A method for coordinated optimization of power grid operation based on distributed generation power injection

Abstract

The present invention relates to the field of power grid operation and maintenance technology. The present invention provides a method for coordinated optimization operation of a power grid with distributed power source power injection. The present invention combines a genetic algorithm, a power grid simulation model, and a control model to achieve efficient and stable operation of the power grid. The distributed power source power injection scheme is globally optimized through a genetic algorithm to obtain a power injection scheme that meets power grid constraints and is optimal for the target. At the same time, the power grid simulation model is used to simulate and evaluate the scheme to improve its practicality. The control model quickly adjusts the power injection scheme according to the real-time power grid status and task requirements, and is applied to the actual power grid. The present invention improves the scheduling flexibility of the power grid, promotes the efficient use of distributed power sources, reduces the operating costs of the power grid, and enhances the adaptability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation and maintenance, and in particular to a method for coordinated optimization operation of a power grid with distributed power generation injection. Background Art

[0002] Grid collaborative optimization with distributed power generation injection refers to the use of control strategies and technical means to achieve effective collaboration between distributed power sources (such as solar photovoltaic and wind power) and the main power grid. The optimization process aims to improve the operational efficiency, stability, and security of the power grid. Specifically, grid collaborative optimization with distributed power generation injection includes the regulation of the active and reactive power of distributed power sources to achieve optimal control of performance indicators such as grid voltage and power factor. Through technologies such as hierarchical control strategies and multi-agent systems, power injection at distributed power access points can be controlled, enabling them to track reference instructions from the main power grid dispatch center and achieve active power sharing and voltage balancing among distributed power sources.

[0003] In the collaborative optimization process of distributed power generation injection into the power grid, there are the following technical pain points: the output power of distributed power generation is affected by factors such as weather and operating conditions, and shows strong volatility and intermittency, which increases the difficulty of grid frequency and voltage control and makes traditional deterministic planning difficult to adapt to the construction of new power systems. To address this technical pain point, the present invention provides a method for collaborative optimization operation of a power grid with distributed power generation injection. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, the present invention provides a method for coordinated optimization operation of a power grid with distributed power generation injection, which solves the problem that the output power of distributed power generation is affected by factors such as weather and operating conditions, showing strong volatility and intermittency, which increases the difficulty of grid frequency and voltage control and makes traditional deterministic planning difficult to adapt to the construction of new power systems.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] The method for coordinated optimization operation of a power grid with distributed power generation injection of the present invention comprises:

[0007] Step S101, obtaining real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data;

[0008] Step S102: obtaining natural environment data of the location where the power grid is located, performing cluster analysis on the natural environment data of the location where the power grid is located to obtain a clustering result of the natural environment of the location where the power grid is located, and establishing a correlation between the clustering result of the natural environment of the location where the power grid is located and time information to obtain a knowledge base of the natural environment of the power grid;

[0009] Step S103: receiving a grid task for distributed power injection, the grid task for distributed power injection including basic task information, distributed power information, and grid constraint data; matching the grid task for distributed power injection in a grid natural environment knowledge base to obtain natural environment data corresponding to the grid task for distributed power injection;

[0010] Step S104: Using the real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data to train a random forest model to obtain a grid simulation model for distributed power source power injection, and substituting the basic task information into the grid simulation model for distributed power source power injection to obtain a grid simulation result for distributed power source power injection;

[0011] Step S105, compare the grid simulation result of distributed power supply power injection with the grid constraint data to obtain the comparison result of the grid simulation result of distributed power supply power injection; if the grid simulation result of distributed power supply power injection is outside the range of the grid constraint data, optimize the grid simulation model of distributed power supply power injection based on the real-time power data of distributed power supply, grid status data and grid topology data using a genetic algorithm to obtain an optimized grid simulation model of distributed power supply power injection; use the optimized grid simulation model of distributed power supply power injection as the grid control model of distributed power supply power injection; substitute the grid task of distributed power supply power injection into the grid control model of distributed power supply power injection to obtain real-time grid control parameters of distributed power supply power injection.

[0012] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S101 includes:

[0013] Through the interface with the monitoring system or data acquisition and monitoring control system of distributed power sources, the current output power data of each distributed power source is obtained in real time, and the real-time power data of distributed power sources is obtained. Distributed power sources include solar power stations, wind power stations and energy storage systems;

[0014] Obtaining real-time grid status data from the grid monitoring system. The real-time grid status data includes the voltage, current, frequency, power factor of each node, grid load conditions, and line conditions. The line conditions include line overload and line fault conditions, and obtaining grid status data.

[0015] The topological structure information of the power grid includes the connection relationship and parameters of the nodes, lines, transformers, and switchgear of the power grid;

[0016] The control strategy information of the distributed power supply includes power control strategy, voltage control strategy and frequency control strategy. The power control strategy includes constant power control and maximum power point tracking control.

[0017] Obtain dispatch instructions for distributed power sources from the power grid dispatch center, including power dispatch instructions, start and stop instructions, and reactive power compensation instructions, which are used to coordinate power exchange between distributed power sources and the power grid and obtain command data from the power grid dispatch center;

[0018] The historical operation data is obtained from the historical database of the power grid and distributed power generation. The historical operation data includes past power data, power grid status data, fault records and maintenance records.

[0019] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S102 includes:

[0020] Obtaining natural environmental data of the power grid's location, including meteorological data, geographic data, and environmental data. Meteorological data includes temperature, humidity, wind speed, wind direction, precipitation, and air pressure; geographic data includes topography, altitude, and soil type; and environmental data includes air quality and solar radiation intensity.

[0021] Preprocess the natural environment data and extract features from the preprocessed natural environment data. The obtained meteorological features are used to reflect the impact of the natural environment on the operation of the power grid. The meteorological features include daily average temperature features, maximum temperature features, minimum temperature features, average wind speed features, and precipitation features.

[0022] Use clustering algorithms to perform cluster analysis on the extracted features to obtain natural environment clustering results, and establish an association relationship between the natural environment clustering results and the corresponding time information;

[0023] Integrate natural environment data, natural environment clustering results and their association with time information into a structured knowledge base.

[0024] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S103 includes:

[0025] Receive grid tasks injected by distributed power generation, parse the received grid task data, and extract basic task information, distributed power generation information, and grid constraint data;

[0026] The basic information of a task includes task ID, task name, planned execution time, and task priority;

[0027] Distributed power information includes the type, rated power, and access point location of the distributed power source;

[0028] Grid constraint data includes the grid’s voltage level, frequency stability requirements, and power factor limits;

[0029] Extracting task execution time information from the basic task information. The task execution time information includes the date and time period of the planned execution time. Using the task execution time information, search for the natural environment data clustering results corresponding to the task execution time in the power grid natural environment knowledge base. The matching process involves searching for natural environment data corresponding to a specific date, time period or season.

[0030] Extracting natural environment data that matches the task execution time from the power grid natural environment knowledge base. The natural environment data includes temperature, wind speed, precipitation, light information, and wind speed information during the period.

