Power grid vulnerability multi-objective optimization analysis method based on evolutionary algorithm

Abstract

The invention discloses a power grid vulnerability multi-objective optimization analysis method based on an evolutionary algorithm, and relates to the technical field of power grid dispatching and optimization, and the method comprises the following steps: obtaining historical load data of a power grid and related external disturbance information, which are used for accurately describing a fluctuation rule of a power grid load; by combining the historical load data and the external disturbance information, the system significantly improves the power grid load prediction precision, reduces the influence of prediction errors on scheduling, optimizes the power grid resource configuration, avoids overload and equipment faults, and improves the stability, reliability and operation cost effectiveness of the power grid. Meanwhile, a multi-objective optimization model and an evolutionary algorithm are introduced, and the system achieves comprehensive optimization in the aspects of load distribution, power grid stability, cost control, operation efficiency and the like. By flexibly adjusting the scheduling strategy, the power grid can cope with emergencies and high load conditions, the system efficiency is improved, the cost is reduced, and efficient and stable operation is kept.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching and optimization, and in particular to a multi-objective optimization analysis method for power grid vulnerability based on an evolutionary algorithm. Background Art

[0002] "Multi-objective optimization analysis of power grid vulnerability based on evolutionary algorithms" refers to the use of evolutionary algorithms (such as genetic algorithms, differential evolution, etc.) to analyze and optimize the problem of power grid vulnerability, where "vulnerability" refers to the weaknesses of the stability, reliability and recovery ability of the system when the power grid encounters various faults, external disturbances or unpredictable events. This analysis method not only focuses on how to improve the security of the power grid, but also involves the balance of multiple optimization objectives, such as optimizing the load scheduling of the power grid, reducing power loss, improving the system fault tolerance, and reducing operating costs. Since these goals are usually conflicting, multi-objective optimization techniques are needed to find an optimal or compromise solution. Evolutionary algorithms have strong global search capabilities in complex and changeable problems, so they can provide efficient and accurate optimization results in power grid vulnerability analysis, thereby helping the power system to operate more stably in the face of various disturbances.

[0003] The prior art has the following deficiencies:

[0004] In the multi-objective optimization analysis of power grid vulnerability based on evolutionary algorithms, the mismatch between power grid load demand forecast and optimization target may lead to serious consequences. Power grid load usually fluctuates significantly, especially during peak hours or when emergencies occur, the actual load may deviate greatly from the predicted load in the optimization model. If this prediction error is not fully considered during the optimization process, the optimization target may not match the actual operating conditions, which may lead to power grid overload or system instability. Especially in extreme weather or holidays, load fluctuations are particularly severe. Ignoring load prediction errors may cause the power grid to be unable to dispatch in time when facing sudden load changes, which may eventually lead to large-scale power outages or equipment damage, seriously affecting the safety and reliability of the power grid.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-objective optimization analysis method for power grid vulnerability based on an evolutionary algorithm. By combining historical load data and external disturbance information, the system significantly improves the accuracy of power grid load prediction, reduces the impact of prediction errors on scheduling, optimizes power grid resource allocation, avoids overload and equipment failure, and improves the stability, reliability and cost-effectiveness of the power grid. At the same time, by introducing a multi-objective optimization model and an evolutionary algorithm, the system achieves comprehensive optimization in terms of load distribution, power grid stability, cost control and operating efficiency. By flexibly adjusting the scheduling strategy, the power grid can respond to emergencies and high load conditions, improve system efficiency, reduce costs, and maintain efficient and stable operation to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a multi-objective optimization analysis method for power grid vulnerability based on an evolutionary algorithm, comprising the following steps:

[0008] Obtain historical load data of the power grid and related external disturbance information to accurately describe the fluctuation pattern of the power grid load;

[0009] Based on the historical load data of the power grid and combined with external disturbance information, a prediction model for power grid load demand is established, and a machine learning model is used to optimize the load prediction accuracy and reduce the prediction error;

[0010] According to the forecast results of power grid load demand, a multi-objective optimization model is established, and the weights of each optimization objective are defined;

[0011] Use evolutionary algorithms to perform multi-objective optimization analysis to find the optimal solution to minimize grid vulnerability according to the set optimization objectives, taking into account the volatility of grid load and actual operating conditions;

[0012] According to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid.

[0013] Preferably, the specific steps of obtaining the historical load data of the power grid and the relevant external disturbance information for accurately describing the fluctuation law of the power grid load are as follows:

[0014] First, historical load data of the power grid is collected and load changes in different time periods are analyzed to identify periodic fluctuations and abnormal conditions of the power grid load;

[0015] Obtain data on external factors that affect grid load fluctuations to help analyze load change patterns;

[0016] Standardize and pre-process the data collected from different sources to solve the problems of inconsistent data formats and missing data and ensure the validity of the data;

[0017] Through visualization, the data is preliminarily analyzed to identify the fluctuation trend of power grid load and the impact of external disturbances, providing a reference for the load forecasting model.

[0018] Preferably, based on the historical load data of the power grid and in combination with external disturbance information, a prediction model for power grid load demand is established, and the load prediction accuracy is optimized using a machine learning model to reduce the prediction error. The specific steps are as follows:

[0019] By extracting key features from historical load data and external disturbance information and performing standardization, valuable data support is provided for the load forecasting model;

[0020] According to the time series of load data, a machine learning algorithm is selected to establish a prediction model to capture the long-term dependency of load fluctuations;

[0021] Reduce prediction errors and improve generalization ability by dividing the training set and test set, adjusting model parameters, and using cross-validation and regularization methods for model training and optimization;

[0022] By comparing actual and predicted results, calculating error indicators and performing error analysis, model deviations are identified and further optimized to ensure the accuracy and reliability of load forecasting.

[0023] Preferably, according to the prediction results of the power grid load demand, the specific steps of establishing a multi-objective optimization model and defining the weights of each optimization objective are as follows:

[0024] Define multiple goals for grid optimization and clarify the role and significance of each goal;

[0025] Transform the power grid optimization objectives into mathematical functions and build models based on the constraints in actual operation;

[0026] Assign weights to each optimization target based on actual demand and grid operation scenarios to balance the priority of each target;

[0027] The operation constraints of the power grid are introduced, and the multi-objective optimization problem is solved by using evolutionary algorithms and other methods to ensure that the optimization results are practical.

