System and method for simulating running state of power distribution network cooperating with source network load

Abstract

The invention relates to a source-network-load coordinated power distribution network operation state simulation system and method, relates to the technical field of power distribution networks, and realizes accurate deduction of the power distribution network operation state under the background of high-proportion new energy access by constructing a source-network-load coordinated power distribution network simulation model. Through a multi-source data preprocessing and parameter optimization identification mechanism, key operation parameters are effectively calibrated, an abnormal state is quickly positioned, and the accuracy of parameter identification and the efficiency of abnormality diagnosis are improved. In a fault or maintenance scene, multiple transfer schemes can be automatically generated and dynamic simulation can be performed based on real-time topology and load transfer capability, quantitative evaluation and sorting are performed from multiple dimensions of safety, reliability, economy and risk, and the operation safety, economy and new energy consumption capability of the power distribution network are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a power distribution network operation status simulation system and method based on coordinated source-grid-load. Background Technology

[0002] The power distribution network is undergoing a transformation from a traditional passive network to an active distribution network with a high proportion of distributed renewable energy and diversified load access. This transformation makes the operating characteristics of the distribution network increasingly complex. Traditional top-down power grid simulation analysis methods are no longer sufficient to accurately characterize the strong interactions and uncertainties among multiple elements such as sources, grid, and loads. In existing technologies, the simulation analysis of the operating status of the distribution network often focuses on a single dimension. For example, some methods only focus on the calculation and security verification of power flow on the grid side, while ignoring the randomness of distributed power output and the dynamic response characteristics of loads. Although some methods consider the access of sources and loads, they are usually treated as isolated and unchanging injection points, failing to conduct joint simulation and deduction as an organic whole from a collaborative perspective. This makes it difficult to accurately identify the source and load access in practical applications. While the system can identify key operating parameters under complex conditions, it also cannot effectively locate abnormal states caused by drastic fluctuations in source and load. When a line fails or needs maintenance, how to quickly and accurately simulate the power flow transfer path under the coordinated action of source and load, and assess its impact on adjacent lines, transformers, and other equipment, is a major challenge currently faced by operation and dispatch personnel. In addition, distribution network simulation analysis often initiates complex diagnostic processes for even minor power imbalances. In the context of high-proportion renewable energy integration and surging data volume, this leads to a huge waste of computing resources and processing delays, making it difficult to meet the high real-time requirements of operational decision-making. Therefore, how to achieve optimized identification of operating parameters and rapid anomaly location while ensuring effective resource utilization is an urgent problem to be solved. Summary of the Invention

[0003] Therefore, it is necessary to provide a simulation system and method for the operation status of a distribution network with coordinated source-grid-load that can achieve optimized identification of operating parameters and rapid anomaly location, in order to address the above-mentioned technical problems.

[0004] Firstly, a method for simulating the operating state of a distribution network with coordinated source-grid-load relationships is provided, the method comprising: The first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters. The first relevant parameters include at least the operating data of distributed generation, the grid topology, and the operating data of flexible loads. Based on the second relevant parameters, a distribution network simulation model based on source-grid-load coordination is constructed; Based on the power distribution network simulation model, and based on the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including the abnormal parameters. Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0005] Optionally, the first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters, including: Missing data in the first relevant parameters are filled in, and abnormal data points are identified and corrected to obtain the first target parameter, which also includes switch state data; Based on a preset simulation time scale, the first target parameter with different sampling frequencies is subjected to time unification processing to obtain the second target parameter; The switch status data is acquired, and the power grid topology is verified and generated based on the switch status data. The second target parameter is converted into a per-unit value and then into a third target parameter in a preset simulation input format; The second relevant parameter is generated based on the third target parameter and the power grid topology.

[0006] Optionally, based on the second relevant parameters, constructing a distribution network simulation model based on source-grid-load coordination includes: Based on the uncertainty of distributed power output, a first objective model is constructed. Based on the operating state parameters of the power grid topology, a second objective model is constructed; Based on the operational data of the flexible load, a third objective model is constructed; The first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination.

[0007] Optionally, based on the uncertainty of distributed power output, the first objective model includes: Obtain environmental parameters that affect the output of the distributed power source; Based on the environmental parameters and probability distribution, the first target model is determined, and the first target model includes:

[0008] in, This represents the output value of the distributed power source at time t. This represents the conditional probability density function. This represents the different environmental parameter values ​​at time t. Indicates the output of distributed power sources. This indicates that it follows a distribution.