[0031] The acquired natural environment data is integrated with the grid mission data injected by distributed power generation, and the integrated data is used for subsequent grid simulation, optimization and control processes.

[0032] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S104 includes:

[0033] Summarizes real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center command data, and historical operation data;

[0034] Preprocess the data and perform feature extraction on the preprocessed data to obtain the real-time power of the distributed power supply, the voltage of each node in the power grid, the current of each node in the power grid, the power flow of the line, and the control parameters of the distributed power supply;

[0035] The dataset is divided into a training set and a test set. The training set is used to train the random forest model, and the test set is used to evaluate the performance of the model.

[0036] Use the training set data to train the random forest model, receive and set the parameters of the random forest, which include the number of trees, maximum depth, and minimum number of sample splits, run the random forest algorithm, obtain a grid simulation model of distributed power injection, substitute the basic information of the grid task of distributed power injection into the grid simulation model of distributed power injection, the basic information of the grid task includes task time, distributed power type, and power data, run the grid simulation model of distributed power injection, and obtain the grid simulation results of distributed power injection, which include voltage changes at each node of the grid, power flow of the line, and power output data of the distributed power.

[0037] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S105 includes:

[0038] Determine the optimization objectives based on the actual needs and constraints of the power grid. The optimization objectives include minimizing grid losses, stabilizing voltage, and maximizing the utilization of distributed power sources.

[0039] Receive and set the basic parameters of the genetic algorithm, including population size, genetic generations, crossover probability, and mutation probability;

[0040] The power injection scheme of the distributed power generation is represented as an individual in the genetic algorithm using real number coding. The individual includes the power setting value or control parameter of the distributed power generation, and the individual is used as the object of genetic algorithm optimization.

[0041] Initialize the population. According to the set population size, randomly generate a group of initial individuals to form the initial population. Each individual represents a distributed power injection scheme.

[0042] Using real-time power data of distributed generation, grid status data, and grid topology data, the fitness of each individual is evaluated through a grid simulation model. The fitness function should be designed according to the optimization objective.

[0043] According to the fitness evaluation results, individuals with higher fitness are selected as parents for crossover and mutation operations;

[0044] Perform a crossover operation on the selected parent individuals to generate new offspring individuals;

[0045] The crossover operation is performed by single-point crossover, double-point crossover or uniform crossover;

[0046] Perform mutation operations on offspring individuals by changing the gene values ​​in the individuals or introducing new genes;

[0047] Update the population, replace some or all of the parent individuals with the generated offspring individuals, update the population, and determine whether to continue iterating based on the set genetic generations or convergence conditions;

[0048] If the set genetic generation number is reached or the fitness meets the convergence condition, the iteration is stopped. After iterative optimization of the genetic algorithm, the individual with the highest fitness is obtained, that is, the optimized distributed power generation power injection scheme, which is used as the parameter of the optimized distributed power generation power injection power grid simulation model.

[0049] Furthermore, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S105 includes:

[0050] The distributed power generation power injection power grid simulation model is optimized by genetic algorithm, and the optimized model is obtained. The parameters of the optimized distributed power generation power injection power grid simulation model are set as the initial parameters of the distributed power generation power injection power grid control model;

[0051] Receive the grid task of distributed generation power injection from the grid dispatch center. The grid task should include basic information of the task, such as task time, distributed generation type and expected power injection amount;

[0052] Preprocessing the received power grid task data, substituting the preprocessed power grid task data into the optimized distributed power generation power injection power grid control model, and taking the distributed power generation power injection requirements and time constraints in the task as inputs of the model;

[0053] Executing a grid control model for distributed power generation injection, performing simulation calculations based on input task data and parameters of the grid control model for distributed power generation injection, wherein the grid control model for distributed power generation injection uses the current state and topology of the grid and the real-time power data of the distributed power generation to calculate a distributed power generation injection plan that meets grid constraints and task requirements;

[0054] Extracting real-time distributed power generation power injection grid control parameters from the output of the distributed power generation power injection grid control model, the real-time distributed power generation power injection grid control parameters including the power adjustment amount of the distributed power generation, the adjustment parameters of the control strategy and the time point of power injection;

[0055] Before applying the grid control parameters of the real-time distributed power generation power injection to the actual grid, the parameters of the grid simulation model of the optimized distributed power generation power injection are compared with the grid control parameters of the real-time distributed power generation power injection. If the grid constraints are met, the verification is successful. If the verification is successful, the grid control parameters of the real-time distributed power generation power injection are applied to the distributed power control system of the actual grid;

[0056] After applying the grid control parameters of the real-time distributed power generation power injection, the state of the grid and the power injection of the distributed power generation are continuously monitored. If they do not meet expectations, the grid control parameters of the real-time distributed power generation power injection are adjusted.

[0057] Beneficial effects of the present invention:

[0058] Leveraging the global optimization capabilities of genetic algorithms, this invention can find the optimal power injection scheme that minimizes losses, ensures voltage stability, and maximizes distributed power generation utilization while meeting grid constraints. This helps improve the overall operational efficiency and stability of the grid, reduces energy waste, and enhances the grid's ability to accommodate distributed power generation.

[0059] This invention simulates and evaluates distributed power generation power injection schemes using a power grid simulation model, providing a scientific basis for grid dispatch. Furthermore, by combining real-time grid status data with task requirements, the invention can rapidly generate and adjust power injection schemes, enhancing the flexibility of grid dispatch. By optimizing the power injection schemes of distributed power sources, the invention can maximize their generation capacity and reduce the occurrence of wind and solar power curtailment. This helps improve the utilization rate of renewable energy and promote the transformation and upgrading of the energy structure.

[0060] This invention combines genetic algorithms, power grid simulation models, and control models to construct an intelligent distributed power generation power injection optimization system. This system automatically develops, verifies, and applies power injection plans, enhancing the intelligence and automation level of the power grid. By optimizing the power injection plans of distributed power sources, this invention can reduce power grid losses and maintenance costs. Furthermore, improving the utilization of distributed power sources also helps reduce dependence on traditional energy sources and procurement costs, thereby reducing the overall operating costs of the power grid.

[0061] The present invention takes into account the real-time status and topology of the power grid and the real-time power data of distributed power sources, so that the optimized power injection scheme can better adapt to changes and fluctuations in the power grid, enhance the adaptability and resilience of the power grid, and improve the power grid's ability to respond to emergencies such as extreme weather and equipment failures.

[0062] In summary, the grid collaborative optimization operation method with distributed power generation power injection described in the present invention has shown significant beneficial effects in improving grid efficiency and stability, enhancing the flexibility and accuracy of grid scheduling, promoting the efficient utilization of distributed power sources, improving the intelligence level of the grid, reducing grid operation costs, and enhancing the adaptability and resilience of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0064] Figure 1 This is a flow chart of the grid collaborative optimization operation method for distributed power generation power injection provided by the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0066] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0067] The method for coordinated optimization operation of a power grid with distributed power generation injection of the present invention comprises:

[0068] Step S101, obtaining real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data;

[0069] Real-time power data for distributed power sources: This interface connects to the monitoring system or Supervisory Control and Data Acquisition (SCADA) of distributed power sources to obtain real-time output power data for each distributed power source. Distributed power sources include, but are not limited to, solar power plants, wind power plants, and energy storage systems.

