; compare the simulation results under different parameter combinations, and evaluate the performance of each combination in terms of computational efficiency and accuracy; in specific implementation, use charts and other methods to intuitively display the impact of different parameters on the simulation results; based on the simulation results, screen out the parameter combination with the best performance in terms of computational efficiency and accuracy, which usually involves comprehensive consideration of multiple indicators to ensure that the selected parameters can guarantee both computational efficiency and accuracy requirements; further verify the selected optimal parameter combination to ensure its stability and reliability under various working conditions; through the above steps, the optimal parameter combination in the mathematical model of the active distribution network can be systematically screened out, thereby improving the accuracy and computational efficiency of the simulation results.

[0059] The beneficial effects achieved by the above content are: by simulating the mathematical model under different parameters, comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is selected, and a simulation calculation model of the active distribution network with practical value is formed accordingly; thus, it can not only assist users to better understand and predict the behavior of the active distribution network, but also provide guidance for users in actual operations and make the most appropriate decisions.

[0060] Working principle: A mathematical model is established according to the structural characteristics and operating rules of the active distribution network, and the discrete event simulation method is used to map the real events in the active distribution network into the mathematical model for simulation; the information and data of the real events are analyzed to obtain the actual constraints, and the mathematical model is parameterized in combination with physical constraints, power electronics characteristics and control strategies; the genetic algorithm is then used to globally optimize the parameters of the mathematical model to achieve optimal control of the performance of the active distribution network; by comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is selected, thereby forming a simulation calculation model of the active distribution network with practical value.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0062] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for constructing an active distribution network simulation calculation model, characterized in that: The following steps are involved: Step 1: Obtain the structural characteristics and operating rules of the active distribution network, and establish a mathematical model that can reflect the operating status of the active distribution network; Step 2: Obtain information and data of real events in the active distribution network, and use discrete event simulation method to map the real events into a mathematical model for simulation; Step 3: Analyze the information and data of real events to obtain actual constraints, and parameterize the mathematical model by combining physical constraints, power electronics characteristics and control strategies; Step 4: Use genetic algorithm to globally optimize the parameters of the mathematical model to achieve optimal control of the performance of the active distribution network; Step 5: By comparing the simulation results of the mathematical model under different parameters, the optimal parameter combination is screened out to form an active distribution network simulation calculation model with practical value.

2. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The obtaining of the structural characteristics and operating rules of the active distribution network specifically includes the following steps: Collect the basic structure and components of the active distribution network. The basic structure includes: the active distribution network adopts a closed-loop design and open-loop operation structure; the components include: the active distribution network is composed of overhead lines, cables, distribution transformers, switchgear, and reactive compensation capacitors; collect the operating laws of the active distribution network, including: the bidirectional flow of power flow and fault current and the regulation of distributed power sources.

3. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The step of establishing a mathematical model capable of reflecting the operating state of the active power distribution network specifically includes the following steps: Determine the basic assumptions and simplified conditions of the mathematical model. According to the actual operation of the active distribution network, determine the basic assumptions and simplified conditions of the mathematical model; establish the corresponding mathematical model according to the structural characteristics of the active distribution network; including: describing the mathematical expressions of each component in the active distribution network, and the connection relationship between each device; define all necessary variables and parameters in the mathematical model, the variables and parameters include: voltage, current and power electrical quantities, as well as network structure parameters.

4. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The obtaining of information and data of real events in the active power distribution network specifically includes the following steps: Obtain information about real events in the active distribution network: collect test areas of real events, obtain geographical information, test objectives and test results of the areas, and determine the number of lines, power supply types and line wattage required for the test areas; Obtaining the operating data of the active distribution network: real-time collection of the operating data of the active distribution network, including voltage, current and power parameters; cleaning and normalizing the operating data, deleting erroneous data and duplicate data, and supplementing the missing data using the average method; using data analysis technology to process and analyze the collected data to identify the operating rules and potential problems of the active distribution network.

5. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The discrete event simulation method is used to map real events into a mathematical model for simulation, which specifically includes the following steps: The event scheduling method in discrete event simulation is used to map real events into mathematical models, define various actual events in the distribution network, and set a unique identifier for each event; An event table is established in the mathematical model to record all predefined events and their occurrence times. When the simulation starts, the event table is scanned by the time control module in the simulation system to determine the next event to occur, so that the simulation clock is advanced to the occurrence time of the event. When the next event is determined, the mathematical model calls the event processing module corresponding to the event to execute the corresponding event processing program; after the event processing is completed, the state of the simulation system is updated and the next event to occur is determined; the simulation steps are repeated until the simulation of the real event predetermined in the event table is completed.

6. The method for constructing an active power distribution network simulation calculation model according to claim 5, characterized in that: After each event is executed, the event is removed from the event table until all events in the event table are executed.

7. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The analysis of the information and data of the real event to obtain the actual constraint conditions specifically includes the following steps: The operating data of each device in the real event under normal conditions is obtained, and the operating parameter threshold of each device is obtained by extracting the average value method as the actual physical constraint condition.

8. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The combination of physical constraints, power electronics characteristics and control strategies specifically includes the following steps: Parameterize the mathematical model using physical constraints: Set and adjust the model parameters according to the actual physical characteristics of the active distribution network to ensure that the physical constraints of the model are consistent with the operating status of the actual distribution network; Parameterize the mathematical model using the characteristics of power electronics: obtain the voltage, current and power parameters of the active distribution network, build an electrical characteristic model that can characterize the electrical characteristics of the active distribution network under different conditions, and obtain the electrical parameter characteristics of the active distribution network through the electrical characteristic model; and adjust the parameters of the mathematical model based on the electrical parameter characteristics of the active distribution network to optimize the performance of the mathematical model; Parameterize the mathematical model using the control strategy: select the appropriate control strategy according to the control objective, and parameterize the control strategy based on the mathematical model, including: determining the initial value of the control parameter, the adjustment range, and the selection of the optimization algorithm.

9. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The method of using a genetic algorithm to globally optimize the parameters of the mathematical model specifically includes the following steps: Binary encode the mathematical model parameters, and then convert the optimized binary string back to the actual parameter value; An initial population is randomly generated, each individual represents a possible solution, and the population size and the number of generations of evolution are set according to the complexity of the problem and the computing resources; Calculate the fitness of each individual; Selection, crossover and mutation: select excellent individuals according to fitness for crossover to generate new individuals; Repeat the selection, crossover and mutation process until the preset number of iterations is reached or the predetermined stopping condition is met, and the individual finally obtained is the optimal solution; Simulation verification is carried out in the mathematical model to evaluate the impact of the optimized parameter configuration on the performance. The effectiveness of the genetic algorithm is verified by comparing the performance indicators before and after optimization.

10. The method for constructing an active power distribution network simulation calculation model according to claim 1, characterized in that: The forming of the active distribution network simulation calculation model with practical value specifically includes the following steps: Use different parameter combinations to conduct simulation experiments and record the simulation results under each combination; Compare simulation results under different parameter combinations and evaluate the performance of each combination in terms of computational efficiency and accuracy; Based on the simulation results, select the parameter combination with the best performance in terms of computational efficiency and accuracy; The selected optimal parameter combination is further verified to ensure its stability and reliability under various working conditions.

Images