Group power consumption behavior simulation analysis method for multi-user cooperation scene

Abstract

The invention relates to the technical field of electric power system analysis, and discloses a multi-user cooperation scene-oriented group electricity consumption behavior simulation analysis method, which comprises the following steps of S1, data acquisition: collecting electricity consumption historical data, geographical location information, family member quantity, social and economic conditions, weather data and seasonal factors of users; s2, data preprocessing: cleaning and standardizing the collected data, removing abnormal values, filling missing values, and standardizing the data to ensure the consistency of the data; and S3, data clustering: grouping the users by using a clustering algorithm, and classifying the users with similar power consumption behaviors into one group. Based on the combination of the game theory model, the optimal control theory and the reinforcement learning algorithm, accurate optimization and real-time dynamic adjustment of the power grid dispatching strategy are realized, the power grid load management efficiency and the resource utilization rate are improved, and the stability and high efficiency of the power grid in a complex environment are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and specifically to a simulation analysis method for group electricity consumption behavior in a multi-user collaborative scenario. Background Art

[0002] In modern society, electricity has become an indispensable resource in daily life. From household daily electricity consumption to industrial production and commercial operations, the demand for electricity is increasing day by day, and it shows diversification and uncertainty. For example, different times, seasons, and the occurrence of special events will directly affect the electricity consumption behavior of various users, and the fluctuations of the power grid load will also increase accordingly. In order to ensure the stable and efficient operation of the power grid, there is an urgent need for a power grid scheduling method that can dynamically adjust resource allocation according to real-time data and optimize load management.

[0003] In the prior art, power grid scheduling systems usually rely on fixed scheduling strategies, perform load forecasting through historical data, and allocate resources based on these forecasts. Many traditional power grid scheduling systems have been able to provide a certain degree of scheduling optimization under static conditions, reduce energy waste, and improve the reliability of the power grid. Through the statistical analysis of user electricity consumption, these methods can reasonably predict the changes in power grid load in most cases and adopt certain resource allocation strategies, thereby effectively reducing the risk of power grid imbalance.

[0004] However, there are still some deficiencies in the prior art; firstly, most of the existing power grid scheduling methods rely on the electricity consumption data of a single user, lacking the modeling of the interaction relationships between different users. With the diversification of power grid load and user behavior, simple prediction and scheduling methods can no longer handle complex collaborative effects; secondly, many existing scheduling methods cannot perform real-time dynamic adjustment, and the scheduling strategies of the power grid cannot quickly respond to uncertain factors such as sudden load fluctuations or equipment failures, resulting in low efficiency of power grid load management and resource allocation. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a simulation analysis method for group electricity consumption behavior in a multi-user collaborative scenario, which solves the problems in the prior art that it is impossible to effectively simulate multi-user collaborative behavior and real-time dynamically adjust the power grid scheduling strategy.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A simulation analysis method for group electricity consumption behavior in a multi-user collaborative scenario, including the following steps: S1. Data collection: Collect the electricity consumption historical data, geographical location information, number of family members, social and economic situation, weather data, and seasonal factors of users; S2. Data preprocessing: Clean and standardize the collected data, remove outliers, fill in missing values, and standardize the data to ensure data consistency; S3. Data clustering: Use clustering algorithms to group users and classify users with similar electricity consumption behaviors into one group; S4. Behavior modeling: Based on the clustering results of the data, use game theory models to simulate the interaction behaviors among group users and determine the decision-making strategies of each user in group behaviors; S5. Scheduling optimization: Use optimal control theory to optimize the power grid scheduling strategy, minimize the power grid operation cost, and ensure the balance of the power grid load and the optimal allocation of resources; S6. Real-time optimization: Based on reinforcement learning algorithms, adjust the scheduling strategy in a real-time environment and perform dynamic optimization according to changes in user behaviors.

[0007] Preferably, the data collection includes: Collect users' electricity consumption behavior data, historical power demands, users' socioeconomic information, meteorological conditions, and seasonal impact data; Collect and real-time update users' electricity consumption behavior data, including real-time data fed back by smart meters and home appliance automation systems; Collect environmental information related to the power grid, including weather changes, power load status, and daily electricity consumption patterns.

