A planning and configuration method for multi-point layout of new energy synchronous reactor systems in regional power grids

Abstract

This invention relates to the field of photovoltaic power generation system operation and maintenance technology. The invention provides a planning and configuration method for a multi-point layout of new energy synchronous machine stack systems in a regional power grid, aiming to solve the problems of weak voltage support capacity and transient overvoltage in high-proportion new energy power systems. The invention includes acquiring basic grid data, new energy resource data, power system operation data, and synchronous machine parameters; training a neural network model using the acquired data to obtain a planning and configuration model; collecting real-time operation data of the new energy synchronous machine stack system; substituting this data into the model to obtain a preliminary planning and configuration; verifying the planning and configuration through simulation; and optimizing historical data and simulation feature data using a genetic algorithm until a planning and configuration scheme that meets the transient overvoltage configuration requirements is found. This invention improves grid stability, optimizes new energy utilization, and realizes intelligent planning and configuration.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation system operation and maintenance technology, and in particular to a planning and configuration method for a multi-point layout of new energy synchronous reactor systems in a regional power grid. Background Technology

[0002] The multi-point deployment of renewable energy synchronous generator (MGP) systems in regional power grids aims to enhance grid stability. By deploying MGPs at multiple nodes within the grid, effective regulation of power flow and minimization of grid losses are achieved. As the interface between renewable energy sources and the grid, MGPs provide electrical isolation between the renewable energy fields and the grid, preventing adverse disturbances from one side from propagating to the other. They also provide sufficient inertia and reactive power support, ensuring stable grid operation. This multi-point deployment maximizes the advantages of MGPs and meets the optimal site selection and capacity configuration requirements of regional power grid energy storage systems.

[0003] The planning and configuration steps for multi-point deployment of new energy synchronous generator (MGP) systems in regional power grids are as follows: Based on the load characteristics, distribution of new energy resources, and existing power grid structure of the regional power grid, system analysis and evaluation are conducted to determine potential deployment points for the new energy synchronous generators (MGPs). Advanced power system simulation technology is used to simulate the power grid operation under different deployment schemes and evaluate the effect of each scheme on improving power grid stability. Based on the simulation results, the optimal MGP deployment points and their capacity configurations are optimized to effectively absorb new energy while minimizing the impact on the power grid. Finally, an implementation plan and scheme are formulated, including equipment selection, installation and commissioning, grid connection testing, and subsequent operation and maintenance strategies, so that the entire system can be smoothly integrated into and improve the overall performance of the regional power grid.

[0004] In the planning and configuration of multi-point layout of new energy synchronous reactor systems in regional power grids, the following technical pain points exist: the voltage support capability of some high-proportion new energy power systems at the sending end is weak, and the transient overvoltage problem is serious. Transient overvoltage will cause voltage fluctuation and flicker problems in the power grid. To address this, the present invention provides a planning and configuration method for multi-point layout of new energy synchronous reactor systems in regional power grids. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a planning and configuration method and extraction method for multi-point layout of new energy synchronous reactor systems in regional power grids. This method solves the problems of weak voltage support capability and serious transient overvoltage issues in some high-proportion new energy power systems at the sending end, which can cause voltage fluctuations and flicker in the power grid.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0007] This invention provides a method for planning and configuring a multi-point layout of a new energy synchronous reactor system in a regional power grid, comprising:

[0008] Step S101: Obtain regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters;

[0009] Step S102: Based on the regional power grid basic data, the neural network model is trained using the regional power grid basic data, new energy resource data, power system operation data and new energy synchronous machine parameters to obtain the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model.

[0010] Step S103: Collect real-time operating data of the new energy synchronous machine stack system and substitute it into the planning and configuration model of the multi-point layout of the new energy synchronous machine stack system in the regional power grid to obtain the planning and configuration of the first new energy synchronous machine stack system.

[0011] Step S104: Substitute the planning configuration of the new energy synchronous machine stack system into the preset simulation model of the new energy synchronous machine stack system to obtain grid operation simulation data. Extract data features from the grid operation simulation data to obtain grid operation simulation feature data. Match the grid operation simulation feature data in the preset transient overvoltage scheme configuration knowledge base to obtain transient overvoltage scheme configuration matching results. The transient overvoltage matching scheme results include successful configuration of the transient overvoltage matching scheme and unsuccessful configuration of the transient overvoltage matching scheme.

[0012] Step S105: If the transient overvoltage matching scheme fails, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data are used to optimize the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system using a genetic algorithm. The optimized regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to process the real-time new energy synchronous machine stack system operation data, and output the second new energy synchronous machine stack system planning and configuration. The second new energy synchronous machine stack system planning and configuration is used as the planning and configuration of the regional power grid multi-point layout new energy synchronous machine stack system.

[0013] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S101 includes:

[0014] The basic data of the regional power grid includes the topology of the regional power grid, transmission line parameters, substation locations, substation capacity, and substation load distribution information;

[0015] New energy resource data includes wind energy and solar energy within the region;

[0016] Acquiring power system operation data includes real-time load data of the power grid, real-time power supply and demand curves of the power grid, real-time power grid frequency of the power grid, and real-time voltage stability indicators of the power grid.

[0017] Obtaining parameters for new energy synchronous machines includes technical parameters of new energy wind turbines, technical parameters of inverters and synchronous machines in new energy solar power systems, rated power, rated voltage, moment of inertia, control strategy parameters, and protection settings of the new energy synchronous machine.

[0018] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for new energy synchronous reactor systems, step S102 includes:

[0019] Based on regional power grid basic data, data features are preprocessed using regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters. The preprocessed data and extracted data features are used to train a neural network model. The model parameters are adjusted by gradient descent to minimize the model prediction error, thus obtaining a regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model.

[0020] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for new energy synchronous reactor systems, step S103 includes:

[0021] By using sensors, monitoring equipment, or remote communication interfaces installed on the new energy synchronous machine stack system, real-time operating data of the new energy synchronous machine stack system is collected. The collected real-time operating data is preprocessed and loaded into the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model. The regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to calculate the input real-time operating data to obtain the planning and configuration scheme of the new energy synchronous machine stack system. The planning and configuration scheme of the new energy synchronous machine stack system includes the deployment point of the synchronous machine, capacity configuration, and control strategy.