[0009] Optionally, based on the operating state parameters of the power grid topology, constructing a second target model includes: Determine the nodes and links of the power grid topology, and determine the power grid topology model based on the nodes and links; Based on the power grid topology model, the second target model is determined, and the second target model includes:

[0010]

[0011]

[0012] in, , Let these represent the active power and reactive power input to node i, respectively. , Let these represent the active power and reactive power input to node j, respectively. , These represent the active and reactive power of the load at the end of link ij, respectively. , These represent the resistance and reactance of link ij, respectively. , These represent the voltage amplitudes at nodes i and j, respectively. Optionally, based on the operational data of the flexible load, constructing a third objective model includes: Obtain historical electricity consumption data to determine flexible loads; Based on the aforementioned flexible load, the third objective model is constructed, which includes:

[0013] in, This represents the actual power of the m-th flexible load at time t. This represents the basic power required by the m-th flexible load at time t. Indicates the power adjustment value. , These represent the minimum and maximum values ​​of the power adjustment, respectively. This indicates the total electricity consumption. Indicates the operating cycle.

[0014] Optionally, the first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination, including: Based on a coupling mechanism, the first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination. The coupling mechanism includes:

[0015] in, Indicates the deviation of the system from equilibrium. Indicates the number of nodes. Indicates the voltage phase angle difference. This represents the element in the i-th row and j-th column of the power network node admittance matrix. represents an imaginary number, Indicates electrical conductance. Indicates susceptance. and All of these represent weighting coefficients.

[0016] Optionally, based on the power distribution network simulation model and an optimized identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and the abnormal parameters are identified, including: Obtain the set of parameters to be calibrated from the second relevant parameters; Construct an optimization identification model, which includes an objective function and constraints. The constraints include at least coupling mechanism constraints, operational safety constraints, and parameter physical constraints. The objective function includes:

[0017] in, , These represent the measured values ​​of node voltage and link power flow, respectively. , These represent the calculated values ​​of node voltage and link power flow, respectively. The optimal parameter solution is obtained by solving the optimization identification model using a nonlinear programming algorithm. Based on the optimal parameter solution, the deviation of the system from equilibrium is determined; When the deviation of the system from equilibrium is less than or equal to a first preset threshold, the system is determined to be in equilibrium, and a third relevant parameter is generated based on the optimal parameter solution. When the deviation of the system equilibrium state is greater than a first preset threshold and less than a second preset threshold, it is determined that the system is in a slightly unbalanced state. The optimal parameter solution is compared with the historical parameters. Based on the comparison result, a preset number of parameters with the highest ranking are placed into the queue to be processed. When the deviation of the system equilibrium state is greater than a second preset threshold, it is determined that the system is in a severely unbalanced state. The optimal parameter solution is compared with the historical parameters. Based on the comparison result, a preset number of parameters that rank higher are defined as abnormal parameters. The higher the ranking, the greater the difference in the comparison result.

[0018] Optionally, based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are used for simulation deduction and comprehensive evaluation, including: Set up the initial faulty circuit to be inspected; The third relevant parameter is input into the power distribution network simulation model, and multiple candidate power transfer schemes for distribution lines are generated based on the output results and the power grid topology. For each of the candidate power transfer schemes, dynamic simulation is performed within the target simulation time window with a preset time step. During the simulation, the load rate of each link, the voltage of each node, and the deviation of the system equilibrium state are recorded to generate simulation results. Based on the simulation results, an evaluation index system is determined, which includes safety, reliability, economy, and risk indicators. Based on the aforementioned evaluation index system, all candidate power transfer schemes for distribution lines are quantitatively scored and ranked.

[0019] Secondly, a simulation system for the operation status of a distribution network with coordinated power generation, grid, and load is provided, the system comprising: The data acquisition module is used to acquire the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, and the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; The model building module is used to build a distribution network simulation model based on the second relevant parameters and the source-grid-load coordination. The calibration module is used to calibrate the second relevant parameters of the power distribution network based on the power distribution network simulation model and the optimization identification mechanism, to obtain the third relevant parameters, and to determine the abnormal parameters, wherein the third relevant parameters include the abnormal parameters. The simulation module is used to simulate the operation of the power distribution line based on the third relevant parameter and the abnormal parameter, and to perform simulation deduction and comprehensive evaluation on the simulation results. The power distribution line is connected to the source load.