[0008] Preferably, the data cleaning and standardization process includes: Clean the collected multi-dimensional data, remove missing values and noisy data, and fill in the missing data; Perform standardization processing on the data, convert data from different sources into a unified scale for subsequent analysis and modeling; Use data normalization techniques to convert different data types into standardized numerical values to ensure data consistency.

[0009] Preferably, the clustering algorithms include: Use the K-means clustering algorithm to classify user data and classify users with similar electricity consumption behaviors into one category to reduce the computational complexity; Based on the clustering results, conduct behavior analysis of user groups and identify the electricity consumption demands and scheduling patterns of different groups.

[0010] Preferably, the game theory models include: Use the Nash equilibrium model to simulate the decision-making behaviors of multiple users when facing power grid scheduling pressures; By defining the utility function of each user, describe its decision-making objectives and constraints in group electricity consumption behaviors; Simulate the interaction effects among multiple users, optimize the user decision-making strategies through the game process, and calculate the stable points of the group's electricity consumption behavior.

[0011] Preferably, the optimal control theory includes: Define the scheduling objective of the power grid, minimize the operating cost of the power grid, and meet the resource constraints of the power grid; Based on the dynamic programming method in the optimal control theory, solve the power grid scheduling strategy and dynamically adjust the load distribution; Through the Pontryagin maximum principle, optimize the scheduling strategy to ensure the balance of the power grid load and maximize the energy utilization efficiency.

[0012] Preferably, the reinforcement learning algorithm includes: Use the Q-learning algorithm, combined with real-time data feedback, to dynamically adjust the power grid scheduling strategy; By updating the Q-value function, optimize the user electricity consumption behavior and the power grid resource allocation to ensure the operation of the power grid; Introduce a deep Q-network model to enhance the ability of Q-learning to handle high-dimensional state spaces and action spaces.

[0013] Preferably, the optimization of the power grid scheduling strategy includes: Based on the prediction results of the game theory model and the reinforcement learning algorithm, adjust the power grid scheduling strategy in real time; In the case of uneven power grid load, dynamically optimize the resource allocation to balance the power grid load; Use the reinforcement learning model to automatically adjust the electricity consumption strategy according to the real-time changes in the power grid load to optimize the scheduling effect of the power grid.

[0014] Preferably, the real-time feedback mechanism includes: Collect power grid and user electricity consumption data and update the input of the simulation model in real time; Based on the real-time power grid load, user behavior feedback, and environmental changes, automatically adjust the power grid scheduling strategy to cope with load fluctuations and emergencies; Provide a real-time decision support system to ensure that the power grid can respond quickly and optimize the scheduling plan.

[0015] Preferably, the decision support system includes: Provide a visual display of the power grid scheduling plan to help power grid managers grasp the scheduling results in real time; Provide intelligent scheduling optimization suggestions to dynamically adjust the power grid based on the simulation model; Through the interactive interface, power grid managers can adjust the power grid strategy according to the real-time feedback.

[0016] The present invention provides a simulation analysis method for group electricity consumption behavior in a multi-user collaboration scenario, which has the following beneficial effects: 1. The present invention adopts a game theory model, combines Nash equilibrium and evolutionary game, and effectively simulates the interactive decisions among multiple users. Compared with the prior art, it can more accurately predict and optimize the collective behavior of the user group, avoiding the deficiency of traditional models that ignore the interactive relationship among users.

[0017] 2. By introducing the optimal control theory, the present invention realizes the global optimal scheduling of power grid resources. This method not only minimizes the operation cost of the power grid, but also effectively balances the load and resource allocation. Compared with the existing methods, this global optimization has more advantages in complex power grid scenarios, improving the economy and stability of the power grid.