[0022] The planning and configuration of the new energy synchronous machine stack system is decoded and transformed into planning and configuration information, which includes the geographical location of the deployment point, the capacity and model of the synchronous machine, and the setting of control parameters, and output as a report.

[0023] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S104 includes:

[0024] Determine the simulation objectives and scope, select simulation tools, and build a synchronous machine model in the simulation tool based on the simulation objectives, scope, and tools, and according to the actual configuration and parameters of the new energy synchronous machine stack system.

[0025] Set the initial and boundary conditions of the model, and configure different simulation scenarios based on the operating characteristics of the new energy synchronous machine stack system and the actual situation of the power grid. The different simulation scenarios include normal operation scenario, fault scenario and extreme weather scenario.

[0026] Run the simulation model, collect power grid operation simulation data, start the simulation tool, run the simulation model according to the set parameters and scenario, monitor the operation status of the model during the simulation, and collect the power grid operation data generated during the simulation, including voltage, current, power and frequency.

[0027] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S105 includes:

[0028] The simulation characteristic data of power grid operation is compared with the historical data of the regional power grid multi-point layout new energy synchronous machine stack system to verify the accuracy of the simulation model of the new energy synchronous machine stack system. If there is a large deviation between the simulation characteristic data of power grid operation and the historical data of the regional power grid multi-point layout new energy synchronous machine stack system, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system will be optimized.

[0029] Furthermore, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S105 includes:

[0030] Integrate historical data and grid operation simulation characteristic data of multi-point layout of new energy synchronous machine stack systems in regional power grids, and sort out the historical data of multi-point layout of new energy synchronous machine stack systems in regional power grids, including the operation records, fault records and maintenance records of new energy synchronous machine stack systems. Extract grid operation simulation characteristic data, including voltage fluctuation, current change and power factor index.

[0031] The optimization objectives are received, including minimizing voltage fluctuations, maximizing the utilization rate of new energy sources, and reducing system losses.

[0032] Receive the framework parameters of the genetic algorithm, determine the population size of the genetic algorithm based on the framework parameters, i.e. the number of solutions participating in the optimization at the same time, design selection, crossover, and mutation genetic operators, define the fitness function to evaluate the quality of each solution, and the solution is the planned configuration scheme.

[0033] Generate an initial solution set, i.e., the population of the genetic algorithm. Based on the parameter range and constraints of the planning and configuration model of the multi-point layout of new energy synchronous machine stack system in the regional power grid, randomly generate a set of initial solutions. Each solution represents a planning and configuration scheme of a new energy synchronous machine stack system.

[0034] The fitness value of each solution in the population is calculated. Each solution is then substituted into the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system. Based on the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data, the fitness value corresponding to each solution is calculated.

[0035] A new set of solutions is generated through selection, crossover, and mutation until the preset number of iterations is reached or the optimization objective is met. After optimization, the solution with the highest fitness value is selected from the population as the optimal solution, which is the optimized new energy synchronous machine stack system planning and configuration scheme.

[0036] The beneficial effects of this invention;

[0037] This invention, through a multi-point deployment of renewable energy synchronous generator (NEG) systems, effectively regulates power flow, reduces grid losses, and enhances the grid's voltage support capacity. It is particularly effective in addressing transient overvoltage issues in some renewable energy power systems at the sending end. This helps reduce voltage fluctuations and flicker in the grid, improving overall grid stability. Through intelligent planning and configuration methods, this invention can rationally allocate the deployment points, capacity configurations, and control strategies of NEG synchronous generators, thereby maximizing the utilization of renewable energy, improving the efficiency and economy of renewable energy power generation, and promoting the development of green energy.

[0038] This invention employs advanced technologies such as neural network models and genetic algorithms to achieve intelligent and automated planning and configuration of new energy synchronous machine (NEM) reactor systems. This not only improves the efficiency and accuracy of planning and configuration but also reduces the cost and risk of human intervention. By collecting and analyzing a large amount of basic power grid data, new energy resource data, power system operation data, and NEM parameters, this invention provides solid data support for planning and configuration. This makes the decision-making process more scientific and objective, and helps improve the feasibility and practicality of planning and configuration schemes.

[0039] This invention utilizes a pre-defined simulation model of a new energy synchronous reactor system for simulation verification, enabling a more accurate evaluation of the effectiveness of the planned configuration scheme. Furthermore, by incorporating a transient overvoltage scheme configuration knowledge base for matching, the accuracy of the simulation verification is further improved. The planning and configuration method of this invention possesses high flexibility and scalability. As the power grid structure, the distribution of new energy resources, and the operating conditions of the power system change, the method of this invention can quickly adapt and make corresponding optimization adjustments.

[0040] In summary, this invention, through an intelligent planning and configuration method, effectively improves the stability of regional power grids, optimizes the utilization of new energy sources, realizes intelligent and data-driven decision-making processes, improves the accuracy of simulation verification, and possesses good flexibility and scalability. It provides a comprehensive and effective solution for the planning and configuration of multi-point deployment of new energy synchronous reactor systems in regional power grids. Attached Figure Description

[0041] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the planning and configuration method for a multi-point layout of a new energy synchronous reactor system in a regional power grid, provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. 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. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0044] To better understand the purpose of this invention, the invention will now be described in further detail.

[0045] This invention provides a method for planning and configuring a multi-point layout of a new energy synchronous reactor system in a regional power grid, comprising:

[0046] Step S101: Obtain regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters;

[0047] In step S101, obtaining regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters is the first and crucial step in planning and configuring a new energy synchronous machine stack system.

[0048] The topology of a regional power grid describes the substations, transmission lines, and their interconnections within the grid.

[0049] Transmission line parameters include electrical parameters such as resistance, reactance, and susceptance.

[0050] The substation location record records the geographical location of each substation, which helps to determine the appropriate deployment location for new energy synchronous machines.

[0051] Substation capacity reflects a substation's ability to process electrical energy.