[0020] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: The first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters. The first relevant parameters include at least the operating data of distributed generation, the grid topology, and the operating data of flexible loads. Based on the second relevant parameters, a distribution network simulation model based on source-grid-load coordination is constructed; Based on the power distribution network simulation model, and based on the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including the abnormal parameters. Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0021] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps: The first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters. The first relevant parameters include at least the operating data of distributed generation, the grid topology, and the operating data of flexible loads. Based on the second relevant parameters, a distribution network simulation model based on source-grid-load coordination is constructed; Based on the power distribution network simulation model, and based on the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including the abnormal parameters. Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0022] Fifthly, a computer program product is provided, the computer program product comprising a computer program, which, when executed by a processor, performs the following steps: The first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters. The first relevant parameters include at least the operating data of distributed generation, the grid topology, and the operating data of flexible loads. Based on the second relevant parameters, a distribution network simulation model based on source-grid-load coordination is constructed; Based on the power distribution network simulation model, and based on the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including the abnormal parameters. Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0023] The aforementioned simulation system and method for the operation status of a distribution network based on source-grid-load coordination includes the following steps: acquiring first relevant parameters of the distribution network; preprocessing the first relevant parameters to obtain second relevant parameters, wherein the first relevant parameters include at least the operation data of distributed generation sources, the grid topology, and the operation data of flexible loads; constructing a distribution network simulation model based on source-grid-load coordination based on the second relevant parameters; calibrating the second relevant parameters of the distribution network based on the distribution network simulation model and an optimization identification mechanism to obtain third relevant parameters and identify abnormal parameters, wherein the third relevant parameters include the abnormal parameters; simulating the operation process of distribution lines based on the third relevant parameters and the abnormal parameters, and performing simulation extrapolation and comprehensive evaluation on the simulation results, wherein the distribution lines are connected to the source and load. This invention, by constructing a source-grid-load coordinated distribution network simulation model, achieves accurate extrapolation of the operation status of the distribution network under the background of high-proportion renewable energy access. Through multi-source data preprocessing and parameter optimization identification mechanisms, it effectively calibrates key operating parameters and quickly locates abnormal states, improving the accuracy of parameter identification and the efficiency of abnormal diagnosis. In fault or maintenance scenarios, it can automatically generate multiple power transfer schemes and perform dynamic simulations based on real-time topology and load transfer capabilities. It can also perform quantitative evaluation and ranking from multiple dimensions such as safety, reliability, economy, and risk, effectively improving the safety, economy, and renewable energy absorption capacity of the distribution network. Attached Figure Description

[0024] Figure 1 This is an application environment diagram of the distribution network operation state simulation method of coordinated source-grid-load in one embodiment; Figure 2 This is a flowchart illustrating a method for simulating the operation status of a distribution network with coordinated source, grid, and load in one embodiment. Figure 3 This is a block diagram of a distribution network operation state simulation system with coordinated source-grid-load in one embodiment. Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0026] It should be understood that, in the description of this invention, unless the context explicitly requires it, the words "comprising," "including," and similar terms throughout the specification should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to."

[0027] It should also be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] It should be noted that the terms "S1," "S2," etc., are used only for descriptive purposes and do not specifically refer to the order or sequence, nor are they intended to limit the present invention. They are merely for the convenience of describing the method of the present invention and should not be construed as indicating the sequential order of the steps. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0029] The distribution network operation state simulation method based on coordinated source-grid-load provided by this invention can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with a data processing platform set on server 104 via a network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0030] In one embodiment, such as Figure 2 As shown, a method for simulating the operating state of a distribution network with coordinated source-grid-load characteristics is provided, and this method is applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps: S1: Obtain the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, wherein the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads.

[0031] It should be noted that distributed power generation operation data mainly includes real-time and predicted output (active / reactive power), voltage frequency, start-stop status, inverter parameters, etc. of equipment such as photovoltaic and wind turbines, which are used to reflect the volatility and uncertainty of new energy power generation; grid topology refers to the physical connection relationship of the distribution network, including the connection method of equipment such as lines, switches, transformers, and buses, as well as the network structure (radial, ring, etc.); flexible load operation data includes real-time power, interruptible capacity, adjustment potential, response time, and electricity consumption preferences of adjustable loads (such as air conditioners, charging piles, and flexible industrial equipment), which are used to characterize the flexible adjustment capability of the load side.

[0032] S2: Based on the second relevant parameters, construct a distribution network simulation model based on source-grid-load coordination.

[0033] S3: Based on the power distribution network simulation model and the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are determined, including the abnormal parameters.