[0018] 3. The present invention combines the Q-learning algorithm for real-time optimization, enabling the power grid to dynamically adjust the scheduling strategy and quickly respond to load fluctuations and emergencies. As a result, the power grid scheduling can flexibly cope with uncertainties. Compared with traditional static scheduling methods, real-time optimization significantly improves the adaptability and flexibility of the power grid in the face of changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to the attached Figure 1 , the embodiments of the present invention provide a simulation analysis method for group electricity consumption behavior in a multi-user collaboration scenario, including the following steps: S1. Data collection: Collect the electricity consumption historical data, geographical location information, number of family members, social and economic situation, weather data and seasonal factors of users; The purpose of data collection is to collect multi-dimensional user data, environmental data, power grid data and social and economic information related to user behavior. These data provide the basis for subsequent behavior modeling, scheduling optimization and real-time adjustment. The quality and comprehensiveness of the data directly affect the accuracy of the final simulation analysis results and the optimization effect of power grid scheduling. Therefore, in this embodiment of data collection, the data sources, collection methods and their quality control will be strictly controlled to ensure that the collected data can provide accurate and comprehensive information support for the subsequent steps.

[0022] User electricity consumption behavior data mainly refers to the user's historical electricity consumption data, electricity consumption patterns, and the usage of household devices. The electricity consumption behavior of users is affected by various factors, including but not limited to time, season, weather, and household activity patterns. The collected data can help the system analyze the user's electricity consumption preferences, thus providing input for the simulation model.

[0023] Historical electricity consumption data: This part of the data mainly records the user's daily electricity consumption, which can be collected through smart meters. Smart meters will record the electricity consumption in each time period (unit: kWh) in real time and transmit the data to the data processing center through wireless communication protocols. By analyzing the historical electricity consumption data, the system can identify the user's electricity consumption patterns and demand fluctuations.

[0024] Usage of electrical equipment: This part of the data is collected through home automation systems or smart home devices and can identify the devices that users turn on or off during specific time periods. For example, devices with relatively large power consumption such as air conditioners, water heaters, and washing machines have a greater impact on the overall power grid load.

[0025] The core purpose of collecting these data is to understand the electricity consumption demand patterns of different users and provide a basis for subsequent modeling and prediction.

[0026] Environmental factors such as weather, season, and holidays have a significant impact on users' electricity consumption behavior. For example, in the hot summer, the usage of air conditioners will increase significantly, especially in high-temperature weather. By obtaining real-time weather data and season information, the system can predict the peak electricity consumption period of users and perform load scheduling based on this data.

[0027] Weather data: Weather data includes temperature, humidity, wind speed, etc. These data are collected through real-time APIs provided by meteorological departments, and the system can obtain real-time weather change information. The key role of weather data is to analyze its impact on high-energy-consuming devices such as air conditioners and heating systems.

[0028] Seasonal data: Seasonal changes directly affect users' electricity consumption behavior. For example, the heating demand is relatively high in winter, and the air conditioner demand is relatively large in summer. Seasonal factors are calculated based on dates and climate patterns. The system adjusts the electricity consumption demand model according to seasonal changes to ensure the accuracy of load forecasting.

[0029] Data such as the load status of the power grid, power supply capacity, and generator operating status help the system understand the current working state of the power grid, and then make reasonable resource allocation during the scheduling process. The data collection of the power grid mainly relies on the real-time data interfaces provided by power grid companies, including information such as load, power generation capacity, and transmission line status.

[0030] Grid Load: Grid load reflects the total electricity consumption of the entire power grid at a certain moment. Through real-time load data, the system can judge the current load status of the power grid and conduct load dispatching according to the predicted group electricity consumption behavior.

[0031] Power Generation Capacity and Grid Status: The operating status of generators directly affects the power supply capacity of the grid. By obtaining data such as the grid load status, generator operation conditions, and equipment health status, the system can adjust grid resources in real time and respond to emergencies (such as insufficient power supply).

[0032] The social and economic information of users provides additional value for personalized electricity consumption prediction. For example, households with higher income levels may have more power-consuming devices, while the electricity demand of low-income households is relatively low.

[0033] Family Structure and Income Level: Social and economic factors such as the number of family members, the living habits of family members, and income level will affect electricity consumption behavior. For example, large families may have higher electricity demand during specific periods, while the demand of single-person households fluctuates less. Households with higher income levels may be more inclined to use smart home devices, increasing the complexity of electricity load.

[0034] The implementation method of data collection combines modern Internet of Things technology and smart meter systems. All collected data is transmitted in real time through wireless sensor networks (WSN), smart meters, and home appliance automation systems. After being collected by sensors or meters, the data is preprocessed and then uploaded to the data center for unified storage and analysis.