[0070] Grid status data: Real-time grid status data is obtained from the grid monitoring system. This data includes basic parameters such as voltage, current, frequency, power factor, etc. at each grid node, as well as the grid load and line status (such as whether it is overloaded or has a fault).

[0071] Grid topology data: Obtain grid topology information, including the connection relationships and parameters of nodes, lines, transformers, switchgear, etc. This information is the basis for understanding the grid structure, performing grid simulation, and optimizing it.

[0072] Distributed power generation control strategy data: This data captures the control strategy information for distributed power generation, including power control strategies (such as constant power control and maximum power point tracking), voltage control strategies, and frequency control strategies. This strategy information reflects the operating mode and regulation capabilities of the distributed power generation.

[0073] Grid dispatch center command data: Obtain dispatch instructions for distributed power sources from the grid dispatch center. These instructions include power dispatch instructions, start and stop instructions, reactive power compensation instructions, etc., which are used to coordinate power exchange between distributed power sources and the grid.

[0074] Historical operating data: This data is collected from the historical databases of the power grid and distributed generation (DGs). This includes past power data, grid status data, fault records, and maintenance records. Historical data is crucial for analyzing the operating patterns of the power grid and DGs and improving the accuracy of simulation models.

[0075] Through step S101, the method of the present invention can comprehensively and real-timely acquire data of the power grid and distributed power sources, providing a solid data foundation for subsequent data processing, simulation, optimization and control.

[0076] Step S102: obtaining natural environment data of the location where the power grid is located, performing cluster analysis on the natural environment data of the location where the power grid is located to obtain a clustering result of the natural environment of the location where the power grid is located, and establishing a correlation between the clustering result of the natural environment of the location where the power grid is located and time information to obtain a knowledge base of the natural environment of the power grid;

[0077] First, obtain the natural environment data of the location where the power grid is located, including but not limited to meteorological data (such as temperature, humidity, wind speed, wind direction, precipitation, air pressure, etc.), geographical data (such as terrain, altitude, soil type, etc.) and environmental data (such as air quality, solar radiation intensity, etc.).

[0078] The acquired natural environment data undergoes preprocessing, including steps such as data cleaning, denoising, and interpolation, to improve data quality. Features are then extracted from the preprocessed data to reflect the impact of the natural environment on grid operation. For example, meteorological features may include daily average temperature, maximum temperature, minimum temperature, average wind speed, and precipitation.

[0079] Clustering algorithms (such as K-means and hierarchical clustering) are used to perform cluster analysis on the extracted features, clustering similar natural environment data into clusters. The clustering results reflect the distribution patterns and change patterns of the natural environment at different time or spatial scales.

[0080] The natural environment clustering results are associated with the corresponding time information (such as date, time period, season, etc.). This association helps to understand the changing trends of the natural environment over time and the possible impact of such changes on power grid operation.

[0081] The natural environment data, natural environment clustering results, and their relationship with time information are integrated into a structured knowledge base. This knowledge base not only contains a wealth of natural environment data but also reveals the inherent connections and changing patterns between these data, providing an important reference for subsequent power grid simulation, optimization, and control.

[0082] Through step S102, the method of the present invention can establish a comprehensive and accurate power grid natural environment knowledge base, providing strong support for subsequent power grid operation optimization.

[0083] Step S103: receiving a grid task for distributed power injection, the grid task for distributed power injection including basic task information, distributed power information, and grid constraint data; matching the grid task for distributed power injection in a grid natural environment knowledge base to obtain natural environment data corresponding to the grid task for distributed power injection;

[0084] A grid task that receives power from distributed generation (DGs) includes all the basic information required to execute the task, including the task ID, task name, planned execution time, and task priority. Furthermore, the task includes information about the DG, such as its type (e.g., solar power plant, wind power plant, energy storage system), rated power, and access point location. The task also specifies grid constraints, such as voltage level, frequency stability requirements, and power factor limits. These constraints are key parameters for ensuring safe and stable grid operation.

[0085] To match against the power grid's natural environment knowledge base, the system extracts information related to the task's execution time from the basic task information, including the date and time period of the planned execution. Using this time information, it searches and matches against the previously constructed power grid's natural environment knowledge base. The natural environment data in the knowledge base has been categorized and organized based on time information (such as date, time period, and season). Through the matching process, the system is able to find clusters of natural environment data corresponding to the task's execution time. These clusters include key natural environment parameters such as temperature, wind speed, precipitation, and light intensity during that time period.

[0086] When a matching natural environment data cluster is found, the system extracts the specific natural environment data from the knowledge base. This natural environment data is then used in subsequent grid simulation, optimization, and control processes to ensure that the distributed generation power injection scheme is adapted to the prevailing natural environment conditions while meeting the grid's constraints.

[0087] Through step S103, the method of the present invention can ensure that the grid task of injecting power from distributed power sources can fully take the prevailing natural environmental conditions into consideration when executing the task, thereby improving the safety and stability of grid operation.

[0088] Step S104: Using the real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data to train a random forest model to obtain a grid simulation model for distributed power source power injection, and substituting the basic task information into the grid simulation model for distributed power source power injection to obtain a grid simulation result for distributed power source power injection;

[0089] First, all acquired data is aggregated, including real-time power data from distributed generation (DGs), grid status data, grid topology data, DG control strategy data, grid dispatch center command data, and historical operation data. This data is then preprocessed, including data cleaning (removing outliers and missing values), data transformation (such as normalization and standardization), and data dimensionality reduction (such as feature selection and principal component analysis) to improve data quality and reduce the complexity of model training.

[0090] Extract key features from the preprocessed data. These features may include the real-time power output of distributed generation (DGs), voltage and current at each grid node, power flow along the lines, and control parameters of the DGs. Feature selection should be based on their impact on grid operation and their correlation with DG power injection.

[0091] Split the dataset into a training set and a test set. The training set is used to train the random forest model, while the test set is used to evaluate the model's performance. Ensure that the training and test sets have similar data distributions to avoid overfitting or underfitting.

[0092] Use the training set data to train a random forest model. Random forest is an ensemble learning method that improves model accuracy by building multiple decision trees and combining their predictions. During training, you need to set parameters for the random forest, such as the number of trees, maximum depth, and minimum number of sample splits. These parameters should be adjusted based on the specific problem and dataset.

[0093] Evaluate the trained random forest model using the test set data. Measure the model's performance by comparing the model's predictions with the actual results. Based on the evaluation results, fine-tune the model, such as adjusting parameters and adding features, to improve its accuracy and generalization.

[0094] Basic mission information (such as mission time, DG type, and power data) is fed into the trained random forest model. Based on the input mission information and current grid status data, the model simulates the impact of DG power injection on the grid and outputs simulation results for the DG power injection. These results may include voltage changes at each grid node, power flow along the lines, and DG power output data, providing important insights for subsequent grid optimization and control.