[0028] Preferably, an evolutionary algorithm is used to perform multi-objective optimization analysis to find the optimal solution for minimizing grid vulnerability according to the set optimization goal. The specific steps considering the volatility of grid load and actual operating conditions are as follows:

[0029] In the evolutionary algorithm, a population is first initialized, in which each individual represents a power grid dispatching scheme, and an objective function is set to measure the pros and cons of each dispatching scheme. For power grid vulnerability analysis, the multi-objective function is expressed as follows:

[0030] F(x)=(f1(x), f2(x), f3(x), f4(x))

[0031] , where F(x) is the objective function, x represents a decision variable vector of a solution, f1(x) is grid stability, f2(x) is load balance, f3(x) is cost optimization, and f4(x) is operating efficiency;

[0032] After the population is initialized, the evolutionary algorithm selects individuals with high fitness to enter the next generation through selection operations. The fitness function is used to evaluate the quality of each individual. The weighted sum optimization strategy of each objective function is adopted. Assuming the weighted sum form is used, the fitness function is expressed as follows:

[0033] Fitness(x)=w1f1(x)+w2f2(x)+w3f3(x)+w4f4(x)

[0034] , where Fitness(x) is the fitness function, w1, w2, w3 and w4 are the weights of grid stability f1(x), load balance f2(x), cost optimization f3(x) and operation efficiency f4(x), respectively, reflecting the importance of each objective in the optimization;

[0035] In crossover and mutation, the genetic operation of the evolutionary algorithm is used to generate new individuals. Suppose the selected parent individuals are x1 and x2, and the crossover operation generates a new offspring individual x3. The formula is as follows:

[0036] x3=α·x1+(1-α)·x2

[0037] , where α∈[0,1] is the crossover weight parameter, which controls the degree of influence of the parent individual on the offspring individual. The mutation operation introduces diversity by randomly adjusting the decision variables of the new offspring individual x3 generated by the crossover operation. The mutation operation is as follows:

[0038]

[0039] , where δ is the variation amplitude, r is the random vector, It is the new individual after the mutation operation;

[0040] In each generation of the evolutionary algorithm, by evaluating the fitness of individuals in the population, individuals with low fitness are continuously eliminated, and individuals with high fitness are retained, so as to gradually improve the quality of the entire population. After multiple generations of iterations, the optimal solution is finally obtained. The optimal solution formula is as follows:

[0041]

[0042] , where x opt is the optimal solution, X tis the population set of generation t, Represents the population set X in the tth generation t In the above example, find the individual corresponding to the maximum value of the fitness function Fitness(x).

[0043] Preferably, according to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid. The specific steps are as follows:

[0044] According to the optimal solution obtained by the evolutionary algorithm, the load distribution scheme of each node in the power grid is first calculated, and the load is adjusted according to the optimal solution to ensure that the load distribution of each node in the power grid is reasonable. The calculation expression is as follows:

[0045]

[0046] , where L i is the load distribution value of the ith grid node, P i is the predicted load demand of the ith grid node, C i is the capacity factor of the ith grid node, P j is the predicted load demand of the jth grid node, C j is the capacity factor of the jth grid node, N is the total number of nodes in the grid, L max is the maximum load capacity of the entire power grid;

[0047] In the actual operation of the power grid, load fluctuations and emergencies will lead to deviations in load distribution. At this time, the real-time monitoring system continuously detects the load status of each node in the power grid, and dynamically corrects the load distribution plan through real-time data to avoid overload and equipment damage. The calculation expression is as follows:

[0048] ΔL i =L i -L actual，i

[0049] , where ΔL i is the load adjustment value of the ith grid node, i.e., load deviation, L actual，i is the actual load value of the ith grid node;

[0050] After correcting the load adjustment of each grid node, the grid dispatching strategy is calculated based on the adjustment. The calculation expression is as follows:

[0051]

[0052] , where S i is the dispatching strategy of the ith grid node, D ijis the transmission limitation coefficient between the i-th grid node and the j-th grid node;

[0053] After load distribution adjustment and dispatch strategy optimization, the next step is to optimize the operation efficiency of the power grid in real time. The optimization goal is to minimize the operation cost of the power grid while ensuring the efficiency and reliability of the power grid in operation. The calculation expression is as follows:

[0054]

[0055] , where E opt is the optimized grid operation efficiency, R i is the power loss coefficient of the ith grid node, ω i is the load efficiency factor of the ith grid node, β i is the weight coefficient of the dispatching strategy of the ith grid node.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] By combining historical load data and external disturbance information, the system can significantly improve the accuracy of power grid load forecasting, thereby reducing the impact of load forecasting errors on scheduling. Accurate load forecasting not only improves the scientificity and efficiency of power grid scheduling, but also helps the power grid identify potential load fluctuations in advance to avoid overload and equipment failure. By more efficiently scheduling power grid resources, ensuring the reasonable allocation of power generation capacity during peak load periods, reducing energy waste, and reducing the risk of system overload, the stability and reliability of the power grid are improved. In addition, accurate load forecasting also optimizes the operating cost of the power grid, ensuring more economical power production and operation.

[0058] With the help of multi-objective optimization models and the introduction of evolutionary algorithms, the present invention has comprehensively optimized the power grid in multiple dimensions such as load distribution, power grid stability, cost control and operating efficiency. The system dynamically adjusts the weights of the optimization objectives and flexibly adjusts the scheduling strategy according to real-time load demand and external disturbance factors to ensure the stable operation of the power grid under various complex situations. By optimizing equipment scheduling, reducing energy loss and improving system efficiency, the power grid can maintain efficient and stable operation when responding to emergencies and high loads, which not only reduces operating costs, but also improves the reliability and operating efficiency of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0060] Figure 1 The present invention is a flow chart of a method for multi-objective optimization analysis of power grid vulnerability based on an evolutionary algorithm. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0062] The present invention provides Figure 1 The multi-objective optimization analysis method of power grid vulnerability based on evolutionary algorithm shown in the figure includes the following steps:

[0063] Obtain historical load data of the power grid and related external disturbance information, including meteorological data, holiday information, etc., to accurately describe the fluctuation pattern of power grid load;

[0064] The specific steps for obtaining the historical load data of the power grid and related external disturbance information, including meteorological data, holiday information, etc., to accurately describe the fluctuation law of the power grid load are as follows:

[0065] First, historical load data of the power grid is collected and load changes in different time periods are analyzed to identify periodic fluctuations and abnormal conditions of the power grid load;

[0066] First, it is necessary to collect historical load data of the power grid. These data are usually provided by power companies or related power grid operating units, and include load changes in different time periods of the power grid. The time span of the data can be determined according to specific needs. Generally, a longer time span can help better analyze the load fluctuation pattern of the power grid. Historical load data usually includes the daily average, maximum, minimum value of the load, and the load distribution in different areas of the power grid. By analyzing these historical data, the periodic changes, daily fluctuation patterns, and abnormal load peaks of the power grid load can be identified, providing basic data support for the subsequent load demand forecasting model.