[0052] Substation load distribution information shows the load situation in different areas of the power grid, which helps to assess the impact of new energy synchronous machines on the power grid load.

[0053] The regional wind energy record records the distribution and intensity of wind energy resources within the region, serving as an important basis for planning wind power generation.

[0054] The regional solar energy data provides information such as the radiation intensity and sunshine duration of solar energy resources within the region.

[0055] Real-time load data of the power grid is used to reflect the current load level of the power grid.

[0056] The real-time power supply and demand curves of the power grid demonstrate the dynamic balance between power supply and demand in the grid, which helps to understand the operating status of the power grid.

[0057] The real-time grid frequency is one of the important indicators for assessing grid stability.

[0058] Real-time voltage stability indicators of the power grid provide a quantitative assessment of the grid's voltage stability, which is of great significance for identifying potential problems in the power grid.

[0059] The parameters of the new energy synchronous machine include:

[0060] Technical parameters of new energy wind turbines include the rated power, speed range, and wind energy conversion efficiency of the wind turbine, which are parameters for evaluating wind power generation capabilities.

[0061] The technical parameters of the inverter and synchronous machine in the new energy solar power generation system describe the coordination method and working performance between the inverter and the synchronous machine.

[0062] The rated power, rated voltage, moment of inertia, control strategy parameters, and protection settings of the new energy synchronous machine: these parameters together determine the operating characteristics and protection mechanism of the synchronous machine.

[0063] Step S102: Based on the regional power grid basic data, the neural network model is trained using the regional power grid basic data, new energy resource data, power system operation data and new energy synchronous machine parameters to obtain the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model.

[0064] The neural network model is trained to preprocess the collected regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters. This includes data cleaning (removing outliers, handling missing values, etc.), data normalization or standardization, and other steps to improve the quality and consistency of the data.

[0065] Next, features are extracted from the preprocessed data. These features should comprehensively reflect the structure and operation of the power grid, as well as the characteristics of the new energy synchronous machines, such as the topological characteristics of the power grid, the parameter characteristics of transmission lines, the load characteristics of the power system, the distribution characteristics of new energy resources, and the technical parameter characteristics of the synchronous machines.

[0066] Choose a suitable neural network model for handling this type of planning and configuration problem, such as a multilayer perceptron (MLP), convolutional neural network (CNN), or recurrent neural network (RNN). Depending on the complexity of the problem and the characteristics of the data, it may be necessary to design a specific network structure, such as increasing the number of hidden layers or selecting an appropriate activation function.

[0067] The structure of the input layer, hidden layer, and output layer of the model is determined. The input layer should be able to receive preprocessed feature data; the hidden layer is used to extract deep features of the data; and the output layer outputs the planning and configuration scheme of the new energy synchronous machine stack system, such as the deployment point, capacity configuration, and control strategy of the synchronous machine.

[0068] The neural network model is trained using preprocessed data and its features as input. During training, the model learns the mapping relationship between input data and output configuration by continuously adjusting its internal parameters (weights and biases) to minimize prediction error.

[0069] Gradient descent or its variants (such as the Adam optimizer) are used to adjust the model parameters. In each iteration, the error between the planned configuration predicted by the model and the actual configuration is calculated, and then the model parameters are updated according to the error backpropagation algorithm.

[0070] Repeat the above training process until the model's performance on the validation set reaches the preset standard or the preset number of training iterations is reached. At this point, the model is considered to have learned an effective mapping relationship between the input data and the output configuration.

[0071] After model training, the model is validated using a test set to evaluate its generalization ability on unseen data. If the model performance meets the expected standards, it is saved as a planning and configuration model for a multi-point layout of new energy synchronous turbine reactor systems in the regional power grid. The saved model will be used in subsequent steps to process real-time operating data of the new energy synchronous turbine reactor system to output a reasonable planning and configuration scheme.

[0072] Step S103: Collect real-time operating data of the new energy synchronous machine stack system and substitute it into the planning and configuration model of the multi-point layout of the new energy synchronous machine stack system in the regional power grid to obtain the planning and configuration of the first new energy synchronous machine stack system.

[0073] Real-time operational data of the new energy synchronous machine (NEM) reactor system is collected through sensors, monitoring equipment, or remote communication interfaces installed on the system. This data should include the operating status of the NEM (such as power output, voltage, and current), environmental parameters (such as wind speed and solar irradiance, which are particularly important for wind and solar power systems), and relevant grid operating data (such as grid frequency and voltage stability indicators).

[0074] The collected real-time operational data undergoes preprocessing to ensure data quality and consistency. Preprocessing steps may include data cleaning (noise removal, outlier handling, etc.), data normalization or standardization, etc., so that the model can better process the data.

[0075] The preprocessed real-time operational data is loaded into the regional power grid multi-point layout new energy synchronous reactor system planning and configuration model. Based on the input data, the model will use the mapping relationship between input and output learned during training to calculate a preliminary new energy synchronous reactor system planning and configuration scheme.

[0076] The model outputs a system planning and configuration scheme for the first new energy synchronous reactor. This scheme typically includes information such as the deployment location of the synchronous reactor, capacity configuration, and control strategy. This information will provide the foundation for subsequent simulation verification and optimization steps.

[0077] The planning and configuration scheme output from the model is decoded and transformed into easily understandable and implementable planning and configuration information. This includes detailed information such as the geographical location of deployment points, the capacity and model of synchronizers, and the settings of control parameters. The planning and configuration information is then compiled into reports for easy access and use by decision-makers, engineers, and other relevant personnel.

[0078] Step S104: Substitute the planning configuration of the new energy synchronous machine stack system into the preset simulation model of the new energy synchronous machine stack system to obtain grid operation simulation data. Extract data features from the grid operation simulation data to obtain grid operation simulation feature data. Match the grid operation simulation feature data in the preset transient overvoltage scheme configuration knowledge base to obtain transient overvoltage scheme configuration matching results. The transient overvoltage matching scheme results include successful configuration of the transient overvoltage matching scheme and unsuccessful configuration of the transient overvoltage matching scheme.