[0095] Through step S104, the method of the present invention can use machine learning technology to simulate and predict the impact of distributed power generation power injection on the power grid, thereby providing a scientific basis for the optimized operation of the power grid.

[0096] Step S105, compare the grid simulation result of distributed power supply power injection with the grid constraint data to obtain the comparison result of the grid simulation result of distributed power supply power injection; if the grid simulation result of distributed power supply power injection is outside the range of the grid constraint data, optimize the grid simulation model of distributed power supply power injection based on the real-time power data of distributed power supply, grid status data and grid topology data using a genetic algorithm to obtain an optimized grid simulation model of distributed power supply power injection; use the optimized grid simulation model of distributed power supply power injection as the grid control model of distributed power supply power injection; substitute the grid task of distributed power supply power injection into the grid control model of distributed power supply power injection to obtain real-time grid control parameters of distributed power supply power injection.

[0097] In step S105, the method of the present invention conducts an in-depth analysis and optimization of the grid simulation results of the distributed power generation power injection, and the specific steps are as follows:

[0098] The grid simulation results of distributed generation power injection are compared with the grid's constraints, which may include voltage level limits, frequency stability requirements, power factor ranges, and line current carrying capacity limits. The purpose of this comparison is to verify that the simulation results are within all grid constraints, that is, whether they meet the grid's requirements for safe and stable operation.

[0099] If the simulation results show that the grid state after the DG power injection is within all constraints, then the simulation results are considered compliant and can be directly used for subsequent grid control. If the simulation results show that any one or more constraints are violated, then the simulation results are considered non-compliant and require optimization.

[0100] For non-compliant simulation results, a genetic algorithm is used to optimize the grid simulation model for distributed generation power injection. A genetic algorithm is a heuristic search algorithm that solves optimization problems by simulating natural selection and genetic mechanisms. During the optimization process, real-time distributed generation power data, grid status data, and grid topology data are used as inputs, with the goal of minimizing violations of grid constraints or achieving specific optimization goals (such as minimizing voltage deviation or line loss). Through an iterative search using the genetic algorithm, a set of optimal distributed generation power injection schemes is found so that the simulation results meet the grid constraints.

[0101] The optimized grid simulation model for distributed power generation power injection is used as a new grid control model for distributed power generation power injection. This control model can more accurately reflect the impact of distributed power generation power injection on the grid and meet the grid constraints.

[0102] The grid task of injecting power from distributed generation (DGs) is substituted into the optimized grid control model. Based on the model's predictions and the grid's real-time status data, the grid control parameters for DG power injection are calculated. These control parameters may include the DG's power output setpoint, reactive power compensation, and voltage regulation. These parameters are used to guide the actual operation of the DGs and the real-time control of the grid.

[0103] Through step S105, the method of the present invention can ensure that the grid simulation results of distributed power generation power injection meet the grid constraints, and obtain real-time grid control parameters through optimization, providing strong guarantee for the safe and stable operation of the grid.

[0104] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S101 includes:

[0105] Through the interface with the monitoring system or data acquisition and monitoring control system of distributed power sources, the current output power data of each distributed power source is obtained in real time, and the real-time power data of distributed power sources is obtained. Distributed power sources include solar power stations, wind power stations and energy storage systems;

[0106] Obtaining real-time grid status data from the grid monitoring system. The real-time grid status data includes the voltage, current, frequency, power factor of each node, grid load conditions, and line conditions. The line conditions include line overload and line fault conditions, and obtaining grid status data.

[0107] The topological structure information of the power grid includes the connection relationship and parameters of the nodes, lines, transformers, and switchgear of the power grid;

[0108] The control strategy information of the distributed power supply includes power control strategy, voltage control strategy and frequency control strategy. The power control strategy includes constant power control and maximum power point tracking control.

[0109] Obtain dispatch instructions for distributed power sources from the power grid dispatch center, including power dispatch instructions, start and stop instructions, and reactive power compensation instructions, which are used to coordinate power exchange between distributed power sources and the power grid and obtain command data from the power grid dispatch center;

[0110] The historical operation data is obtained from the historical database of the power grid and distributed power generation. The historical operation data includes past power data, power grid status data, fault records and maintenance records.

[0111] Through pre-established interfaces, the system communicates with the monitoring systems of distributed power sources (such as those for solar power plants, wind power plants, and energy storage systems) or supervisory control and data acquisition (SCADA) systems. This system obtains real-time output power data for each distributed power source from these systems. This data is crucial for understanding the real-time power generation capacity of distributed power sources. This data is organized into a real-time distributed power source power dataset, providing a foundation for subsequent analysis and simulation.

[0112] Real-time grid status data is extracted from the grid monitoring system. This data includes, but is not limited to, electrical parameters such as voltage, current, frequency, and power factor at each node. Monitoring line status includes information such as whether the line is overloaded or faulty. This information is crucial for assessing grid stability and security.

[0113] The collected topological information of the power grid includes the connection relationships and parameters of electrical components such as nodes, lines, transformers, and switchgear in the power grid.

[0114] The control strategy information collected for distributed power sources includes power control strategy (such as constant power control, maximum power point tracking control, etc.), voltage control strategy, and frequency control strategy. The control strategy determines the operation mode and response characteristics of distributed power sources in the power grid, and is crucial for optimizing the coordinated operation of distributed power sources and the power grid.

[0115] Communicate with the grid dispatch center to obtain dispatch instructions for distributed power sources. These instructions may include power dispatch instructions (specifying the generated power of distributed power sources), start and stop instructions (controlling the start and stop of distributed power sources), reactive power compensation instructions (adjusting the reactive power output of distributed power sources to improve the voltage quality of the grid), etc. Distributed power generation dispatch instructions are an important basis for coordinating power exchange between distributed power sources and the grid and maintaining stable grid operation.

[0116] Extract historical operating data from the power grid and distributed generation (DG) historical database. This data includes past power data, grid status data, fault records, and maintenance records. Historical data is valuable for analyzing the operating patterns of the power grid and DG, predicting future trends, and optimizing operational strategies.

[0117] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S102 includes:

[0118] Obtaining natural environmental data of the power grid's location, including meteorological data, geographic data, and environmental data. Meteorological data includes temperature, humidity, wind speed, wind direction, precipitation, and air pressure; geographic data includes topography, altitude, and soil type; and environmental data includes air quality and solar radiation intensity.

[0119] Preprocess the natural environment data and extract features from the preprocessed natural environment data. The obtained meteorological features are used to reflect the impact of the natural environment on the operation of the power grid. The meteorological features include daily average temperature features, maximum temperature features, minimum temperature features, average wind speed features, and precipitation features.

[0120] Use clustering algorithms to perform cluster analysis on the extracted features to obtain natural environment clustering results, and establish an association relationship between the natural environment clustering results and the corresponding time information;

[0121] Integrate natural environment data, natural environment clustering results and their association with time information into a structured knowledge base.

[0122] Collect natural environmental data about the grid's location, covering meteorological, geographical, and environmental aspects. Meteorological data includes temperature, humidity, wind speed, wind direction, precipitation, and air pressure. These data directly reflect local climatic conditions and have a direct impact on grid operation and the efficiency of distributed generation.