[0035] The structure of the data collection module is as follows: ; Among them: represents the electricity consumption behavior data of all users, including historical electricity consumption data and real-time electricity consumption data; represents the data of all environmental factors, including weather, seasonal changes, etc.; represents the relevant data of the power grid, including load, generator status, transmission status, etc.; represents the social and economic information of users, such as income level, family structure, etc.

[0036] The key steps of data collection include: Use smart meters to collect users' electricity consumption in real time and upload the data to the cloud platform through wireless protocols (such as ZigBee, LoRa, etc.).

[0037] Obtain real-time weather data through the meteorological API interface, and calculate seasonal changes through holiday tables and date algorithms.

[0038] Obtain real-time power grid load data and generator status through the power grid monitoring system for power grid load monitoring and dispatching preparation.

[0039] User social and economic data can be collected through questionnaires filled out voluntarily by users, social security records, or other public data sources.

[0040] To ensure the effectiveness and real-time nature of the data, the collected raw data must undergo strict quality control. Specifically, it includes: Data consistency check: Ensure that data from different devices or sensors is consistent in time and space. For example, check whether the weather data collected by different sensors is within a reasonable range and adjust inconsistent data.

[0041] Real-time synchronization: Synchronize all real-time data streams to ensure that the simulation analysis can obtain the most timely data input.

[0042] Abnormal data processing: Verify and correct the collected data through preset thresholds and algorithms. Abnormal data, such as sudden sensor errors or meter failures, must be marked and excluded during the data processing stage.

[0043] Through the design of the data acquisition module, multi-dimensional data related to users' electricity consumption behaviors can be comprehensively and real-time collected, covering user behaviors, environmental factors, power grid status, and social and economic information. The control and real-time update of data quality ensure the response speed and accuracy of the entire system in a complex environment, providing solid data support for power grid dispatching optimization.

[0044] S2. Data preprocessing: Clean and standardize the collected data, remove outliers, fill in missing values, and standardize the data to ensure data consistency; Data preprocessing not only cleans the raw data but also includes operations such as data standardization, denoising, and missing value filling to ensure the consistency, integrity, and accuracy of the data used. Accurate and effective preprocessing can improve the calculation efficiency and prediction accuracy of subsequent algorithms.

[0045] Generally, the multi-dimensional data collected often has missing values, outliers, noise, and different dimensions. If these problems are not solved, they will affect the accuracy of simulation analysis and dispatching optimization. Therefore, the goal of data preprocessing is to remove incorrect data, unify the data scale, and provide a reliable data basis for subsequent group behavior modeling and optimization.

[0046] In this embodiment, the main steps of data preprocessing include the following: Data cleaning is the process of initially screening and correcting the collected raw data. The core objective of cleaning is to remove data that does not conform to rules or is invalid, retain accurate data, and prevent incorrect data from affecting subsequent analysis and modeling.

[0047] Removing outliers: Outliers are usually caused by sensor failures, data collection errors, etc. To ensure data accuracy, it is necessary to first identify and remove these outliers. We can identify outliers by setting reasonable thresholds and eliminate them. Suppose is the electricity consumption data of a certain user. If exceeds a certain range , , then the data is considered abnormal and can be screened using the following formula: ; where: represents the dataset after removing outliers, and are the minimum and maximum values of the data respectively; represents the electricity consumption data of the th user at a certain moment.

[0048] Filling in missing data: Data missing is a common problem, and missing data can cause biases in model training and simulation. Common filling methods include mean filling, linear interpolation, and interpolation methods based on other features. In the case of less missing data, the mean filling method can be used to replace the missing data with the mean of the variable. For time series data, it is more reasonable to use linear interpolation to fill in the missing data. The formula for linear interpolation is: ; where: represents the interpolated data value, and are the actual data values at the moments before and after the missing data point respectively.

[0049] Data standardization and normalization are necessary steps for processing data with different scales. Especially when dealing with multi-dimensional data, it is very important to ensure the unified scale of different data sources. Standardization and normalization can ensure that different features will not affect the results due to different dimensions during model training.