[0079] The planned configuration of the new energy synchronous machine reactor system obtained in step S103 is substituted into the preset simulation model of the new energy synchronous machine reactor system. This simulation model should be able to accurately simulate the actual operation of the power grid, including the dynamic characteristics of the synchronous machine, the topology of the power grid, load changes, and other factors.

[0080] Start the simulation model and run it according to the preset simulation parameters and scenarios. During the simulation, the model will simulate the operation of the power grid under different conditions, including normal operation, fault conditions, and extreme weather conditions. Relevant data on power grid operation, such as voltage, current, power, and frequency, will be collected during the simulation. This data will be used to evaluate the impact of planned configurations on power grid operation.

[0081] Data feature extraction is performed on the collected power grid operation simulation data to obtain key feature data that can reflect the power grid's operating status. This feature data should be able to comprehensively and accurately reflect the power grid's operation under different operating conditions.

[0082] The extracted power grid operation simulation feature data is matched against a pre-defined transient overvoltage scheme configuration knowledge base. This knowledge base should contain various possible transient overvoltage scenarios and their corresponding solutions.

[0083] The matching process determines whether the current planned configuration can effectively address potential transient overvoltage issues. The matching result has two possibilities: the transient overvoltage matching scheme is successfully configured, or the transient overvoltage matching scheme is unsuccessful.

[0084] If the matching result is successful, it means that the current planning configuration can effectively solve the transient overvoltage problem, and we can move on to the next implementation stage.

[0085] If the matching result is unsuccessful, it indicates that the current planning configuration may not be able to completely solve the transient overvoltage problem, or there may be other potential risks. In this case, it is necessary to proceed to step S105 to optimize the planning configuration.

[0086] Through simulation verification and transient overvoltage scheme matching in step S104, we can conduct a preliminary assessment of the planned configuration of the new energy synchronous machine reactor system to ensure that it can effectively solve the transient overvoltage problem in the power grid. If the matching is unsuccessful, further optimization steps are needed to improve the planned configuration.

[0087] Step S105: If the transient overvoltage matching scheme fails, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data are used to optimize the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system using a genetic algorithm. The optimized regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to process the real-time new energy synchronous machine stack system operation data, and output the second new energy synchronous machine stack system planning and configuration. The second new energy synchronous machine stack system planning and configuration is used as the planning and configuration of the regional power grid multi-point layout new energy synchronous machine stack system.

[0088] Based on the matching results of step S104, it is confirmed that the current planned configuration of the new energy synchronous turbine reactor system cannot solve the transient overvoltage problem and needs to be optimized. Historical data on the multi-point layout of new energy synchronous turbine reactor systems in the regional power grid should be collected. This data should include past planning and configuration schemes, actual operating results, fault records, and corresponding handling measures. Historical data provides valuable experience and reference for the optimization process.

[0089] The collected historical data is integrated with the power grid operation simulation characteristic data obtained in step S104. Preprocessing steps such as data cleaning, normalization, or standardization are performed to ensure data quality and consistency.

[0090] Genetic algorithms are chosen as the optimization algorithm. Genetic algorithms are search algorithms based on the principles of biological evolution, possessing advantages such as strong global search capabilities and ease of parallelization, making them suitable for solving complex optimization problems. The optimization objective is set according to the actual needs. For example, minimizing the amplitude, duration, or frequency of transient overvoltages can be set as the optimization objective.

[0091] Using historical data and grid operation simulation data of multi-site deployment of new energy synchronous turbine reactor systems in regional power grids, combined with a genetic algorithm, the planning and configuration model of such systems is optimized. When constructing the optimization model, a fitness function needs to be defined to evaluate the merits of different planning and configuration schemes.

[0092] The optimization process of the genetic algorithm is executed. Through genetic operations such as selection, crossover, and mutation, new planning configuration schemes are continuously generated, and their fitness is evaluated. This process is repeated until a planning configuration scheme that satisfies the optimization objective is found or the preset number of iterations is reached.

[0093] The optimized regional power grid multi-point layout planning and configuration model for new energy synchronous reactor systems is used to process real-time operating data of the new energy synchronous reactor system, outputting a second planning and configuration for the new energy synchronous reactor system. This configuration scheme should perform better than the previous scheme in addressing transient overvoltage issues.

[0094] The second new energy synchronous turbine reactor system is planned and configured as the final planning and configuration scheme for the multi-point layout of new energy synchronous turbine reactor systems in the regional power grid. Based on this scheme, corresponding adjustments and optimizations are made to the new energy synchronous turbine reactor system.

[0095] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S101 includes:

[0096] The basic data of the regional power grid includes the topology of the regional power grid, transmission line parameters, substation locations, substation capacity, and substation load distribution information;

[0097] New energy resource data includes wind energy and solar energy within the region;

[0098] Acquiring power system operation data includes real-time load data of the power grid, real-time power supply and demand curves of the power grid, real-time power grid frequency of the power grid, and real-time voltage stability indicators of the power grid.

[0099] Obtaining parameters for new energy synchronous machines includes technical parameters of new energy wind turbines, technical parameters of inverters and synchronous machines in new energy solar power systems, rated power, rated voltage, moment of inertia, control strategy parameters, and protection settings of the new energy synchronous machine.

[0100] The topology of a regional power grid includes the connection relationships between various nodes in the grid (such as substations, power plants, etc.), as well as the route and length of transmission lines.

[0101] Transmission line parameters: These include electrical parameters such as resistance, reactance, and susceptance of the transmission line, as well as the line's rated voltage and rated current.

[0102] Substation location: Geographical location information of the substation, including longitude, latitude, or a specific address description.

[0103] Substation capacity: The transformer capacity of a substation indicates the maximum electrical load it can handle.

[0104] Substation load distribution information: The load area served by the substation and its load distribution, including the size and type of the load (such as industrial, commercial, residential, etc.) and the load variation pattern.

[0105] Regional wind energy: This includes the distribution of wind speed, wind energy density, and frequency distribution of wind direction. This data is usually obtained through weather stations or specialized wind energy assessment equipment.

[0106] Regional solar energy includes solar radiation intensity, sunshine duration, and annual and daily variations in solar radiation. This data is typically obtained through solar radiometers or weather stations.