[0034] S4: Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0035] It should be noted that power distribution lines are the intermediate link connecting power sources and loads in the power distribution network. They are responsible for transmitting the electrical energy generated by distributed power sources to various flexible loads, and at the same time, they must also bear the bidirectional power flow caused by load fluctuations and changes in power output. They are the key physical carrier for realizing source-load coordinated interaction and energy transmission.

[0036] In some specific implementations, obtaining the first relevant parameters of the distribution network and preprocessing the first relevant parameters to obtain the second relevant parameters includes: The missing data in the first relevant parameters are filled in, and abnormal data points are identified and corrected to obtain the first target parameter. The first target parameter also includes switch status data, which refers to the set of real-time opening and closing status of equipment such as circuit breakers, disconnectors, and tie switches in the distribution network. It is usually represented by 0 / 1 or open / closed. This data is used to describe the current on and off path and to dynamically identify the real-time topology of the power grid and generate load transfer schemes. Based on a preset simulation time scale, the first target parameter with different sampling frequencies is subjected to time unification processing to obtain the second target parameter. The simulation time scale refers to the time range and step size used in the simulation process. It defines the simulation time span (such as the next 15 minutes or 24 hours) and the calculation time interval (such as once per second or every 5 minutes). The corresponding time scale can be selected according to the preset scenario (real-time control, short-term planning, etc.) to balance the calculation efficiency and simulation accuracy. The switch status data is acquired, and based on the switch status data, the power grid topology is verified and generated. That is, by collecting the real-time opening / closing signals of circuit breakers and disconnecting switches in systems such as SCADA, the switch status of the entire network is obtained. Based on these statuses, graph theory or network topology analysis algorithms are used to dynamically verify the connection relationship of electrical equipment, automatically identify the current power supply area, islanded network and energized status, thereby generating a power grid topology consistent with the real-time operation mode. The second target parameter is converted into a per-unit value and then into a third target parameter in a preset simulation input format. The per-unit value is a dimensionless relative value that divides a nominal value (such as voltage or power) by a selected reference value. This allows equipment parameters of different voltage levels and capacities to be calculated and compared under the same reference, thereby simplifying the calculation process and improving numerical stability. The above conversion method is a commonly used method, and the specific process will not be described in detail here. Based on the third target parameter and the power grid topology, the second related parameter is generated, that is, the second related parameter includes the third target parameter and the power grid topology data, etc.

[0037] In some specific implementations, constructing a distribution network simulation model based on source-grid-load coordination, based on the second relevant parameters, includes: Based on the uncertainty of distributed power output, a first objective model is constructed. Based on the operating state parameters of the power grid topology, a second objective model is constructed; Based on the operational data of the flexible load, a third objective model is constructed; The first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination.

[0038] In some specific implementations, based on the uncertainty of distributed power output, the first objective model is constructed as follows: Obtain environmental parameters that affect the output of the distributed power source, such as temperature and light intensity; Based on the environmental parameters and probability distribution, the first target model is determined, and the first target model includes:

[0039] in, This represents the output value of the distributed power source at time t. This represents the conditional probability density function. This represents the different environmental parameter values ​​at time t. Indicates the output of distributed power sources. Indicating conformity to distribution, distributed power output refers to the active and reactive power actually generated by distributed energy sources (such as photovoltaics, wind turbines, energy storage, etc.) within a specific time period.

[0040] In some specific implementations, constructing a second target model based on the power grid topology operating state parameters includes: The nodes and links of the power grid topology are determined, and a power grid topology model is determined based on the nodes and links. Nodes refer to electrical connection locations such as busbars and transformer connection points in the power grid, which are key points for voltage and power calculation and monitoring. Links refer to power transmission channels such as transmission lines and switches that connect nodes. Based on the power grid topology model, the second target model is determined, and the second target model includes:

[0041]

[0042]

[0043] in, , Let these represent the active power and reactive power input to node i, respectively. , Let these represent the active power and reactive power input to node j, respectively. , These represent the active and reactive power of the load at the end of link ij, respectively. , These represent the resistance and reactance of link ij, respectively. , These represent the voltage amplitudes at nodes i and j, respectively. In some specific implementations, constructing a third target model based on the operational data of the flexible load includes: To obtain historical electricity consumption data and identify flexible loads, historical electricity consumption data refers to detailed electricity consumption records of users or equipment over a period of time, including power curves, electricity consumption periods, peak and valley values, etc. Based on this data, by analyzing their electricity consumption patterns and adjustability potential (such as interruptible and transferable load characteristics), we can identify and determine which loads are flexible loads that can participate in grid interaction. Based on the aforementioned flexible load, the third objective model is constructed, which includes:

[0044] in, This represents the actual power of the m-th flexible load at time t. This represents the basic power required by the m-th flexible load at time t. Indicates the power adjustment value. , These represent the minimum and maximum values ​​of the power adjustment, respectively. This indicates the total electricity consumption. Indicates the operating cycle.