[0067] Obtain data on external factors that affect grid load fluctuations, such as weather information, holidays, and emergencies, to help analyze load change patterns;

[0068] The impact of external disturbance information on grid load fluctuations cannot be ignored, especially weather changes, holidays, man-made events (such as large-scale events or emergencies), etc., which can cause abnormal fluctuations in grid load. Therefore, it is very important to obtain external disturbance information related to load fluctuations. Meteorological data is an important part of it, including temperature, precipitation, wind speed, humidity, etc., which directly affect the power demand of the grid, especially in extreme weather in winter or summer, when load fluctuations may be more severe. In addition, the electricity consumption pattern on holidays is often different from that on normal working days, so holiday information should also be included in the scope of data collection. For the analysis of grid load, the historical records of external disturbances should be combined to identify their influence on grid load, so as to better provide reference for grid load forecasting.

[0069] Standardize and pre-process the data collected from different sources to solve the problems of inconsistent data formats and missing data and ensure the validity of the data;

[0070] After collecting the historical load data of the power grid and the external disturbance information, these data need to be sorted and preprocessed. Since the data formats and time dimensions of different data sources may differ, the data must be standardized to ensure that various types of data can be effectively compared and analyzed. For example, align the meteorological data and holiday data with the power grid load data in time to ensure that all data are in the same time frame for cross-analysis. At the same time, filling in missing data is also a key step. Commonly used methods include interpolation and mean filling. In addition, outliers in the data need to be screened to remove invalid data that may be caused by equipment failure or collection errors to ensure that the final data quality can accurately reflect the true fluctuation characteristics of the power grid load.

[0071] Conduct preliminary data analysis through visualization to identify the fluctuation trend of power grid load and the impact of external disturbances, and provide reference for load forecasting models;

[0072] After completing data collection and preprocessing, the next step is to visualize and conduct preliminary analysis of the data. The purpose of this process is to intuitively identify the fluctuation trend of the power grid load and the impact of external disturbances on the load through charts or statistical indicators. For example, by drawing a correlation graph between the power grid load and meteorological data such as temperature and precipitation, the impact of weather changes on the load can be intuitively seen; at the same time, the impact of holidays on load fluctuations can also be analyzed by comparing the load differences between holidays and non-holidays. This process can not only help researchers deeply understand the laws of power grid load fluctuations, but also establish a clear background for subsequent load forecasting models, ensuring that these key factors can be taken into account when establishing load forecasting models to improve the accuracy of forecasts.

[0073] Based on the historical load data of the power grid and combined with external disturbance information, a prediction model for power grid load demand is established, and a machine learning model is used to optimize the load prediction accuracy and reduce the prediction error;

[0074] Based on the historical load data of the power grid and combined with external disturbance information, a prediction model for power grid load demand is established. The specific steps to optimize the load prediction accuracy and reduce the prediction error using the machine learning model are as follows:

[0075] By extracting key features from historical load data and external disturbance information and performing standardization, valuable data support is provided for the load forecasting model;

[0076] After completing the collection and preprocessing of the historical load data of the power grid and the external disturbance information, the next step is to establish a load demand prediction model based on these data. First, it is necessary to select and construct appropriate feature variables. The load fluctuation of the power grid is not only related to the historical load data, but also affected by external factors such as meteorological conditions (such as temperature and humidity) and holidays. Therefore, the key to feature engineering is to extract features such as periodicity, trend, and volatility from the historical load data, and input meteorological data, holidays, etc. into the model as additional features. The selection of these features can help capture the laws of power grid load changes and provide more valuable information for subsequent load forecasting. Through appropriate feature selection and data transformation (such as normalization and standardization), the model can better adapt to the complexity of load fluctuations.

[0077] According to the time series of load data, a machine learning algorithm (such as LSTM) is selected to establish a prediction model to capture the long-term dependency of load fluctuations;

[0078] After feature engineering is completed, the next step is to build a prediction model. According to the complexity of power grid load demand, machine learning algorithms are used to build accurate load forecasting models. Common machine learning models include decision trees, random forests, support vector machines (SVM), long short-term memory networks (LSTM), etc. Since power grid load data is time-series and is affected by multiple factors, LSTM (Long Short-Term Memory Network) is particularly suitable for processing such time-series data and can capture long-term dependencies in the data. Based on the collected power grid load data, meteorological data, and holiday information, the LSTM model can be used to predict future loads. LSTM can better handle the time series characteristics in load data, identify short-term and long-term load fluctuation trends, and reduce the overfitting or underfitting problems that may be encountered in traditional prediction methods.

[0079] Reduce prediction errors and improve generalization ability by dividing the training set and test set, adjusting model parameters, and using cross-validation and regularization methods for model training and optimization;

[0080] After selecting a suitable machine learning algorithm, model training and optimization are required. First, the data set is divided into a training set and a test set to ensure that the model can generalize well on unknown data. During the training process, the model will minimize the prediction error by continuously adjusting parameters (such as learning rate, number of neural network layers, etc.), thereby optimizing the prediction accuracy. In order to avoid overfitting, cross-validation methods (such as K-fold cross-validation) are usually used to evaluate the effect of the model. In addition, regularization methods can also be used to control the complexity of the model and further improve the stability of the model. By training and optimizing the model, it can be ensured that it can accurately predict the future load demand of the power grid and reduce the prediction error in the face of different load demand fluctuations.