[0107] Real-time load data of the power grid: This indicates the power load situation of the power grid at various times, and usually includes the total load, the proportion of various types of load, and the load change trend.

[0108] The real-time power supply and demand curve of the power grid represents the balance between power supply and demand in the power grid, including generator output, load consumption, and the charging and discharging status of energy storage devices.

[0109] Real-time grid frequency: This represents the frequency of alternating current in the power grid and is one of the important indicators for the stable operation of the power grid.

[0110] Real-time voltage stability indicators of the power grid: used to assess the stability of the power grid voltage, including the voltage fluctuation range, voltage deviation, and voltage recovery capability.

[0111] Technical parameters of new energy wind turbines include the rated power, rated voltage, speed range, starting wind speed, cut-out wind speed, rotor diameter, and generator type.

[0112] Technical parameters of inverters and synchronous machines in new energy solar power generation systems include the inverter's rated power, conversion efficiency, input voltage range, output voltage and frequency, as well as the coordination parameters between the inverter and the synchronous machine, such as control strategies and protection settings.

[0113] Rated power and rated voltage of new energy synchronous machines: These indicate the maximum power and voltage that the synchronous machine can provide during normal operation.

[0114] Moment of inertia: This refers to the magnitude of the inertia of the synchronous machine rotor when it rotates, and it affects the synchronous machine's response speed to changes in the power grid frequency.

[0115] Control strategy parameters: including relevant parameters for control strategies such as synchronous machine startup, grid connection, speed regulation, and voltage regulation.

[0116] Protection settings: These include settings for protection functions such as overcurrent protection, overvoltage protection, undervoltage protection, and overheat protection of the synchronous machine.

[0117] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S102 includes:

[0118] Based on regional power grid basic data, data features are preprocessed using regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters. The preprocessed data and extracted data features are used to train a neural network model. The model parameters are adjusted by gradient descent to minimize the model prediction error, thus obtaining a regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model.

[0119] Preprocessing is performed on regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters. The purpose of preprocessing includes data cleaning (removing erroneous, abnormal, or missing data), data normalization or standardization (converting data to a uniform scale so that the model can process it better), and possible data transformations (such as converting time series data to frequency domain data).

[0120] Features that significantly impact the planning and configuration of renewable energy synchronous machine (SMT) reactor systems are extracted from the preprocessed data. These features may include grid topology, load distribution, spatiotemporal distribution of renewable energy resources, and technical parameters of the SMT. Feature extraction methods may include statistical analysis, machine learning algorithms, and signal processing techniques.

[0121] Based on the extracted data features, a neural network model suitable for solving the planning and configuration problem of new energy synchronous machine reactor systems is constructed. The neural network structure may include an input layer, a hidden layer, and an output layer. The input layer receives the extracted data features, the hidden layer performs complex nonlinear transformations, and the output layer provides suggestions or predictions for the planning and configuration.

[0122] Using preprocessed data and extracted data features as input to the neural network, optimization algorithms such as gradient descent are used to adjust the network's parameters (such as weights and biases) to minimize the model's prediction error. During training, the dataset may need to be divided into training, validation, and test sets to evaluate the model's performance and fine-tune the parameters.

[0123] After training, the neural network model is validated and evaluated using a test set. Evaluation metrics may include prediction accuracy, recall, F1 score, etc.

[0124] After training, validation, and evaluation, a neural network model was obtained that can be used for the planning and configuration of multi-point layout of new energy synchronous reactor systems in regional power grids. This model can provide optimized planning and configuration suggestions based on new input data (such as real-time power system operation data and new energy resource data).

[0125] Through the training process in step S102, we obtained a neural network model capable of predicting and optimizing the planning and configuration of new energy synchronous reactor systems. This model will serve as the basis for subsequent steps (such as simulation verification and optimization) to help us better plan and configure new energy synchronous reactor systems in regional power grids.

[0126] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S103 includes:

[0127] By using sensors, monitoring equipment, or remote communication interfaces installed on the new energy synchronous machine stack system, real-time operating data of the new energy synchronous machine stack system is collected. The collected real-time operating data is preprocessed and loaded into the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model. The regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to calculate the input real-time operating data to obtain the planning and configuration scheme of the new energy synchronous machine stack system. The planning and configuration scheme of the new energy synchronous machine stack system includes the deployment point of the synchronous machine, capacity configuration, and control strategy.

[0128] The planning and configuration of the new energy synchronous machine stack system is decoded and transformed into planning and configuration information, which includes the geographical location of the deployment point, the capacity and model of the synchronous machine, and the setting of control parameters, and output as a report.

[0129] The system's operational data is collected in real time through various sensors, monitoring devices, or remote communication interfaces installed on the new energy synchronous machine stack system. This data may include electrical parameters such as the synchronous machine's output power, speed, voltage, and current, as well as environmental parameters such as ambient temperature, wind speed, and solar radiation intensity.

[0130] The collected real-time operational data undergoes preprocessing to ensure its accuracy and consistency. Preprocessing may include steps such as data cleaning (removing erroneous or outlier data), data normalization or standardization (transforming the data to a range acceptable to the model), and data synchronization (ensuring temporal consistency of data from different sources).

[0131] The preprocessed real-time operational data is loaded into the previously trained regional power grid multi-point layout new energy synchronous reactor system planning and configuration model. The model will use this data as input for subsequent calculations and optimizations.

[0132] By utilizing a regional power grid multi-point deployment planning and configuration model for new energy synchronous machine (SMT) reactor systems, and calculating based on input real-time operational data, a planning and configuration scheme for the new energy SMT reactor system is obtained. This scheme should consider factors such as the current state of the power grid, future load forecasts, and the availability of new energy resources, and provide specific recommendations on the deployment points, capacity configuration, and control strategies of the SMT reactors.

[0133] The calculated planning and configuration scheme is decoded and transformed into planning and configuration information that is easier to understand and implement. This information should include the geographical location of the deployment point (such as longitude, latitude, or specific address description), the capacity and model of the synchronizer (such as rated power, rated voltage, moment of inertia, etc.), and the settings of control parameters (such as start-up wind speed, cut-off wind speed, control strategy parameters, etc.).