[0045] In some specific implementations, the first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination, including: Based on a coupling mechanism, the first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination. The coupling mechanism includes:

[0046] in, Indicates the deviation of the system from equilibrium. Indicates the number of nodes. Indicates the voltage phase angle difference. This represents the element in the i-th row and j-th column of the power network node admittance matrix. represents an imaginary number, Indicates electrical conductance. Indicates susceptance. and All represent weighting coefficients, where the smaller the deviation of the system from equilibrium, the more stable the system is.

[0047] In some specific implementations, based on the power distribution network simulation model and an optimized identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including: Obtain the set of parameters to be calibrated from the second relevant parameters. The set of parameters to be calibrated refers to the key parameters in the distribution network simulation model whose actual values ​​deviate from theoretical values ​​due to measurement errors, model inaccuracies, or equipment aging. These parameters typically include line resistance / reactance, transformer turns ratio, and distributed power source control parameters. They need to be calibrated by optimizing the identification algorithm to improve the accuracy and reliability of the simulation model. An optimization identification model is constructed, comprising an objective function and constraints. The constraints include at least coupling mechanism constraints, operational safety constraints, and physical parameter constraints. Coupling mechanism constraints refer to the physical coupling relationships between the source, grid, and load, such as the aforementioned system equilibrium deviation equation. Operational safety constraints refer to the technical limits that various electrical quantities must meet to ensure the safe and stable operation of the power grid, mainly including the upper and lower limits of node voltage and the thermal stability limits of line and transformer power transmission, which can be set according to actual needs. Physical parameter constraints refer to the physical meaning and range that the parameters to be identified in the model must follow, such as the line resistance must be positive and the transformer ratio must vary within a certain range. The objective function includes:

[0048] in, , These represent the measured values ​​of node voltage and link power flow, respectively. , These represent the calculated values ​​of node voltage and link power flow, respectively. The optimal parameter solution is obtained by solving the optimization identification model based on the nonlinear programming algorithm. The solution method is a common method, and the specific solution process will not be described in detail here. The optimal parameter solution refers to a set of model parameter values ​​that minimize the objective function (such as the overall error between the simulation value and the measured value) obtained by solving the optimization identification model, such as resistance, voltage, and flexible load. Based on the optimal parameter solution, the deviation of the system equilibrium state is determined, that is, the optimal parameter solution is input into the above-mentioned distribution network simulation model to obtain the deviation of the system equilibrium state. When the deviation of the system equilibrium state is less than or equal to a first preset threshold, it is determined that the system is in equilibrium. Based on the optimal parameter solution, a third relevant parameter is generated, that is, skipping in-depth anomaly localization analysis and directly outputting parameter calibration results. The first preset threshold can be set according to actual needs. When the deviation of the system equilibrium state is greater than a first preset threshold and less than a second preset threshold, it is determined that the system is in a slightly unbalanced state. The optimal parameter solution is compared with historical parameters. According to the comparison result, a preset number of parameters with the highest ranking are placed in the processing queue, that is, preliminary anomaly screening and recording are performed. For example, the optimal parameter solution is compared with typical values ​​or historical ranges, and the parameters with the largest deviations are marked as to be observed, but advanced diagnosis is not triggered. The second preset threshold can be set according to actual needs. When the deviation of the system from equilibrium exceeds a second preset threshold, the system is determined to be in a severely unbalanced state. The optimal parameter solution is compared with historical parameters. Based on the comparison results, a preset number of parameters with the highest ranking are defined as anomalous parameters, triggering a comprehensive anomaly localization and fault diagnosis procedure. For example, the sensitivity of the objective function to each parameter is analyzed. Parameters with high sensitivity and whose optimized values ​​deviate too much from their typical values ​​are judged as anomalous parameters. The higher the ranking, the greater the difference in the comparison results. The preset number can be set according to actual needs.