[0081] By comparing actual and predicted results, calculating error indicators and performing error analysis, model deviations can be identified and further optimized to ensure the accuracy and reliability of load forecasting;

[0082] After the model training is completed, the prediction effect of the model needs to be evaluated. By comparing the actual load data with the model prediction results, the error indicators (such as mean square error MSE, mean absolute error MAE, R 2 Values, etc.) are used to measure the prediction accuracy of the model. In addition, error analysis is performed for different external disturbance factors (such as meteorological changes, holidays, etc.) to identify possible prediction deviations of the model under specific circumstances. For example, if certain extreme weather conditions cause large load fluctuations and the model prediction has obvious errors, these situations need to be specially handled. Through error analysis, the model can be further optimized, feature selection can be improved, or model parameters can be adjusted to ensure that the model can maintain a high prediction accuracy under various operating environments, thereby effectively reducing the error of load forecasting.

[0083] According to the forecast results of power grid load demand, a multi-objective optimization model is established, which includes the goals of power grid stability, load balance, cost optimization, operation efficiency, etc., and the weights of various optimization goals are defined;

[0084] According to the prediction results of power grid load demand, a multi-objective optimization model is established, which includes the goals of power grid stability, load balance, cost optimization, operation efficiency, etc., and the specific steps of defining the weights of each optimization goal are as follows:

[0085] Define multiple goals of grid optimization, such as stability, load balancing, cost optimization, and operational efficiency, and clarify the role and significance of each goal;

[0086] After establishing the load forecasting model based on the forecast results of the power grid load demand, the next step is to define the multi-objective optimization model. Multi-objective optimization problems usually involve multiple conflicting objectives, which need to be set according to the operation requirements of the power grid. For the optimization of the power grid, common goals include: grid stability (ensuring the normal operation of the power grid under high load and fault conditions), load balance (ensuring the balanced distribution of loads in different areas and time periods), cost optimization (minimizing the operation and maintenance costs of the power grid), and operating efficiency (improving the energy utilization efficiency of the power grid and reducing losses). When setting these goals, it is necessary to take into account the relationship between them, such as load balance may affect cost optimization, and stability optimization may lead to a certain cost increase. Therefore, it is crucial to clarify the specific meaning and role of each goal, which provides a basis for the subsequent optimization model design.

[0087] Transform the power grid optimization objectives into mathematical functions and build models based on the constraints in actual operation;

[0088] In order to achieve multi-objective optimization, the above objectives need to be converted into mathematical expressions. These objective functions are usually functions based on the state of the power grid. For example, the grid stability objective can be measured by the safety margin of the power grid (such as minimum load, maximum load and other indicators); the load balance objective can be expressed by the sum of squares of load differences in each region; cost optimization can be modeled by optimizing power production costs, transmission and distribution costs, and system losses; operating efficiency can be expressed by minimizing energy losses and optimizing scheduling. The construction of each objective function must take into account the actual situation in the operation of the power grid, such as constraints such as the load limit of the power grid, transmission capacity, and maximum load of the equipment. By mathematically modeling these objectives, it is ensured that they can be quantified and reasonably weighed in the optimization process.

[0089] Assign weights to each optimization target based on actual demand and grid operation scenarios to balance the priority of each target;

[0090] In multi-objective optimization, since there may be conflicts between various objectives, the weight of each objective must be defined to determine the importance of each objective in the optimization process. The definition of weights is usually determined by expert experience or data-driven methods. For example, the stability of the power grid may be more important than cost optimization in some application scenarios, especially in extreme weather or fault conditions, maintaining the stability of the power grid may need to be given priority. In daily operation, cost optimization and operating efficiency may be more critical, so it is necessary to assign different weights to each objective based on actual conditions. The setting of weights can be carried out through the analytic hierarchy process (AHP), minimization method, etc., and historical data analysis can also be used to determine which objectives have a greater impact on the operation of the power grid. Reasonable weight setting helps ensure that the optimization process can balance various objectives and obtain an optimal solution with practical application value.

[0091] Introducing the operation constraints of the power grid, using evolutionary algorithms and other methods to solve multi-objective optimization problems, ensuring that the optimization results are practical and feasible;

[0092] In the multi-objective optimization model, in addition to the objective function and weights, it is also necessary to introduce constraints on the operation of the power grid to ensure that the optimization results are feasible in practical applications. Common constraints include load limits of the power grid (maximum and minimum loads of each node), equipment capacity constraints (such as the maximum transmission capacity of transformers and transmission lines), and safety constraints (such as the recovery capacity of the power grid in the event of a fault). These constraints ensure that the optimization scheme is not only the optimal solution in theory, but also executable in actual operation. The solution of the optimization problem can be carried out by a variety of methods, such as genetic algorithms, particle swarm optimization (PSO) and other evolutionary algorithms, which can effectively explore the multi-objective optimization space and find compromise solutions between multiple objectives. By solving the optimization model, a set of power grid operation scheduling schemes can be obtained, which can achieve a good balance between different objectives and provide effective guidance in actual operation.

[0093] Use evolutionary algorithms to perform multi-objective optimization analysis to find the optimal solution to minimize grid vulnerability according to the set optimization objectives, taking into account the volatility of grid load and actual operating conditions;

[0094] The evolutionary algorithm is used to perform multi-objective optimization analysis. According to the set optimization objectives, the optimal solution to minimize the vulnerability of the power grid is found. The specific steps considering the volatility of the power grid load and the actual operating conditions are as follows:

[0095] In the evolutionary algorithm, a population is first initialized, in which each individual represents a power grid dispatching scheme. The code of each individual is usually a vector containing parameters such as load distribution and operation strategy of power grid equipment. The objective function is set to measure the pros and cons of each dispatching scheme. For power grid vulnerability analysis, the multi-objective function is expressed as follows:

[0096] F(x)=(f1(x), f2(x), f3(x), f4(x))

[0097] , where F(x) is the objective function, x represents a solution decision variable vector (such as equipment scheduling, load distribution, etc.), f1(x) is grid stability, f2(x) is load balance, f3(x) is cost optimization, and f4(x) is operating efficiency;

[0098] Each objective function reflects different requirements in power grid dispatching. Each x element in the objective function (such as the output power of the generator, the load of the transmission line, etc.) needs to be continuously adjusted through the optimization process to ensure that the final optimization solution meets the actual operating conditions and target requirements of the power grid.