[0134] The planning and configuration information should be compiled into a report so that decision-makers and implementers can clearly understand and adopt these recommendations. The report may include various formats such as charts, tables, and text descriptions to ensure that people from different backgrounds can understand it.

[0135] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S104 includes:

[0136] Determine the simulation objectives and scope, select simulation tools, and build a synchronous machine model in the simulation tool based on the simulation objectives, scope, and tools, and according to the actual configuration and parameters of the new energy synchronous machine stack system.

[0137] Set the initial and boundary conditions of the model, and configure different simulation scenarios based on the operating characteristics of the new energy synchronous machine stack system and the actual situation of the power grid. The different simulation scenarios include normal operation scenario, fault scenario and extreme weather scenario.

[0138] Run the simulation model, collect power grid operation simulation data, start the simulation tool, run the simulation model according to the set parameters and scenario, monitor the operation status of the model during the simulation, and collect the power grid operation data generated during the simulation, including voltage, current, power and frequency.

[0139] The simulation objectives and scope are defined to assess the impact of new energy synchronous machine (SCM) reactor systems on grid stability, optimize the layout and capacity configuration of SCM reactors, and verify the effectiveness of control strategies. Based on the simulation objectives, the simulation scope is defined, including the time scale (e.g., short-term, medium-term, long-term), spatial scale (e.g., local grid, regional grid, national grid), and physical phenomena to be considered (e.g., power flow distribution, voltage stability, frequency response).

[0140] Choose the appropriate simulation tool based on the simulation objectives and scope. Common power system simulation tools include PSASP, PSS / E, ETAP, and DIgSILENT. These tools have different characteristics and advantages, and the selection should be based on specific requirements.

[0141] In the simulation tool, a synchronous machine model is built based on the actual configuration and parameters of the new energy synchronous machine stack system. This includes the electrical parameters of the synchronous machine (such as rated power, rated voltage, impedance, etc.), mechanical parameters (such as moment of inertia, damping coefficient, etc.), and control strategy parameters (such as governor parameters, excitation system parameters, etc.).

[0142] Set the initial conditions for the simulation model, such as the initial power flow distribution of the power grid and the initial operating state of the synchronous machine. Based on the actual situation of the power grid and the simulation objectives, set the boundary conditions for the simulation model, such as the connection point of the external power grid and the load variation pattern.

[0143] Based on the operating characteristics of the new energy synchronous machine reactor system and the actual conditions of the power grid, different simulation scenarios are configured. These scenarios should cover various operating conditions that the power grid may encounter, such as normal operation scenarios, fault scenarios (such as line tripping, generator failure, etc.), and extreme weather scenarios (such as strong winds, heavy rain, high temperatures, etc.).

[0144] Start the simulation: Launch the simulation tool and run the simulation model according to the set parameters and scenario.

[0145] Monitoring Status: During the simulation process, the running status of the model is monitored in real time to ensure the smooth progress of the simulation.

[0146] Data Acquisition: Collect power grid operation data generated during the simulation process, including voltage, current, power, and frequency. This data will be used for subsequent analysis and evaluation.

[0147] Data collection: Organize and save the data generated during the simulation for subsequent analysis.

[0148] Data analysis: Analyze the collected simulation data to evaluate the impact of the new energy synchronous machine system on grid stability, optimize the layout and capacity configuration of the synchronous machine, and verify the effectiveness of the control strategy.

[0149] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S105 includes:

[0150] The simulation characteristic data of power grid operation is compared with the historical data of the regional power grid multi-point layout new energy synchronous machine stack system to verify the accuracy of the simulation model of the new energy synchronous machine stack system. If there is a large deviation between the simulation characteristic data of power grid operation and the historical data of the regional power grid multi-point layout new energy synchronous machine stack system, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system will be optimized.

[0151] The simulation data of power grid operation characteristics are compared with historical data of regional power grid multi-point layout of new energy synchronous reactor systems. This process aims to evaluate the accuracy of the simulation model by comparing actual operating data with simulation data.

[0152] The comparison results will be used to verify the accuracy of the simulation model of the new energy synchronous machine reactor system. If the simulation data matches the historical data well, it indicates that the model can simulate the actual system operation well; otherwise, further adjustments and optimizations to the model may be necessary.

[0153] Deviation Analysis: If significant deviations are found between the simulated power grid operation characteristics data and historical data of the regional power grid's multi-point layout of new energy synchronous reactor systems, deviation analysis is required. This process aims to identify the causes of the deviations, which may include improper model parameter settings, discrepancies between simulation conditions and actual conditions, or errors in historical data.

[0154] Data optimization: Based on the results of deviation analysis, historical data of multi-point layout of new energy synchronous reactor systems in the regional power grid are optimized. This may include data cleaning (removing erroneous or abnormal data), data calibration (adjusting data to better reflect reality), and data augmentation (increasing data points to improve data density through interpolation or extrapolation).

[0155] After data optimization, the simulation model needs to be rerun, and the new simulation results compared with the optimized historical data. This process may require multiple iterations until the accuracy of the simulation model meets the requirements. During the iterative optimization process, if defects or deficiencies are found in the model itself, adjustments may be necessary. This may include modifying the model structure, adjusting model parameters, and improving the control strategy.

[0156] Once the accuracy of the simulation model is satisfied, the simulation results need to be evaluated. This includes analyzing the performance of the new energy synchronous reactor system under different scenarios and assessing the feasibility and effectiveness of the planned configuration scheme.

[0157] The evaluation results should be compiled into a report so that decision-makers or implementers can clearly understand the accuracy of the simulation model, the optimization process of historical data, and the analysis and evaluation of the simulation results. The report should include detailed data comparison results, deviation analysis, data optimization methods, model adjustment plans, and simulation result analysis.

[0158] Specifically, in the aforementioned method for planning and configuring a multi-point layout of a regional power grid for a new energy synchronous reactor system, step S105 includes:

[0159] Integrate historical data and grid operation simulation characteristic data of multi-point layout of new energy synchronous machine stack systems in regional power grids, and sort out the historical data of multi-point layout of new energy synchronous machine stack systems in regional power grids, including the operation records, fault records and maintenance records of new energy synchronous machine stack systems. Extract grid operation simulation characteristic data, including voltage fluctuation, current change and power factor index.