[0049] In the above implementation, by setting the system equilibrium deviation degree and its hierarchical processing mechanism, the degree of system imbalance can be effectively distinguished. For slight, self-recoverable imbalances, the system chooses to ignore or simply record them, thereby significantly saving computing resources, reducing system load, and improving the overall efficiency of simulation and analysis. Only when the degree of imbalance exceeds the safety threshold is the calculation of a computationally intensive, precise fault location and recovery strategy initiated, realizing intelligent on-demand allocation of computing resources.

[0050] In some specific implementations, the operation of the power distribution line is simulated based on the third relevant parameter and the abnormal parameter, and the simulation results are used for simulation deduction and comprehensive evaluation, including: Set an initial fault line to be inspected, such as simulating line L-16 tripping due to a fault; The third relevant parameter is input into the power distribution network simulation model. Based on the output results and the power grid topology, multiple candidate power transfer schemes for distribution lines are generated. The output results are used to describe the load transfer capacity. For the identified abnormal parameters, they are set according to their true values. Based on the network topology, multiple feasible load transfer paths (schemes) are automatically generated. For example, scheme A: power supply to the power outage area by closing tie switch SW-12, and scheme B: power supply by closing tie switch SW-34. For each candidate power transfer scheme, dynamic simulation is performed within the target simulation time window with a preset time step. During the simulation, the load rate of each link, the voltage of each node, and the deviation of the system equilibrium state are recorded to generate simulation results. The target simulation time window and the preset time step can be set according to actual needs, such as 3 seconds within 2 minutes. In the simulation model, the operation instructions of each candidate power transfer scheme are executed sequentially, such as disconnecting the faulty line and closing the tie switch. The simulation is advanced with a short time step (such as seconds or minutes) to dynamically calculate the system state. During the simulation, the fluctuation of the source and load is taken into account. That is, the first target model is called to generate the fluctuating output of the distributed power source based on the predicted environmental data within the simulation time. The third target model is called to simulate the natural changes of the load and the response of the flexible load. During the entire simulation time window, the line / transformer load rate, node voltage level, and system equilibrium state deviation of the entire network are recorded and monitored in real time. Based on the simulation results, an evaluation index system is determined. The evaluation index system includes safety, reliability, economy, and risk indicators. That is, after the simulation, a quantitative comprehensive evaluation score is calculated for each scheme. Safety indicators include whether there is any equipment overload (>100%) or voltage exceeding the limit, etc. Reliability indicators include whether all power loss loads have been successfully restored, etc. Economy indicators include the number of switching operations required to execute the scheme, etc. Risk indicators include the robustness of the scheme and the pressure on abnormal equipment, etc. For example, in the simulation, the proportion of time when the system equilibrium deviation exceeds the threshold, this indicator directly measures the stability of the scheme under source load fluctuations. Based on the aforementioned evaluation index system, all candidate power transfer schemes for distribution lines are quantitatively scored and ranked. That is, by using methods such as weighted summation, the above indicators are combined into a comprehensive score, and all candidate schemes are ranked. This is a commonly used method, and the specific process will not be elaborated here. Among them, the higher the comprehensive score, the better the scheme.

[0051] The above-mentioned method for simulating the operation status of a distribution network based on source-grid-load coordination includes: acquiring first relevant parameters of the distribution network; preprocessing the first relevant parameters to obtain second relevant parameters, wherein the first relevant parameters include at least the operation data of distributed generation, the grid topology, and the operation data of flexible loads; constructing a distribution network simulation model based on source-grid-load coordination based on the second relevant parameters; calibrating the second relevant parameters of the distribution network based on the distribution network simulation model and an optimization identification mechanism to obtain third relevant parameters and determine abnormal parameters, wherein the third relevant parameters include the abnormal parameters; simulating the operation process of the distribution lines based on the third relevant parameters and the abnormal parameters, and performing simulation extrapolation and comprehensive evaluation on the simulation results, wherein the distribution lines are connected to the source and load. This invention, by constructing a distribution network simulation model based on source-grid-load coordination, achieves accurate extrapolation of the operation status of the distribution network under the background of high proportion of new energy access. Through multi-source data preprocessing and parameter optimization identification mechanism, it effectively calibrates key operation parameters and quickly locates abnormal states, improving the accuracy of parameter identification and the efficiency of abnormal diagnosis. In fault or maintenance scenarios, it can automatically generate multiple power transfer schemes and perform dynamic simulations based on real-time topology and load transfer capabilities. It can also perform quantitative evaluation and ranking from multiple dimensions such as safety, reliability, economy, and risk, effectively improving the safety, economy, and renewable energy absorption capacity of the distribution network.