[0123] Geographic data, including topography, altitude, and soil type, determines the difficulty of grid construction and the installation conditions of distributed power generation (DGs). Environmental data, including air quality and solar radiation intensity, significantly impacts the aging of grid equipment and the efficiency of DGs (particularly solar power plants).

[0124] The collected natural environment data is cleaned to remove outliers, fill in missing values, and perform necessary data conversions to ensure data accuracy. Features are extracted from the preprocessed data. These features should reflect the impact of the natural environment on grid operation. For example, daily average temperature, maximum temperature, and minimum temperature characteristics can reflect the impact of temperature on grid equipment load and distributed power generation efficiency; mean wind speed characteristics can reflect the impact of wind energy on wind power station efficiency; and precipitation characteristics can reflect the impact of precipitation on grid equipment safety.

[0125] Cluster analysis is performed on the extracted features using clustering algorithms (such as K-means and DBSCAN). The goal of cluster analysis is to group time periods or locations with similar natural environmental characteristics to better understand and predict the impact of the natural environment on grid operation. Cluster analysis yields natural environment clustering results that reveal grid operation patterns and distributed generation characteristics under different natural environmental characteristics.

[0126] The natural environment clustering results are associated with corresponding time information (such as date and season). This association helps analyze the impact of temporal changes in the natural environment on grid operation and the efficiency of distributed generation. For example, the impact of seasonal changes in meteorological characteristics such as temperature and wind speed on grid load and distributed generation efficiency can be analyzed, providing a scientific basis for grid scheduling and operation.

[0127] Integrate natural environment data, natural environment clustering results, and their associations with temporal information into a structured knowledge base. This knowledge base should contain rich natural environment and power grid operation information, supporting data query, analysis, and mining. By building this knowledge base, we can achieve a deep understanding and efficient utilization of the relationship between the natural environment and power grid operation, providing strong support for the coordinated optimization of power grid operations.

[0128] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S103 includes:

[0129] Receive grid tasks injected by distributed power generation, parse the received grid task data, and extract basic task information, distributed power generation information, and grid constraint data;

[0130] The basic information of a task includes task ID, task name, planned execution time, and task priority;

[0131] Distributed power information includes the type, rated power, and access point location of the distributed power source;

[0132] Grid constraint data includes the grid’s voltage level, frequency stability requirements, and power factor limits;

[0133] Extracting task execution time information from the basic task information. The task execution time information includes the date and time period of the planned execution time. Using the task execution time information, search for the natural environment data clustering results corresponding to the task execution time in the power grid natural environment knowledge base. The matching process involves searching for natural environment data corresponding to a specific date, time period or season.

[0134] Extracting natural environment data that matches the task execution time from the power grid natural environment knowledge base. The natural environment data includes temperature, wind speed, precipitation, light information, and wind speed information during the period.

[0135] The acquired natural environment data is integrated with the grid mission data injected by distributed power generation, and the integrated data is used for subsequent grid simulation, optimization and control processes.

[0136] Receive grid tasks for distributed generation power injection from superior dispatchers or users. Parse the received grid task data and extract key information, including basic task information, distributed generation information, and grid constraint data.

[0137] Basic task information: includes task ID (unique identifier), task name, planned execution time (specific date and time period), and task priority.

[0138] Distributed power generation information: clearly specify the type of distributed power generation (such as solar power stations, wind power stations, energy storage systems, etc.), rated power, and the specific location where it is connected to the power grid.

[0139] Grid constraint data: Lists the constraints that the grid must comply with for operation, such as voltage levels, frequency stability requirements, and power factor limits.

[0140] Extract task execution time information: Extract the time information related to the task execution from the basic task information, that is, the specific date and time period of the planned execution time.

[0141] Matching natural environment data: Use the extracted task execution time information to search in the previously constructed power grid natural environment knowledge base.

[0142] The matching process involves finding the corresponding cluster of natural environmental data based on a specific date, time period, or season. This includes temperature, wind speed, precipitation, sunlight intensity (especially important for solar power plants), and wind speed information (especially important for wind power plants) during that period.

[0143] Extract and integrate natural environment data: Extract natural environment data that precisely matches the task execution time from the power grid natural environment knowledge base. This natural environment data is integrated with the grid task data from distributed generation power injection. The integrated dataset not only contains detailed task information but also incorporates the natural environment conditions that may be encountered during task execution.

[0144] Preparation for subsequent processing: The integrated data is used in subsequent grid simulation, optimization, and control processes. In the simulation phase, this data helps build a grid model that is closer to reality. In the optimization phase, it supports the search for solutions that maximize distributed generation utilization and grid efficiency while meeting grid constraints. In the control phase, it is used to adjust the output of distributed generation in real time to respond to changes in the natural environment and changes in grid demand.

[0145] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S104 includes:

[0146] Summarizes real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center command data, and historical operation data;

[0147] Preprocess the data and perform feature extraction on the preprocessed data to obtain the real-time power of the distributed power supply, the voltage of each node in the power grid, the current of each node in the power grid, the power flow of the line, and the control parameters of the distributed power supply;

[0148] The dataset is divided into a training set and a test set. The training set is used to train the random forest model, and the test set is used to evaluate the performance of the model.

[0149] Use the training set data to train the random forest model, receive and set the parameters of the random forest, which include the number of trees, maximum depth, and minimum number of sample splits, run the random forest algorithm, obtain a grid simulation model of distributed power injection, substitute the basic information of the grid task of distributed power injection into the grid simulation model of distributed power injection, the basic information of the grid task includes task time, distributed power type, and power data, run the grid simulation model of distributed power injection, and obtain the grid simulation results of distributed power injection, which include voltage changes at each node of the grid, power flow of the line, and power output data of the distributed power.

[0150] Summarize all the data collected in the previous steps, including real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center command data, and historical operation data.

[0151] The aggregated data is preprocessed, including data cleaning (removing outliers, filling missing values, etc.), data transformation (such as standardization and normalization), and data screening (selecting features useful for model training). Features are extracted from the preprocessed data. These features should reflect the impact of distributed power generation (DG) power injection on the grid. For example, the real-time power of DGs, the voltage and current at each grid node, the power flow of the lines, and the control parameters of DGs.

[0152] After preprocessing and feature extraction, the dataset is divided into a training set and a test set. The training set is used to train the random forest model, while the test set is used to evaluate model performance, such as precision and recall. The training set data is used to train the random forest model. Random forest is an ensemble learning method that improves model accuracy and stability by constructing multiple decision trees and combining their predictions. During training, you need to receive and set random forest parameters, such as the number of trees, maximum depth, and minimum number of sample splits. These parameters affect model complexity and performance. The random forest algorithm is run to generate a grid simulation model with distributed generation power injection.

[0153] Basic information about the distributed generation (DG) power injection task is fed into the trained grid simulation model. This information includes task time, DG type, and power data. The DG power injection grid simulation model is then run to simulate the impact of DG power injection on the grid. Simulation results include voltage changes at each grid node, power flow along the lines, and DG power output data. These results can be used to assess the impact of DG power injection on grid stability and efficiency, providing decision support for subsequent grid optimization and control.