[0160] The optimization objectives are received, including minimizing voltage fluctuations, maximizing the utilization rate of new energy sources, and reducing system losses.

[0161] Receive the framework parameters of the genetic algorithm, determine the population size of the genetic algorithm based on the framework parameters, i.e. the number of solutions participating in the optimization at the same time, design selection, crossover, and mutation genetic operators, define the fitness function to evaluate the quality of each solution, and the solution is the planned configuration scheme.

[0162] Generate an initial solution set, i.e., the population of the genetic algorithm. Based on the parameter range and constraints of the planning and configuration model of the multi-point layout of new energy synchronous machine stack system in the regional power grid, randomly generate a set of initial solutions. Each solution represents a planning and configuration scheme of a new energy synchronous machine stack system.

[0163] The fitness value of each solution in the population is calculated. Each solution is then substituted into the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system. Based on the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data, the fitness value corresponding to each solution is calculated.

[0164] A new set of solutions is generated through selection, crossover, and mutation until the preset number of iterations is reached or the optimization objective is met. After optimization, the solution with the highest fitness value is selected from the population as the optimal solution, which is the optimized new energy synchronous machine stack system planning and configuration scheme.

[0165] Historical data on multi-site deployment of new energy synchronous turbine reactor systems in the regional power grid were integrated, including operation records, fault records, and maintenance records. This data provides practical operational experience and reference for optimization. Feature data, such as voltage fluctuations, current variations, and power factor indices, were extracted from power grid operation simulations. This data reflects the operating status of the power grid under different configurations and is an important basis for evaluating the merits of planned configuration schemes.

[0166] Based on the actual needs of the power grid and the characteristics of the new energy synchronous reactor system, optimization objectives are set. Common optimization objectives include minimizing voltage fluctuations, maximizing the utilization rate of new energy sources, and reducing system losses.

[0167] This paper determines the population size (i.e., the number of solutions simultaneously participating in optimization), the design methods for selection, crossover, and mutation genetic operators, and the definition method for the fitness function. The fitness function is used to evaluate the quality of each solution. It is usually constructed based on the optimization objective, such as converting indicators like voltage fluctuation, renewable energy utilization rate, and system losses into fitness values.

[0168] Based on the parameter range and constraints of the regional power grid multi-point layout new energy synchronous machine reactor system planning and configuration model, a set of initial solutions is randomly generated. Each solution represents a planning and configuration scheme for a new energy synchronous machine reactor system, including the deployment point of the synchronous machine, capacity configuration, and control strategy.

[0169] Substituting each solution into the regional power grid multi-point layout new energy synchronous reactor system planning and configuration model, and combining historical data and simulation characteristic data, the fitness value corresponding to each solution is calculated. This step is crucial for evaluating the merits of each solution.

[0170] Selection operation: Select the best solution based on its fitness value as the parent solution to generate the next generation of solutions.

[0171] Crossover operation: Perform a crossover operation on the selected parent solution to generate a new solution (child).

[0172] Mutation operation: Perform a mutation operation on the newly generated solution to increase the diversity of solutions.

[0173] Iterative optimization: Repeat selection, crossover, and mutation operations until the preset number of iterations is reached or the optimization objective is met.

[0174] After optimization, the solution with the highest fitness value is selected as the optimal solution from the population. This solution is the optimized planning and configuration scheme for the new energy synchronous machine reactor system. The optimal solution and its corresponding planning and configuration scheme are output. The data, charts, and results from the optimization process are compiled into a detailed report so that decision-makers or implementers can understand the optimization process and results.

[0175] This invention provides a planning and configuration method for multi-point layout of new energy synchronous reactor systems in regional power grids. Addressing the issues of weak voltage support capacity, severe transient overvoltage problems, and voltage fluctuations and flicker caused by transient overvoltages in some high-proportion new energy power systems at the sending end, the following technical solutions are adopted:

[0176] First, by acquiring basic data on the regional power grid, new energy resources, power system operation data, and parameters of new energy synchronous machines, these data provide a foundation for subsequent model training and planning configuration.

[0177] Secondly, based on the above data, a neural network model was trained to obtain a planning and configuration model for a multi-point layout of new energy synchronous turbine reactor systems in the regional power grid. This model can output the first planning and configuration of the new energy synchronous turbine reactor system based on real-time operating data of the new energy synchronous turbine reactor system.

[0178] Then, the planned configuration is substituted into a preset simulation model of a new energy synchronous machine reactor system for simulation to obtain grid operation simulation data. Features are extracted from these data and matched against a preset transient overvoltage scheme configuration knowledge base to check if a suitable transient overvoltage scheme configuration exists.

[0179] If the transient overvoltage matching scheme fails, meaning no suitable configuration scheme is found, historical data of the regional power grid's multi-point deployment of new energy synchronous reactor systems and power grid operation simulation characteristic data will be used to optimize the planning configuration model using a genetic algorithm. Through continuous iteration and optimization, a second planned configuration for the new energy synchronous reactor system is obtained. This optimized configuration scheme is more likely to solve the transient overvoltage problem.

[0180] In practical implementation, by monitoring the real-time operating data of the new energy synchronous machine stack system and substituting it into the optimized planning and configuration model, a more accurate and effective planning and configuration scheme can be calculated. This scheme includes the deployment point of the synchronous machine, capacity configuration, and control strategies, which can effectively improve the stability of the regional power grid and solve transient overvoltage problems, thereby avoiding voltage fluctuations and flicker problems in the power grid.

[0181] In summary, this invention provides an effective planning and configuration method for multi-point layout of new energy synchronous reactor systems in regional power grids through a series of steps including neural network model training, simulation verification, and genetic algorithm optimization. This method can solve the problems of weak voltage support capability and transient overvoltage in some high-proportion new energy power systems at the sending end.