[0052] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0053] In one embodiment, such as Figure 3 As shown, a simulation system for the operation status of a distribution network with coordinated source-grid-load is provided, including: a data acquisition module, a model building module, a calibration module, and a simulation module, wherein: The data acquisition module is used to acquire the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, and the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; The model building module is used to build a distribution network simulation model based on the second relevant parameters and the source-grid-load coordination. The calibration module is used to calibrate the second relevant parameters of the power distribution network based on the power distribution network simulation model and the optimization identification mechanism, to obtain the third relevant parameters, and to determine the abnormal parameters, wherein the third relevant parameters include the abnormal parameters. The simulation module is used to simulate the operation of the power distribution line based on the third relevant parameter and the abnormal parameter, and to perform simulation deduction and comprehensive evaluation on the simulation results. The power distribution line is connected to the source load.

[0054] Specific limitations regarding the simulation system for the operation status of a distribution network with coordinated power generation, grid, and load can be found in the limitations of the simulation method for the operation status of a distribution network with coordinated power generation, grid, and load described above, and will not be repeated here. Each module in the aforementioned simulation system for the operation status of a distribution network with coordinated power generation, grid, and load can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0055] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for simulating the operating state of a distribution network in a coordinated source-grid-load configuration. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0056] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0057] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: S1: Obtain the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, wherein the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; S2: Based on the second relevant parameters, construct a distribution network simulation model based on source-grid-load coordination; S3: Based on the power distribution network simulation model and the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are determined. The third relevant parameters include the abnormal parameters. S4: Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0058] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: S1: Obtain the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, wherein the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; S2: Based on the second relevant parameters, construct a distribution network simulation model based on source-grid-load coordination; S3: Based on the power distribution network simulation model and the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are determined. The third relevant parameters include the abnormal parameters. S4: Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0059] In one embodiment, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, performs the following steps: S1: Obtain the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, wherein the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; S2: Based on the second relevant parameters, construct a distribution network simulation model based on source-grid-load coordination; S3: Based on the power distribution network simulation model and the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are determined. The third relevant parameters include the abnormal parameters. S4: Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0061] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for simulating the operating state of a distribution network with coordinated source-grid-load systems, characterized in that, The method includes: The first relevant parameters of the distribution network are obtained, and the first relevant parameters are preprocessed to obtain the second relevant parameters. The first relevant parameters include at least the operating data of distributed generation, the grid topology, and the operating data of flexible loads. Based on the second relevant parameters, a distribution network simulation model based on source-grid-load coordination is constructed; Based on the power distribution network simulation model, and based on the optimization identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and abnormal parameters are identified, including the abnormal parameters. Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are simulated and comprehensively evaluated. The power distribution line is connected to the source load.

2. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 1, characterized in that, Obtain the first relevant parameters of the distribution network, and preprocess the first relevant parameters to obtain the second relevant parameters, including: Missing data in the first relevant parameters are filled in, and abnormal data points are identified and corrected to obtain the first target parameter, which also includes switch state data; Based on a preset simulation time scale, the first target parameter with different sampling frequencies is subjected to time unification processing to obtain the second target parameter; The switch status data is acquired, and the power grid topology is verified and generated based on the switch status data. The second target parameter is converted into a per-unit value and then into a third target parameter in a preset simulation input format; The second relevant parameter is generated based on the third target parameter and the power grid topology.

3. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 2, characterized in that, Based on the second relevant parameter, the simulation model of the distribution network based on source-grid-load coordination is constructed as follows: Based on the uncertainty of distributed power output, a first objective model is constructed. Based on the operating state parameters of the power grid topology, a second objective model is constructed; Based on the operational data of the flexible load, a third objective model is constructed; The first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination.

4. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 3, characterized in that, Based on the uncertainty of distributed power generation output, the first objective model is constructed as follows: Obtain environmental parameters that affect the output of the distributed power source; Based on the environmental parameters and probability distribution, the first target model is determined, and the first target model includes: in, This represents the output value of the distributed power source at time t. This represents the conditional probability density function. This represents the different environmental parameter values ​​at time t. Indicates the output of distributed power sources. This indicates that it follows a distribution.

5. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 4, characterized in that, Based on the operating state parameters of the power grid topology, the second objective model is constructed as follows: Determine the nodes and links of the power grid topology, and determine the power grid topology model based on the nodes and links; Based on the power grid topology model, the second target model is determined, and the second target model includes: in, , Let these represent the active power and reactive power input to node i, respectively. , Let these represent the active power and reactive power input to node j, respectively. , These represent the active and reactive power of the load at the end of link ij, respectively. , These represent the resistance and reactance of link ij, respectively. , These represent the voltage amplitudes at nodes i and j, respectively.

6. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 5, characterized in that, Based on the operational data of the flexible load, the third objective model is constructed as follows: Obtain historical electricity consumption data to determine flexible loads; Based on the aforementioned flexible load, the third objective model is constructed, which includes: in, This represents the actual power of the m-th flexible load at time t. This represents the basic power required by the m-th flexible load at time t. Indicates the power adjustment value. , These represent the minimum and maximum values ​​of the power adjustment, respectively. This indicates the total electricity consumption. Indicates the operating cycle.

7. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 6, characterized in that, The first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination, which includes: Based on a coupling mechanism, the first target model, the second target model, and the third target model are coupled to obtain the distribution network simulation model based on source-grid-load coordination. The coupling mechanism includes: in, Indicates the deviation of the system from equilibrium. Indicates the number of nodes. Indicates the voltage phase angle difference. This represents the element in the i-th row and j-th column of the power network node admittance matrix. represents an imaginary number, Indicates electrical conductance. Indicates susceptance. and All of these represent weighting coefficients.

8. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 7, characterized in that, Based on the aforementioned power distribution network simulation model and an optimized identification mechanism, the second relevant parameters of the power distribution network are calibrated to obtain the third relevant parameters, and the abnormal parameters are identified as follows: Obtain the set of parameters to be calibrated from the second relevant parameters; Construct an optimization identification model, which includes an objective function and constraints. The constraints include at least coupling mechanism constraints, operational safety constraints, and parameter physical constraints. The objective function includes: in, , These represent the measured values ​​of node voltage and link power flow, respectively. , These represent the calculated values ​​of node voltage and link power flow, respectively. The optimal parameter solution is obtained by solving the optimization identification model using a nonlinear programming algorithm. Based on the optimal parameter solution, the deviation of the system from equilibrium is determined; When the deviation of the system from equilibrium is less than or equal to a first preset threshold, the system is determined to be in equilibrium, and a third relevant parameter is generated based on the optimal parameter solution. When the deviation of the system equilibrium state is greater than a first preset threshold and less than a second preset threshold, it is determined that the system is in a slightly unbalanced state. The optimal parameter solution is compared with the historical parameters. Based on the comparison result, a preset number of parameters with the highest ranking are placed into the queue to be processed. When the deviation of the system equilibrium state is greater than a second preset threshold, it is determined that the system is in a severely unbalanced state. The optimal parameter solution is compared with the historical parameters. Based on the comparison result, a preset number of parameters that rank higher are defined as abnormal parameters. The higher the ranking, the greater the difference in the comparison result.

9. The method for simulating the operation status of a distribution network with coordinated source-grid-load as described in claim 8, characterized in that, Based on the third relevant parameter and the abnormal parameter, the operation process of the power distribution line is simulated, and the simulation results are used for simulation deduction and comprehensive evaluation, including: Set up the initial faulty circuit to be inspected; The third relevant parameter is input into the power distribution network simulation model, and multiple candidate power transfer schemes for distribution lines are generated based on the output results and the power grid topology. For each of the candidate power transfer schemes, dynamic simulation is performed within the target simulation time window with a preset time step. During the simulation, the load rate of each link, the voltage of each node, and the deviation of the system equilibrium state are recorded to generate simulation results. Based on the simulation results, an evaluation index system is determined, which includes safety, reliability, economy, and risk indicators. Based on the aforementioned evaluation index system, all candidate power transfer schemes for distribution lines are quantitatively scored and ranked.

10. A simulation system for the operation status of a distribution network with coordinated source-grid-load, characterized in that, The system includes: The data acquisition module is used to acquire the first relevant parameters of the distribution network, preprocess the first relevant parameters to obtain the second relevant parameters, and the first relevant parameters include at least the operating data of distributed generation, the grid topology and the operating data of flexible loads; The model building module is used to build a distribution network simulation model based on the second relevant parameters and the source-grid-load coordination. The calibration module is used to calibrate the second relevant parameters of the power distribution network based on the power distribution network simulation model and the optimization identification mechanism, to obtain the third relevant parameters, and to determine the abnormal parameters, wherein the third relevant parameters include the abnormal parameters. The simulation module is used to simulate the operation of the power distribution line based on the third relevant parameter and the abnormal parameter, and to perform simulation deduction and comprehensive evaluation on the simulation results. The power distribution line is connected to the source load.

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