[0154] Through the implementation of step S104, the present invention can use the random forest model to accurately simulate and predict the impact of distributed power generation power injection on the power grid, providing a powerful tool and method for the coordinated optimization of power grid operation. This data-driven approach can improve the flexibility of power grid scheduling, help achieve efficient utilization of distributed power generation and stable operation of the power grid.

[0155] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S105 includes:

[0156] Determine the optimization objectives based on the actual needs and constraints of the power grid. The optimization objectives include minimizing grid losses, stabilizing voltage, and maximizing the utilization of distributed power sources.

[0157] Receive and set the basic parameters of the genetic algorithm, including population size, genetic generations, crossover probability, and mutation probability;

[0158] The power injection scheme of the distributed power generation is represented as an individual in the genetic algorithm using real number coding. The individual includes the power setting value or control parameter of the distributed power generation, and the individual is used as the object of genetic algorithm optimization.

[0159] Initialize the population. According to the set population size, randomly generate a group of initial individuals to form the initial population. Each individual represents a distributed power injection scheme.

[0160] Using real-time power data of distributed generation, grid status data, and grid topology data, the fitness of each individual is evaluated through a grid simulation model. The fitness function should be designed according to the optimization objective.

[0161] According to the fitness evaluation results, individuals with higher fitness are selected as parents for crossover and mutation operations;

[0162] Perform a crossover operation on the selected parent individuals to generate new offspring individuals;

[0163] The crossover operation is performed by single-point crossover, double-point crossover or uniform crossover;

[0164] Perform mutation operations on offspring individuals by changing the gene values ​​in the individuals or introducing new genes;

[0165] Update the population, replace some or all of the parent individuals with the generated offspring individuals, update the population, and determine whether to continue iterating based on the set genetic generations or convergence conditions;

[0166] If the set genetic generation number is reached or the fitness meets the convergence condition, the iteration is stopped. After iterative optimization of the genetic algorithm, the individual with the highest fitness is obtained, that is, the optimized distributed power generation power injection scheme, which is used as the parameter of the optimized distributed power generation power injection power grid simulation model.

[0167] In step S105, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention uses a genetic algorithm to optimize the power injection scheme of the distributed power generation. The detailed process of this step is as follows:

[0168] Determine the optimization objectives. Based on the actual grid needs and constraints, clearly define the optimization objectives. These objectives typically include minimizing grid losses, ensuring voltage stability, and maximizing the utilization of distributed generation (DGs). These objectives are interrelated and require trade-offs during the optimization process.

[0169] Set Genetic Algorithm Parameters: Receive and set the basic parameters of the genetic algorithm, including population size (the number of individuals participating in the optimization simultaneously), generation number (the number of optimization iterations), crossover probability (the probability of a crossover between individuals), and mutation probability (the probability of an individual mutating). These parameters affect the performance and convergence speed of the genetic algorithm.

[0170] Encoded individuals: The power injection schemes of distributed generation are represented as individuals in the genetic algorithm using real number encoding. Each individual contains a set of power setpoints or control parameters of the distributed generation, which are the variables to be optimized.

[0171] Initialize the population: Based on the set population size, a group of initial individuals are randomly generated to form the initial population. Each individual represents a possible distributed power injection scheme.

[0172] Evaluating Fitness: Using real-time DG power data, grid status data, and grid topology data, the previously trained grid simulation model is used to evaluate the fitness of each individual. The fitness function should be designed based on the optimization objective. For example, the fitness function can be defined by comprehensively considering factors such as grid loss, voltage stability, and DG utilization.

[0173] Parent selection: Based on the fitness evaluation results, individuals with higher fitness are selected as parents for subsequent crossover and mutation operations. Selection strategies can use methods such as roulette wheel selection and tournament selection.

[0174] Crossover operation:

[0175] Perform a crossover operation on the selected parent individuals to generate new offspring individuals. The crossover operation can be performed through single-point crossover, double-point crossover, or uniform crossover to simulate the gene recombination process in biological evolution.

[0176] Mutation: Perform mutation operations on offspring individuals to increase population diversity and explore new solution spaces. Mutation can be achieved by changing the gene values ​​in individuals or introducing new genes.

[0177] Update the population: Replace some or all of the parent individuals with the generated offspring individuals to update the population. This step simulates the natural selection process in biological evolution. The decision to continue iteration is based on the set number of generations or convergence criteria. Iteration stops if the set number of generations is reached or if the fitness meets the convergence criteria.

[0178] Obtaining optimization results: After iterative optimization using the genetic algorithm, the individual with the highest fitness is obtained, which is the optimized distributed generation power injection plan. This plan is the best solution that achieves optimization goals (such as minimizing grid losses, ensuring voltage stability, and maximizing distributed generation utilization) while meeting grid constraints.

[0179] The optimization scheme is used as the parameter of the grid simulation model for the optimized distributed generation power injection, and is used for subsequent grid operation simulation, optimization and control.

[0180] By implementing step S105, the present invention can use the genetic algorithm to perform global optimization on the power injection scheme of the distributed power source, and find the best solution for achieving the optimization goal while satisfying the grid constraints.

[0181] Specifically, the grid coordinated optimization operation method for distributed power generation power injection according to the present invention, step S105 includes:

[0182] The distributed power generation power injection power grid simulation model is optimized by genetic algorithm, and the optimized model is obtained. The parameters of the optimized distributed power generation power injection power grid simulation model are set as the initial parameters of the distributed power generation power injection power grid control model;

[0183] Receive the grid task of distributed generation power injection from the grid dispatch center. The grid task should include basic information of the task, such as task time, distributed generation type and expected power injection amount;

[0184] Preprocessing the received power grid task data, substituting the preprocessed power grid task data into the optimized distributed power generation power injection power grid control model, and taking the distributed power generation power injection requirements and time constraints in the task as inputs of the model;

[0185] Executing a grid control model for distributed power generation injection, performing simulation calculations based on input task data and parameters of the grid control model for distributed power generation injection, wherein the grid control model for distributed power generation injection uses the current state and topology of the grid and the real-time power data of the distributed power generation to calculate a distributed power generation injection plan that meets grid constraints and task requirements;

[0186] Extracting real-time distributed power generation power injection grid control parameters from the output of the distributed power generation power injection grid control model, the real-time distributed power generation power injection grid control parameters including the power adjustment amount of the distributed power generation, the adjustment parameters of the control strategy and the time point of power injection;

[0187] Before applying the grid control parameters of the real-time distributed power generation power injection to the actual grid, the parameters of the grid simulation model of the optimized distributed power generation power injection are compared with the grid control parameters of the real-time distributed power generation power injection. If the grid constraints are met, the verification is successful. If the verification is successful, the grid control parameters of the real-time distributed power generation power injection are applied to the distributed power control system of the actual grid;

[0188] After applying the grid control parameters of the real-time distributed power generation power injection, the state of the grid and the power injection of the distributed power generation are continuously monitored. If they do not meet expectations, the grid control parameters of the real-time distributed power generation power injection are adjusted.