[0050] Standardization: Standardization is to convert data into a form with zero mean and unit standard deviation through the mean and standard deviation of the data. Specifically, the standardization process can be carried out using the following formula: ; where, represents the standardized data, is the original data, is the mean of the dataset, is the standard deviation of the dataset. After standardization, the mean of all features is 0 and the standard deviation is 1, which is beneficial for each feature to have the same weight in subsequent analysis.

[0051] Normalization: Normalization is to compress the data into a specified range, usually [0, 1] or [-1, 1]. In this embodiment, the commonly used normalization method is min-max normalization. The formula is as follows: ; where: represents the normalized data, and are the minimum and maximum values of the data respectively. The normalized data will be compressed into the range of [0, 1], which helps to ensure that each data feature will not affect the performance of the model due to its different magnitudes in subsequent modeling; represents the original data value.

[0052] The object of the present invention is to process a dataset with multi-dimensional data, which comes from different sources and covers user behavior, grid status, and relevant information on the environment and socioeconomic. In order to make full use of these data, multi-dimensional data fusion and integration must be carried out to ensure that the data can be used in a unified feature space.

[0053] Feature fusion: Feature fusion is the process of integrating data from different sources into a unified feature space. In this embodiment, methods such as weighted average method, principal component analysis (PCA), etc. may be used for data fusion. Through the weighted average method, different weights can be assigned to each feature and combined into a unified feature vector. Specifically, assume , , , are data features from different sources, and the fused feature can be expressed as: ; where: , , , are the weights of the features, which can be assigned according to the magnitude of the overall influence of the features; , , , represent data features from different sources.

[0054] Principal Component Analysis (PCA): PCA is a commonly used dimensionality reduction method that can reduce the dimensionality of data while retaining the main information. PCA calculates the covariance matrix and performs eigenvalue decomposition on it to obtain the directions of the principal components, projects the original data onto these principal components, and forms a low-dimensional representation. In this embodiment, PCA is used to reduce the dimensionality of the feature space, thereby improving the computational efficiency of subsequent modeling.

[0055] To ensure the quality of the data, quality control will be carried out after data collection. Especially during the data cleaning and preprocessing process, the following measures are required to ensure the validity of the data: Consistency check: By comparing the outputs of different data sources, check whether there are inconsistencies or logical errors in the data. If data deviations are found, adjustments or discards will be made.

[0056] Real-time synchronization: To ensure the update and consistency of real-time data, all data collected by sensors will be synchronized through timestamps to ensure the correspondence between each data item and its time point.

[0057] Through accurate cleaning and processing, the data will be able to reflect the real needs and behaviors of users, providing reliable support for the simulation analysis of the group electricity consumption behavior of the present invention. At the same time, data quality control ensures the stability and reliability of the entire system during long-term operation, providing a solid foundation for subsequent precise power grid scheduling decisions.

[0058] S3. Data clustering: Use clustering algorithms to group users and classify users with similar electricity consumption behaviors into one group; Data clustering is to group users with similar electricity consumption behaviors for subsequent tasks such as group behavior modeling and scheduling optimization. Through clustering analysis, we can reduce the computational complexity and provide targeted scheduling strategies by identifying the electricity consumption patterns of different user groups.

[0059] Generally, there are various types of clustering methods, including distance-based algorithms, density-based algorithms, etc. To ensure the efficiency and accuracy of subsequent processing, this embodiment selects the classic K-means clustering algorithm. This algorithm divides the data set into several clusters and finds the optimal clustering result by minimizing the distance between the samples within the cluster and the cluster center.

[0060] In this embodiment, the K-means algorithm is used for data clustering, and the specific process is as follows: The K-means algorithm is an unsupervised learning algorithm based on distance metrics, suitable for large-scale datasets, especially for classifying user behavior patterns. In the analysis of group electricity consumption behavior, the K-means algorithm can group users according to information such as users' electricity consumption data and environmental factors, so that users within each group have similarities in electricity consumption behavior.

[0061] The basic idea of the K-means algorithm is: by dividing the dataset into clusters, minimizing the distance from each data point to the center of its belonging cluster. The clustering process includes the following steps: Initializing cluster centers: First, randomly select initial cluster centers.