[0099] After the population is initialized, the evolutionary algorithm selects individuals with high fitness to enter the next generation through selection operations. The fitness function is used to evaluate the quality of each individual. The weighted sum optimization strategy of each objective function is adopted. Assuming the weighted sum form is used, the fitness function is expressed as follows:

[0100] Fitness(x)=w1f1(x)+w2f2(x)+w3f3(x)+w4f4(x)

[0101] , where Fitness(x) is the fitness function, w1, w2, w3 and w4 are the weights of grid stability f1(x), load balance f2(x), cost optimization f3(x) and operation efficiency f4(x), respectively, reflecting the importance of each objective in the optimization;

[0102] The value of each objective function f i (x) will change continuously according to the actual operating conditions of the power grid during the optimization process, and the selection of weights is determined by the actual needs of the power grid. For example, in some cases, the stability of the power grid (f1) may be more important than cost optimization (f3). Through fitness evaluation, individuals with higher fitness are selected for crossover and mutation operations to push the population closer to the optimal solution.

[0103] In crossover and mutation, the genetic operation of the evolutionary algorithm is used to generate new individuals. Suppose the selected parent individuals are x1 and x2, and the crossover operation generates a new offspring individual x3. The formula is as follows:

[0104] x3=α·x1+(1-α)·x2

[0105] , where α∈[0,1] is the crossover weight parameter, which controls the degree of influence of the parent individual on the offspring individual. The mutation operation introduces diversity by randomly adjusting the decision variables of the new offspring individual x3 generated by the crossover operation. The mutation operation is as follows:

[0106]

[0107] , where δ is the variation amplitude, r is a random vector, indicating the randomness in the variation process, is the new individual after the mutation operation, which means the result of mutation of the individual x3 generated after crossover;

[0108] The above steps can improve the global search capability of the algorithm by generating new individuals and exploring a wider solution space, helping to find the optimal solution that minimizes the vulnerability of the power grid.

[0109] In each generation of the evolutionary algorithm, by evaluating the fitness of individuals in the population, individuals with low fitness are continuously eliminated, and individuals with high fitness are retained, so as to gradually improve the quality of the entire population. After multiple generations of iterations, the optimal solution is finally obtained. The optimal solution formula is as follows:

[0110]

[0111] , where x opt is the optimal solution, which means the optimal solution found in the tth generation, that is, the grid dispatching scheme or configuration that best meets the multi-objective optimization conditions, X t is the population set of generation t, Represents the population set X in the tth generation t In the above example, find the individual corresponding to the maximum value of the fitness function Fitness(x), that is, find the individual with the highest fitness and use it as the optimal solution x opt .

[0112] Optimal solution x opt It represents the vulnerability minimization scheme of the power grid in the multi-objective optimization process, which can simultaneously meet multiple objectives such as power grid stability, load balance, cost optimization and operation efficiency. As the number of generations increases, the evolutionary algorithm continuously approaches the global optimal solution through operations such as crossover, mutation and selection, and finally achieves the optimization goal of power grid vulnerability analysis.

[0113] According to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid;

[0114] According to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid. The specific steps are as follows:

[0115] According to the optimal solution obtained by the evolutionary algorithm, the load distribution scheme of each node of the power grid (such as substations, transmission lines, etc.) is first calculated. The optimal solution provided by the evolutionary algorithm usually includes the expected distribution result of the power grid load and the load capacity of each area of ​​the power grid. The load is adjusted according to the optimal solution to ensure that the load distribution of each node of the power grid is reasonable. The calculation expression is as follows:

[0116]

[0117] , where L i is the load distribution value of the ith grid node, P i is the predicted load demand of the ith grid node (derived from the prediction model), C i is the capacity factor of the ith grid node, P j is the predicted load demand of the jth grid node, C j is the capacity factor of the jth grid node, N is the total number of nodes in the grid, L max is the maximum load capacity of the entire power grid;

[0118] This step performs weighted calculation based on the node's load demand and equipment capacity factor to ensure that the load distribution meets the maximum capacity constraint of the power grid.

[0119] In the actual operation of the power grid, load fluctuations and emergencies will lead to deviations in load distribution. At this time, the load status of each node in the power grid is continuously detected through a real-time monitoring system (such as a SCADA system), and the load distribution plan is dynamically corrected through real-time data to avoid overload and equipment damage. The calculation expression is as follows:

[0120] ΔL i =L i -L actual，i

[0121] , where ΔL i is the load adjustment value of the ith grid node, i.e., load deviation, L actual，i is the actual load value of the ith grid node, obtained through real-time monitoring data;

[0122] This step calculates the deviation value of the node load to determine whether the load distribution needs to be adjusted, and dynamically adjusts the grid dispatching strategy according to the actual load situation.

[0123] After the load adjustment of each grid node is corrected, the grid dispatch strategy is calculated based on the adjustment. This process not only considers the balance of node load, but also factors such as grid equipment capacity, transmission restrictions and economic dispatch to ensure that the operation of the grid is both safe and efficient. The calculation expression is as follows:

[0124]

[0125] , where S i is the dispatching strategy of the ith grid node, reflecting the dispatching demand of the node, D ij is the transmission limitation coefficient between the ith grid node and the jth grid node (reflecting the transmission capacity of the grid connection);

[0126] This step calculates the dispatching strategy between each node in the power grid, and generates a reasonable dispatching plan by taking a weighted average of the load adjustment values, combined with the capacity factor and transmission limitation of the power grid.

[0127] After load distribution adjustment and dispatch strategy optimization, the next step is to optimize the operation efficiency of the power grid in real time. The optimization goal is to minimize the operation cost of the power grid while ensuring the efficiency and reliability of the power grid in operation. In order to achieve this goal, it is necessary to combine the power loss of the system, the equipment failure rate and the stability of the power grid for comprehensive optimization. The calculation expression is as follows:

[0128]

[0129] , where E opt is the optimized grid operation efficiency (objective function), which is used to measure the economy and efficiency of grid operation. i is the power loss coefficient of the ith grid node, ω i is the load efficiency factor of the i-th grid node, which indicates the energy efficiency performance of the node under specific load conditions, β i is the weight coefficient of the dispatching strategy of the ith grid node, which is used to measure the impact of the dispatching strategy on the grid operation efficiency.

[0130] This step optimizes the operation efficiency of the power grid in real time by comprehensively evaluating the load, loss, cost and efficiency of each node in the power grid, ensuring that the operating cost of the system is minimized while meeting safety requirements.