[0189] In step S105, the present invention describes in detail how to formulate and apply actual grid control parameters using the grid simulation model of distributed power generation power injection optimized by the genetic algorithm. The following is a detailed process of this step:

[0190] A genetic algorithm is used to optimize the grid simulation model for distributed generation power injection to find the optimal or near-optimal distributed generation power injection scheme. The optimized model parameters are set as the initial parameters of the grid control model for distributed generation power injection. These parameters, including the distributed generation power setpoints and control strategy parameters, serve as the basis for subsequent control decisions.

[0191] Receive grid tasks for distributed generation power injection from the grid dispatch center. Task information should include essential task elements, such as task execution time, the types of distributed generation involved, and the expected power injection amount. Preprocess the received grid task data to ensure accuracy and consistency. This may include data cleansing, format conversion, and verification. Substitute the preprocessed task data into the optimized grid control model for distributed generation power injection.

[0192] Execute the grid control model for distributed generation power injection, performing simulations based on the input mission data and model parameters. The model should comprehensively consider the current state of the grid, topology, real-time distributed generation power data, and mission requirements to calculate a distributed generation power injection plan that meets grid constraints and mission requirements. Grid control parameters for real-time distributed generation power injection are extracted from the model output. These parameters may include the power adjustment amount of the distributed generation, adjustment parameters of the control strategy, and the specific time of power injection.

[0193] Before applying the control parameters to the actual power grid, the rationality and feasibility of the control parameters are verified by comparing the optimized grid simulation model parameters with the real-time control parameters. If the verification is successful, meaning that the control parameters meet the grid constraints, they can be applied to the distributed power generation control system of the actual power grid. The verified grid control parameters for real-time distributed power generation power injection are then applied to the actual power grid. After applying the control parameters, the grid status and the power injection status of the distributed power generation are continuously monitored to ensure stable grid operation and that the distributed power generation is injecting power as expected.

[0194] If monitoring results indicate that the grid status or DG power injection is not as expected, the grid control parameters for real-time DG power injection should be adjusted promptly. If necessary, the grid control model can be rerun or other optimization methods can be used to find more appropriate control parameters, which can then be verified and applied again.

[0195] By implementing this step S105, the present invention can ensure that the grid control parameters for distributed power generation power injection are based on the results of the optimization model and meet the actual operation requirements of the grid.

[0196] The grid coordinated optimization operation method for distributed power generation power injection provided by the present invention effectively solves the problem that the output power of distributed power generation exhibits strong volatility and intermittency due to factors such as weather and operating conditions, which increases the difficulty of grid frequency and voltage control and makes traditional deterministic planning difficult to adapt to the construction of new power systems. This problem is solved by the following technical means:

[0197] The present invention acquires distributed power supply power data, grid status data, grid topology data, distributed power supply control strategy data, grid dispatch center instruction data, and historical operation data in real time. The real-time acquisition of these data provides a basis for subsequent analysis and optimization.

[0198] The system obtains natural environmental data (including meteorological, geographical, and environmental data) for the grid's location, performs cluster analysis, and associates the clustering results with time information to build a knowledge base for the grid's natural environment. This step enables the system to predict the impact of changes in the natural environment on grid operations and make proactive adjustments.

[0199] After receiving a power grid task, the system matches the task's natural environment knowledge base to obtain natural environment data corresponding to the task execution time. This data, when integrated with the task data, provides a more comprehensive input for subsequent power grid simulation and optimization. A random forest model is trained using real-time and historical data to generate a power grid simulation model. By substituting basic task information, the system simulates the power grid state after distributed generation power injection. The random forest model can handle high-dimensional data and complex nonlinear relationships, improving simulation accuracy.

[0200] The simulation results are compared with the grid constraint data. If the constraints are not met, a genetic algorithm is used to optimize the simulation model. By iteratively searching for the optimal solution, the genetic algorithm can handle the complex optimization problem of distributed generation power injection, improving the operational efficiency and stability of the grid. The simulated model optimized by the genetic algorithm serves as the grid control model. After receiving grid tasks, it outputs real-time control parameters. These parameters, including the power adjustment amount of the distributed generation, the adjustment parameters of the control strategy, and the timing of power injection, are directly applied to the distributed generation control system of the actual grid. After applying the control parameters, the grid status and the power injection status of the distributed generation are continuously monitored. If they do not meet expectations, the control parameters are adjusted promptly to ensure stable grid operation.

[0201] Through the above-mentioned technical means, the present invention realizes the precise control and optimization of the power injection of distributed power sources, effectively solves the grid control problem caused by the volatility and intermittency of the output power of distributed power sources, improves the operation efficiency, stability and safety of the grid, and adapts to the construction requirements of new power systems.

Claims

1. A method for coordinated optimization of power grid operation with distributed power generation injection, characterized in that: include: Step S101, obtaining real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data; Step S102: obtaining natural environment data of the location where the power grid is located, performing cluster analysis on the natural environment data of the location where the power grid is located to obtain a clustering result of the natural environment of the location where the power grid is located, and establishing a correlation between the clustering result of the natural environment of the location where the power grid is located and time information to obtain a knowledge base of the natural environment of the power grid; Step S103: receiving a grid task for distributed power injection, the grid task for distributed power injection including basic task information, distributed power information, and grid constraint data; matching the grid task for distributed power injection in a grid natural environment knowledge base to obtain natural environment data corresponding to the grid task for distributed power injection; Step S104: Using the real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center instruction data, and historical operation data to train a random forest model to obtain a grid simulation model for distributed power source power injection, and substituting the basic task information into the grid simulation model for distributed power source power injection to obtain a grid simulation result for distributed power source power injection; Step S105: Compare the grid simulation result of the distributed power generation power injection with the grid constraint data to obtain a comparison result of the grid simulation result of the distributed power generation power injection; if the grid simulation result of the distributed power generation power injection is outside the range of the grid constraint data, optimize the grid simulation model of the distributed power generation power injection using a genetic algorithm based on the real-time power data of the distributed power generation, the grid state data, and the grid topology data to obtain an optimized grid simulation model of the distributed power generation power injection; use the optimized grid simulation model of the distributed power generation power injection as the grid control model of the distributed power generation power injection; substitute the grid task of the distributed power generation power injection into the grid control model of the distributed power generation power injection to obtain real-time grid control parameters of the distributed power generation power injection; The step S103 includes: Receive grid tasks injected by distributed power generation, parse the received grid task data, and extract basic task information, distributed power generation information, and grid constraint data; The basic information of a task includes task ID, task name, planned execution time, and task priority; Distributed power information includes the type, rated power, and access point location of the distributed power source; Grid constraint data includes the grid’s voltage level, frequency stability requirements, and power factor limits; Extracting task execution time information from the basic task information. The task execution time information includes the date and time period of the planned execution time. Using the task execution time information, search for the natural environment data clustering results corresponding to the task execution time in the power grid natural environment knowledge base. The matching process involves searching for natural environment data corresponding to a specific date, time period or season. Extracting natural environment data that matches the task execution time from the power grid natural environment knowledge base. The natural environment data includes temperature, wind speed, precipitation, light information, and wind speed information during the period. Integrate the acquired natural environment data with the grid mission data injected by distributed generation power. The integrated data is used for subsequent grid simulation, optimization and control processes. The step S104 includes: Summarizes real-time power data of distributed power sources, grid status data, grid topology data, distributed power source control strategy data, grid dispatch center command data, and historical operation data; Preprocess the data and perform feature extraction on the preprocessed data to obtain the real-time power of the distributed power supply, the voltage of each node in the power grid, the current of each node in the power grid, the power flow of the line, and the control parameters of the distributed power supply; The dataset is divided into a training set and a test set. The training set is used to train the random forest model, and the test set is used to evaluate the performance of the model. Use the training set data to train the random forest model, receive and set the parameters of the random forest, which include the number of trees, maximum depth, and minimum number of sample splits, run the random forest algorithm, obtain a grid simulation model of distributed power injection, substitute the basic information of the grid task of distributed power injection into the grid simulation model of distributed power injection, the basic information of the grid task includes task time, distributed power type, and power data, run the grid simulation model of distributed power injection, and obtain the grid simulation results of distributed power injection, which include voltage changes at each node of the grid, power flow of the line, and power output data of the distributed power.