Claims

1. A method for planning and configuring a multi-point layout of a new energy synchronous reactor system in a regional power grid, characterized in that, include: Step S101: Obtain regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters; Step S102: Based on the regional power grid basic data, the neural network model is trained using the regional power grid basic data, new energy resource data, power system operation data and new energy synchronous machine parameters to obtain the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model. Step S103: Collect real-time operating data of the new energy synchronous machine stack system and substitute it into the planning and configuration model of the multi-point layout of the new energy synchronous machine stack system in the regional power grid to obtain the planning and configuration of the first new energy synchronous machine stack system. Step S104: Substitute the planning configuration of the new energy synchronous machine stack system into the preset simulation model of the new energy synchronous machine stack system to obtain grid operation simulation data. Extract data features from the grid operation simulation data to obtain grid operation simulation feature data. Match the grid operation simulation feature data in the preset transient overvoltage scheme configuration knowledge base to obtain transient overvoltage scheme configuration matching results. The transient overvoltage matching scheme results include successful configuration of the transient overvoltage matching scheme and unsuccessful configuration of the transient overvoltage matching scheme. Step S105: If the transient overvoltage matching scheme fails, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data are used to optimize the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system using a genetic algorithm. The optimized regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to process the real-time new energy synchronous machine stack system operation data, and output the second new energy synchronous machine stack system planning and configuration. The second new energy synchronous machine stack system planning and configuration is used as the planning and configuration of the regional power grid multi-point layout new energy synchronous machine stack system.

2. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 1, characterized in that, Step S101 includes: The basic data of the regional power grid includes the topology of the regional power grid, transmission line parameters, substation locations, substation capacity, and substation load distribution information; New energy resource data includes wind energy and solar energy within the region; Acquiring power system operation data includes real-time load data of the power grid, real-time power supply and demand curves of the power grid, real-time power grid frequency of the power grid, and real-time voltage stability indicators of the power grid. Obtaining parameters for new energy synchronous machines includes technical parameters of new energy wind turbines, technical parameters of inverters and synchronous machines in new energy solar power systems, rated power, rated voltage, moment of inertia, control strategy parameters, and protection settings of the new energy synchronous machine.

3. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 1, characterized in that, Step S102 includes: Based on regional power grid basic data, data features are preprocessed using regional power grid basic data, new energy resource data, power system operation data, and new energy synchronous machine parameters. The preprocessed data and extracted data features are used to train a neural network model. The model parameters are adjusted by gradient descent to minimize the model prediction error, thus obtaining a regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model.

4. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 1, characterized in that, Step S103 includes: By using sensors, monitoring equipment, or remote communication interfaces installed on the new energy synchronous machine stack system, real-time operating data of the new energy synchronous machine stack system is collected. The collected real-time operating data is preprocessed and loaded into the regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model. The regional power grid multi-point layout new energy synchronous machine stack system planning and configuration model is used to calculate the input real-time operating data to obtain the planning and configuration scheme of the new energy synchronous machine stack system. The planning and configuration scheme of the new energy synchronous machine stack system includes the deployment point of the synchronous machine, capacity configuration, and control strategy. The planning and configuration of the new energy synchronous machine stack system is decoded and transformed into planning and configuration information, which includes the geographical location of the deployment point, the capacity and model of the synchronous machine, and the setting of control parameters, and output as a report.

5. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 1, characterized in that, Step S104 includes: Determine the simulation objectives and scope, select simulation tools, and build a synchronous machine model in the simulation tool based on the simulation objectives, scope, and tools, and according to the actual configuration and parameters of the new energy synchronous machine stack system. Set the initial and boundary conditions of the model, and configure different simulation scenarios based on the operating characteristics of the new energy synchronous machine stack system and the actual situation of the power grid. The different simulation scenarios include normal operation scenario, fault scenario and extreme weather scenario. Run the simulation model, collect power grid operation simulation data, start the simulation tool, run the simulation model according to the set parameters and scenario, monitor the operation status of the model during the simulation, and collect the power grid operation data generated during the simulation, including voltage, current, power and frequency.

6. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 1, characterized in that, Step S105 includes: The simulation characteristic data of power grid operation is compared with the historical data of the regional power grid multi-point layout new energy synchronous machine stack system to verify the accuracy of the simulation model of the new energy synchronous machine stack system. If there is a large deviation between the simulation characteristic data of power grid operation and the historical data of the regional power grid multi-point layout new energy synchronous machine stack system, the historical data of the regional power grid multi-point layout new energy synchronous machine stack system will be optimized.

7. The planning and configuration method for a multi-point layout of a regional power grid's new energy synchronous reactor system as described in claim 6, characterized in that, Step S105 includes: Integrate historical data and grid operation simulation characteristic data of multi-point layout of new energy synchronous machine stack systems in regional power grids, and sort out the historical data of multi-point layout of new energy synchronous machine stack systems in regional power grids, including the operation records, fault records and maintenance records of new energy synchronous machine stack systems. Extract grid operation simulation characteristic data, including voltage fluctuation, current change and power factor index. The optimization objectives are received, including minimizing voltage fluctuations, maximizing the utilization rate of new energy sources, and reducing system losses. Receive the framework parameters of the genetic algorithm, determine the population size of the genetic algorithm based on the framework parameters, i.e. the number of solutions participating in the optimization at the same time, design selection, crossover, and mutation genetic operators, define the fitness function to evaluate the quality of each solution, and the solution is the planned configuration scheme. Generate an initial solution set, i.e., the population of the genetic algorithm. Based on the parameter range and constraints of the planning and configuration model of the multi-point layout of new energy synchronous machine stack system in the regional power grid, randomly generate a set of initial solutions. Each solution represents a planning and configuration scheme of a new energy synchronous machine stack system. The fitness value of each solution in the population is calculated. Each solution is then substituted into the planning and configuration model of the regional power grid multi-point layout new energy synchronous machine stack system. Based on the historical data of the regional power grid multi-point layout new energy synchronous machine stack system and the power grid operation simulation characteristic data, the fitness value corresponding to each solution is calculated. A new set of solutions is generated through selection, crossover, and mutation until the preset number of iterations is reached or the optimization objective is met. After optimization, the solution with the highest fitness value is selected from the population as the optimal solution, which is the optimized new energy synchronous machine stack system planning and configuration scheme.

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