[0131] Implementation method 1: This implementation method proposes a power grid load forecasting and optimization scheduling system based on real-time data feedback, which aims to improve the operation stability and efficiency of the power grid and reduce the risk of power grid overload and equipment damage through continuous load monitoring, real-time forecasting and dynamic scheduling.

[0132] The system first collects real-time load data at each node of the power grid through smart meters, sensors and other equipment. Through the intelligent facilities in the power grid, the power consumption of each area and each device can be monitored in real time to obtain accurate load change information. At the same time, meteorological data, holiday information and other external disturbance factors that may affect load changes (such as emergencies, regional energy-saving policies, etc.) are also collected in real time. In order to ensure the accuracy and reliability of the prediction model, these external factors will be combined with historical load data as additional inputs to form a multi-dimensional data set.

[0133] Using this data set, the system uses a prediction model based on machine learning, especially the long short-term memory network (LSTM), to predict the load demand in the next few hours or even days. LSTM can effectively process time series data, learn long-term dependencies in historical data, and accurately capture the laws of load fluctuations. Through continuous iterative updates, the model can make timely adjustments when the grid load changes suddenly, reducing the load prediction error. The load prediction results provide accurate data support for subsequent grid dispatch.

[0134] Based on the real-time load forecast results, the system will then build a multi-objective optimization model involving multiple conflicting objectives, including grid stability, load balance, cost optimization, and operational efficiency. Grid stability is usually measured by monitoring the grid's safety margin, such as the system's ability to recover in the event of a fault or a sudden increase in load and the tolerable range of load fluctuations. The load balance goal is to maintain grid operation stability by ensuring that loads are evenly distributed in different areas and avoiding local overloads or excessive use of equipment.

[0135] In order to further optimize the operation of the power grid, the system also considers cost control and energy efficiency in the operation of the power grid. Through optimized scheduling, the system reduces operating costs as much as possible, including power generation costs, fuel costs, equipment operating costs, etc., while improving energy utilization efficiency and reducing power loss. The multi-objective optimization model comprehensively considers these goals and makes reasonable trade-offs in the operation of the power grid, so that each goal is appropriately met under different circumstances.

[0136] Through evolutionary algorithms (such as genetic algorithms or particle swarm optimization algorithms), the system dispatches the power grid according to the set goals and optimization results. Evolutionary algorithms can effectively handle conflicts between multiple goals, avoid falling into local optimal solutions, and find global optimal solutions. The optimization process will dispatch the operating status of various generators, transmission lines, substations and other equipment in the power grid to ensure that the power grid can maintain stability and improve operating efficiency while meeting load demand. For example, during certain high-load periods, the system can give priority to dispatching high-efficiency generators while limiting the operation of low-efficiency equipment to reduce fuel consumption and system costs.

[0137] Once the optimization results are generated, the grid dispatch center will perform actual operations according to the recommended dispatch plan and adjust the operating status of the grid equipment. However, during the operation of the grid, unexpected situations such as load fluctuations or failures may occur. The system will monitor the operating status of the grid in real time and make dynamic adjustments based on new data feedback. At this time, the real-time data feedback mechanism will optimize the grid dispatch plan again according to the operating status of the grid equipment and changes in load demand, ensuring that the grid can operate smoothly under various conditions.

[0138] During the implementation process, the system will continuously monitor and adjust the performance of the optimization model to ensure that it can adapt to various challenges in actual applications. Specifically, when actual load fluctuations or equipment failures occur in the power grid, the system will verify the optimization plan in real time according to pre-set evaluation criteria (such as load fluctuation amplitude, equipment load balance, etc.). If the optimization plan cannot meet the expected goals, the system will further optimize the prediction algorithm, improve the accuracy of the model, and readjust the weights and optimization goals. In addition, the system can also make timely adjustments through manual intervention to better respond to unforeseen power grid events.

[0139] Through this real-time data feedback mechanism, the power grid dispatching system can quickly respond to changes in power grid operation and provide efficient and stable operation support, while reducing the risk of power grid overload, reducing equipment failures and energy efficiency losses, and improving the overall efficiency of the power grid.

[0140] Implementation method 2: This implementation method proposes a multi-objective dispatch optimization platform based on historical data and external disturbance information, aiming to improve the accuracy and efficiency of power grid dispatch and reduce the risk of system imbalance or overload through big data analysis and optimization of decision support systems.

[0141] The platform first collects historical load data of the power grid through a multi-dimensional data acquisition system, including key indicators such as load changes, maximum load, and minimum load of each power grid node. At the same time, it collects external disturbance information that affects the power grid load, such as weather data (temperature, humidity, precipitation, etc.), holiday information, and large-scale event data. After preprocessing, these data enter the analysis module of the platform. The preprocessing process includes steps such as data cleaning, outlier removal, and time alignment to ensure the accuracy and consistency of the data.

[0142] The platform uses data mining technology to conduct a comprehensive analysis of these data and identify the periodic laws of load fluctuations and the impact of external factors on load changes. For example, rising temperatures may lead to an increase in air conditioning load, while holidays may cause a sudden change in the load demand of the power grid. By establishing a load demand forecasting model, the platform can predict the load demand within a certain period of time in the future based on historical data and external disturbance information, providing a basis for subsequent scheduling decisions.

[0143] Based on the load demand forecast, the platform establishes a multi-objective optimization model and solves it through the objective function. The objectives of the model include grid stability, load balance, cost optimization, and system efficiency. The grid stability goal is to ensure that the grid can operate safely and recover quickly in the event of load changes or sudden failures. The load balance goal is to improve the reliability of the grid by reasonably distributing the load in various areas of the grid to avoid excessive load in a single area. The cost optimization goal aims to maximize economic benefits by dispatching efficient generators to reduce unnecessary fuel consumption and equipment maintenance costs.

[0144] In the multi-objective optimization process, the platform uses evolutionary algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to solve and calculate the optimal solutions for multiple objectives. Since these objectives may conflict, the evolutionary algorithm can adjust the weights of the optimization objectives according to load changes, equipment operation conditions, and external disturbance information at different times to ensure a reasonable balance between the various objectives.

[0145] After the optimization plan is obtained, the platform will convert the optimization results into a specific grid dispatch plan through the automatic dispatch system, and implement real-time dispatch by connecting with the grid control system. The dispatch system will adjust the operating status of the generator set, the load distribution of the equipment, and the energy transmission strategy of the grid according to the optimization results to meet the load demand and ensure the stable operation of the grid.