2. The method for coordinated optimization of power grid operation with distributed power generation injection according to claim 1, characterized in that: The step S101 includes: Through the interface with the monitoring system or data acquisition and monitoring control system of distributed power sources, the current output power data of each distributed power source is obtained in real time, and the real-time power data of distributed power sources is obtained. Distributed power sources include solar power stations, wind power stations and energy storage systems; Obtaining real-time grid status data from the grid monitoring system. The real-time grid status data includes the voltage, current, frequency, power factor of each node, grid load conditions, and line conditions. The line conditions include line overload and line fault conditions, and obtaining grid status data. The topological structure information of the power grid includes the connection relationship and parameters of the nodes, lines, transformers, and switchgear of the power grid; The control strategy information of the distributed power supply includes power control strategy, voltage control strategy and frequency control strategy. The power control strategy includes constant power control and maximum power point tracking control. Obtain dispatch instructions for distributed power sources from the power grid dispatch center, including power dispatch instructions, start and stop instructions, and reactive power compensation instructions, which are used to coordinate power exchange between distributed power sources and the power grid and obtain command data from the power grid dispatch center; The historical operation data is obtained from the historical database of the power grid and distributed power generation. The historical operation data includes past power data, power grid status data, fault records and maintenance records.

3. The method for coordinated optimization of power grid operation with distributed power generation injection according to claim 1, characterized in that: The step S102 includes: Obtaining natural environmental data of the power grid's location, including meteorological data, geographic data, and environmental data. Meteorological data includes temperature, humidity, wind speed, wind direction, precipitation, and air pressure; geographic data includes topography, altitude, and soil type; and environmental data includes air quality and solar radiation intensity. Preprocess the natural environment data and extract features from the preprocessed natural environment data. The obtained meteorological features are used to reflect the impact of the natural environment on the operation of the power grid. The meteorological features include daily average temperature features, maximum temperature features, minimum temperature features, average wind speed features, and precipitation features. Use clustering algorithms to perform cluster analysis on the extracted features to obtain natural environment clustering results, and establish an association relationship between the natural environment clustering results and the corresponding time information; Integrate natural environment data, natural environment clustering results and their association with time information into a structured knowledge base.

4. The method for coordinated optimization of power grid operation with distributed power generation injection according to claim 1, wherein: The step S105 includes: Determine the optimization objectives based on the actual needs and constraints of the power grid. The optimization objectives include minimizing grid losses, stabilizing voltage, and maximizing the utilization of distributed power sources. Receive and set the basic parameters of the genetic algorithm, including population size, genetic generations, crossover probability, and mutation probability; The power injection scheme of the distributed power generation is represented as an individual in the genetic algorithm using real number coding. The individual includes the power setting value or control parameter of the distributed power generation, and the individual is used as the object of genetic algorithm optimization. Initialize the population. According to the set population size, randomly generate a group of initial individuals to form the initial population. Each individual represents a distributed power injection scheme. Using real-time power data of distributed generation, grid status data, and grid topology data, the fitness of each individual is evaluated through a grid simulation model. The fitness function should be designed according to the optimization objective. According to the fitness evaluation results, individuals with higher fitness are selected as parents for crossover and mutation operations; Perform a crossover operation on the selected parent individuals to generate new offspring individuals; The crossover operation is performed by single-point crossover, double-point crossover or uniform crossover; Perform mutation operations on offspring individuals by changing the gene values ​​in the individuals or introducing new genes; Update the population, replace some or all of the parent individuals with the generated offspring individuals, update the population, and determine whether to continue iterating based on the set genetic generations or convergence conditions; If the set genetic generation number is reached or the fitness meets the convergence condition, the iteration is stopped. After iterative optimization of the genetic algorithm, the individual with the highest fitness is obtained, that is, the optimized distributed power generation power injection scheme, which is used as the parameter of the optimized distributed power generation power injection power grid simulation model.

5. The method for coordinated optimization of power grid operation with distributed power generation injection according to claim 4, characterized in that: The step S105 includes: The distributed power generation power injection power grid simulation model is optimized by genetic algorithm, and the optimized model is obtained. The parameters of the optimized distributed power generation power injection power grid simulation model are set as the initial parameters of the distributed power generation power injection power grid control model; Receive the grid task of distributed generation power injection from the grid dispatch center. The grid task should include basic information of the task, including task time, distributed generation type and expected power injection amount; Preprocessing the received power grid task data, substituting the preprocessed power grid task data into the optimized distributed power generation power injection power grid control model, and taking the distributed power generation power injection requirements and time constraints in the task as inputs of the model; Executing a grid control model for distributed power generation injection, performing simulation calculations based on input task data and parameters of the grid control model for distributed power generation injection, wherein the grid control model for distributed power generation injection uses the current state and topology of the grid and the real-time power data of the distributed power generation to calculate a distributed power generation injection plan that meets grid constraints and task requirements; Extracting real-time distributed power generation power injection grid control parameters from the output of the distributed power generation power injection grid control model, the real-time distributed power generation power injection grid control parameters including the power adjustment amount of the distributed power generation, the adjustment parameters of the control strategy and the time point of power injection; Before applying the grid control parameters of the real-time distributed power generation power injection to the actual grid, the parameters of the grid simulation model of the optimized distributed power generation power injection are compared with the grid control parameters of the real-time distributed power generation power injection. If the grid constraints are met, the verification is successful. If the verification is successful, the grid control parameters of the real-time distributed power generation power injection are applied to the distributed power control system of the actual grid; After applying the grid control parameters of the real-time distributed power generation power injection, the state of the grid and the power injection of the distributed power generation are continuously monitored. If they do not meet expectations, the grid control parameters of the real-time distributed power generation power injection are adjusted.

Images