[0146] However, load fluctuations or sudden failures may occur during the operation of the power grid, so the platform also needs to have dynamic adjustment capabilities. When the load changes in the actual operation of the power grid are inconsistent with the prediction, the platform will re-evaluate the optimization results and make dynamic adjustments based on real-time data. At this time, the evolutionary algorithm will optimize the dispatch plan in real time based on the new data input to ensure that the power grid can remain stable in the face of emergencies.

[0147] During the implementation of power grid dispatch, the platform evaluates the dispatch results through a real-time monitoring system. For example, by monitoring the load balance of each node in the power grid, the degree of cost optimization is evaluated, and whether the system stability reaches the expected goal. If it is detected that a certain goal has not been optimized, the system will automatically adjust the optimization model, recalculate the optimal solution, or perform manual intervention when necessary to ensure the effectiveness and practicality of the optimization plan.

[0148] The advantage of this platform is that it can not only improve the operating efficiency of the power grid through data prediction and optimized scheduling, but also make real-time adjustments during actual operation to ensure that the power grid is in the best operating state at all times and minimize the risks of overload and equipment failure.

[0149] Implementation method 3: This implementation method proposes a power grid dispatching method based on multidimensional data analysis and optimization decision support system, aiming to

[0150] Data analysis technology and real-time decision support system optimize grid load forecasting and scheduling, and improve grid stability and operational efficiency.

[0151] The system first collects historical load data, real-time load data, and external disturbance information of the power grid through the monitoring equipment of the power grid, meteorological stations, and external data sources (such as holiday information, large-scale events, etc.). After these data are processed by feature engineering, they are converted into a format suitable for machine learning models. Feature engineering includes data cleaning, normalization, time alignment, and processing of missing values. Through multi-dimensional analysis of power grid load data, meteorological data, and external disturbance information, the system can extract useful features from them, such as load change trends, the relationship between temperature and load, the impact of holidays on load, etc., to form a set of highly correlated feature sets, which provide accurate data input for the training of load forecasting models.

[0152] Based on feature engineering, the system establishes a load forecasting model based on machine learning. The model uses algorithms such as LSTM and random forest to learn historical load data and predict load demand in the future. LSTM is particularly suitable for time series data analysis and can capture the long-term dependencies of power grid load changes. The system gradually improves the accuracy of load forecasting by continuously training and optimizing the model. The load forecasting results will be used for subsequent power grid dispatch optimization to ensure that dispatch decisions are based on accurate data and trends.

[0153] Based on the load forecast, the system then builds a multi-objective optimization model, including grid stability, load balance, cost optimization, and system efficiency. Through evolutionary algorithms, such as genetic algorithms or particle swarm optimization algorithms, the system comprehensively optimizes these goals and finds the optimal balance point between different goals. Specifically, based on the predicted load demand and external disturbance information, the system optimizes the operating status of equipment, adjusts the scheduling of generator sets, and other means to achieve load balance, improve operating efficiency, and control costs while ensuring grid stability. Through this optimization method, the system can dynamically adjust the grid operation strategy according to the actual operating conditions and external environment.

[0154] The optimization results will be fed back to the power grid dispatching system in real time, and the system will adjust the operating status of the power grid equipment in real time according to the dispatching plan. When the power grid experiences load fluctuations or equipment failures, the real-time decision support system can respond quickly, recalculate the optimal dispatching plan, and make timely adjustments. This decision support system ensures the flexibility and adaptability of the power grid dispatching plan. By monitoring the power grid status in real time and combining it with the optimization plan, the system can continuously optimize the power grid operation and respond quickly when an emergency occurs, ensuring the safe and efficient operation of the power grid.

[0155] Through this system based on multi-dimensional data analysis, the power grid can not only maintain high efficiency and stability in daily operation, but also make flexible adjustments in the face of sudden load fluctuations and external disturbances, thereby maximizing the operating efficiency and safety of the power grid.

[0156] By combining historical load data and external disturbance information, the system can significantly improve the accuracy of power grid load forecasting, thereby reducing the impact of load forecasting errors on scheduling. Accurate load forecasting not only improves the scientificity and efficiency of power grid scheduling, but also helps the power grid identify potential load fluctuations in advance to avoid overload and equipment failure. By more efficiently scheduling power grid resources, ensuring the reasonable allocation of power generation capacity during peak load periods, reducing energy waste, and reducing the risk of system overload, the stability and reliability of the power grid are improved. In addition, accurate load forecasting also optimizes the operating cost of the power grid, ensuring more economical power production and operation.

[0157] With the help of multi-objective optimization models and the introduction of evolutionary algorithms, the present invention has comprehensively optimized the power grid in multiple dimensions such as load distribution, power grid stability, cost control and operating efficiency. The system dynamically adjusts the weights of the optimization objectives and flexibly adjusts the scheduling strategy according to real-time load demand and external disturbance factors to ensure the stable operation of the power grid under various complex situations. By optimizing equipment scheduling, reducing energy loss and improving system efficiency, the power grid can maintain efficient and stable operation when responding to emergencies and high loads, which not only reduces operating costs, but also improves the reliability and operating efficiency of the overall system.

[0158] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0159] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0160] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0161] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0162] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0166] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0167] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm, characterized in that: The following steps are involved: Obtain historical load data of the power grid and related external disturbance information to accurately describe the fluctuation pattern of the power grid load; Based on the historical load data of the power grid and combined with external disturbance information, a prediction model for power grid load demand is established, and a machine learning model is used to optimize the load prediction accuracy and reduce the prediction error; According to the forecast results of power grid load demand, a multi-objective optimization model is established, and the weights of each optimization objective are defined; Use evolutionary algorithms to perform multi-objective optimization analysis to find the optimal solution to minimize grid vulnerability according to the set optimization objectives, taking into account the volatility of grid load and actual operating conditions; According to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid.

2. The multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm according to claim 1 is characterized in that: The specific steps for obtaining the historical load data of the power grid and related external disturbance information to accurately describe the fluctuation law of the power grid load are as follows: First, historical load data of the power grid is collected and load changes in different time periods are analyzed to identify periodic fluctuations and abnormal conditions of the power grid load; Obtain data on external factors that affect grid load fluctuations to help analyze load change patterns; Standardize and pre-process the data collected from different sources to solve the problems of inconsistent data formats and missing data and ensure the validity of the data; Through visualization, the data is preliminarily analyzed to identify the fluctuation trend of power grid load and the impact of external disturbances, providing a reference for the load forecasting model.

3. The multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm according to claim 1 is characterized in that: Based on the historical load data of the power grid and combined with external disturbance information, a prediction model for power grid load demand is established. The specific steps to optimize the load prediction accuracy and reduce the prediction error using the machine learning model are as follows: By extracting key features from historical load data and external disturbance information and performing standardization, valuable data support is provided for the load forecasting model; According to the time series of load data, a machine learning algorithm is selected to establish a prediction model to capture the long-term dependency of load fluctuations; Reduce prediction errors and improve generalization ability by dividing the training set and test set, adjusting model parameters, and using cross-validation and regularization methods for model training and optimization; By comparing actual and predicted results, calculating error indicators and performing error analysis, model deviations are identified and further optimized to ensure the accuracy and reliability of load forecasting.

4. The multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm according to claim 1 is characterized in that: According to the prediction results of power grid load demand, the specific steps of establishing a multi-objective optimization model and defining the weights of each optimization objective are as follows: Define multiple goals for grid optimization and clarify the role and significance of each goal; Transform the power grid optimization objectives into mathematical functions and build models based on the constraints in actual operation; Assign weights to each optimization target based on actual demand and grid operation scenarios to balance the priority of each target; The operation constraints of the power grid are introduced, and the multi-objective optimization problem is solved by using evolutionary algorithms and other methods to ensure that the optimization results are practical.

5. The multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm according to claim 1 is characterized in that: The evolutionary algorithm is used to perform multi-objective optimization analysis. According to the set optimization objectives, the optimal solution to minimize the vulnerability of the power grid is found. The specific steps considering the volatility of the power grid load and the actual operating conditions are as follows: In the evolutionary algorithm, a population is first initialized, in which each individual represents a power grid dispatching scheme, and an objective function is set to measure the pros and cons of each dispatching scheme. For power grid vulnerability analysis, the multi-objective function is expressed as follows: F(x)=(f1(x), f2(x), f3(x), f4(x)), Where F(x) is the objective function, x represents a decision variable vector of a solution, f1(x) is grid stability, f2(x) is load balance, f3(x) is cost optimization, and f4(x) is operating efficiency; After the population is initialized, the evolutionary algorithm selects individuals with high fitness to enter the next generation through selection operations. The fitness function is used to evaluate the quality of each individual. The weighted sum optimization strategy of each objective function is adopted. Assuming the weighted sum form is used, the fitness function is expressed as follows: Fitness(x)=w1f1(x)+w2f2(x)+w3f3(x)+w4f4(x), Where Fitness(x) is the fitness function, w1, w2, w3 and w4 are the weights of grid stability f1(x), load balance f2(x), cost optimization f3(x) and operation efficiency f4(x), respectively, reflecting the importance of each objective in the optimization; In crossover and mutation, the genetic operation of the evolutionary algorithm is used to generate new individuals. Suppose the selected parent individuals are x1 and x2, and the crossover operation generates a new offspring individual x3. The formula is as follows: x3=α·x1+(1-α)·x2, In the formula, α∈[0,1] is the crossover weight parameter, which controls the influence of the parent individual on the offspring individual. The mutation operation introduces diversity by randomly adjusting the decision variables of the new offspring individual x3 generated by the crossover operation. The mutation operation is as follows: ， In the formula, δ is the variation amplitude, r is the random vector, It is the new individual after the mutation operation; In each generation of the evolutionary algorithm, by evaluating the fitness of individuals in the population, individuals with low fitness are continuously eliminated, and individuals with high fitness are retained, so as to gradually improve the quality of the entire population. After multiple generations of iterations, the optimal solution is finally obtained. The optimal solution formula is as follows: ， In the formula, x opt is the optimal solution, X t is the population set of generation t, Represents the population set X in the tth generation t In the above example, find the individual corresponding to the maximum value of the fitness function Fitness(x).

6. The multi-objective optimization analysis method for power grid vulnerability based on evolutionary algorithm according to claim 1 is characterized in that: According to the optimal solution of the evolutionary algorithm, the dispatching strategy and load distribution plan of the power grid are adjusted, the operation status of the power grid is monitored in real time, and dynamic adjustments are made according to the actual load fluctuations to ensure the safe and stable operation of the power grid. The specific steps are as follows: According to the optimal solution obtained by the evolutionary algorithm, the load distribution scheme of each node in the power grid is first calculated, and the load is adjusted according to the optimal solution to ensure that the load distribution of each node in the power grid is reasonable. The calculation expression is as follows: ， Where, L i is the load distribution value of the ith grid node, P i is the predicted load demand of the ith grid node, C i is the capacity factor of the ith grid node, P j is the predicted load demand of the jth grid node, C j is the capacity factor of the jth grid node, N is the total number of nodes in the grid, L max is the maximum load capacity of the entire power grid; In the actual operation of the power grid, load fluctuations and emergencies will lead to deviations in load distribution. At this time, the real-time monitoring system continuously detects the load status of each node in the power grid, and dynamically corrects the load distribution plan through real-time data to avoid overload and equipment damage. The calculation expression is as follows: ΔL i =L i -L actual，i , In the formula, ΔL i is the load adjustment value of the ith grid node, i.e., load deviation, L actual，i is the actual load value of the ith grid node; After correcting the load adjustment of each grid node, the grid dispatching strategy is calculated based on the adjustment. The calculation expression is as follows: ， In the formula, S i is the dispatching strategy of the ith grid node, D ij is the transmission limitation coefficient between the i-th grid node and the j-th grid node; After load distribution adjustment and dispatch strategy optimization, the next step is to optimize the operation efficiency of the power grid in real time. The optimization goal is to minimize the operation cost of the power grid while ensuring the efficiency and reliability of the power grid in operation. The calculation expression is as follows: ， In the formula, E opt is the optimized grid operation efficiency, R i is the power loss coefficient of the ith grid node, ω i is the load efficiency factor of the ith grid node, β i is the weight coefficient of the dispatching strategy of the ith grid node.

Images