[0062] Assigning data points: Calculate the distance between each data point and each cluster center, and assign the data point to the cluster center with the closest distance.

[0063] Updating cluster centers: Recalculate the center of each cluster, usually the mean of all data points within the cluster.

[0064] Iterative optimization: Repeat steps 2 and 3 until the cluster centers no longer change or change very little.

[0065] The core of K-means clustering is to calculate the distance between data points and cluster centers. To measure the similarity between data points and cluster centers, the Euclidean distance is usually used. Its calculation formula is as follows: ; where: represents the Euclidean distance between the data point and the cluster center . This distance metric measures the similarity between the data point and the cluster center; is the th eigenvalue of the data point ; is the th eigenvalue of the cluster center , representing the value of the cluster center on this feature; is the number of features of the data point, that is, the data dimension of each user, usually including users' historical electricity consumption data, environmental data, etc.

[0066] By calculating the Euclidean distance between each data point and each cluster center, the K-means algorithm assigns the data points to the closest cluster centers, thus completing the clustering process.

[0067] After clustering is completed, each cluster represents a group of users with similar electricity consumption behaviors. Through cluster analysis, we can identify the electricity consumption patterns and demand characteristics of different user groups. Users in each cluster have similar electricity consumption behaviors and environmental response patterns, and these similarities enable the power grid to adopt targeted scheduling strategies based on the characteristics of different groups.

[0068] Analysis of behavior patterns: The user groups after clustering can be used for further analysis of behavior patterns. For example, users in a certain group may show a high demand for air conditioner use in hot summer, while users in another group may show a high demand for heating in winter. By analyzing the electricity consumption patterns of users within each cluster, the system can extract the electricity consumption demand characteristics of the group, providing support for power grid scheduling.

[0069] Scheduling optimization: During the scheduling optimization process, the power grid can perform load forecasting and resource allocation based on the electricity consumption characteristics of each cluster. For example, when the electricity consumption demand of a certain group increases, the power grid can predict the load pressure in advance, optimize the resource allocation, and avoid load overloading.

[0070] Real-time feedback and adjustment: For users in different groups, the power grid can dynamically adjust the power grid scheduling by combining real-time monitoring data with the clustering results to ensure the stability of the power grid and the optimal use of resources.

[0071] The ultimate goal of K-means clustering is to make the data points within each cluster as close as possible, and the differences between clusters as large as possible. To evaluate the clustering effect, the following metrics can be used: Within-cluster Sum of Squares (WSS): This is an important metric to measure the tightness of samples within a cluster. The smaller the within-cluster sum of squares, the closer the data within the cluster, and the better the clustering effect. The formula is: ; Where: represents the within-cluster sum of squares, that is, the sum of the squares of the distances between all sample points within the cluster and the cluster center; n represents the th cluster; represents the cluster in the data points; is the cluster center; represents the data point and the cluster center the distance between;

[0072] Between-cluster Sum of Squares (BSS): Measures the differences between different clusters. The larger the between-cluster distance, the more significant the differences between different clusters, and the better the clustering effect. The formula is: ; Where: represents the sum of inter-cluster distances, i.e., the distances between the center points of each cluster; is the number of data points in the th cluster; is the center of the th cluster;

[0073] By optimizing the intra-cluster distance and inter-cluster distance, the K-means algorithm can obtain the best clustering results.

[0074] Through the K-means clustering algorithm, users with similar electricity consumption behaviors can be efficiently divided into several groups. Each group represents a class of users with similar electricity consumption demands, facilitating subsequent analysis of behavior patterns, load forecasting, and optimization of power grid dispatching.

[0075] S4. Behavior Modeling: Based on the clustering results of the data, use game theory models to simulate the interaction behaviors among group users and determine the decision-making strategies of each user in group behaviors; S4 adopts the Nash equilibrium and evolutionary game models in game theory to describe and predict the group electricity consumption behaviors in multi-user collaborative scenarios. By establishing these behavior models, power grid dispatching can be adjusted more precisely according to the dynamic electricity consumption behaviors of the group, thereby achieving optimal power grid resource allocation.

[0076] The goal of behavior modeling is to understand how different user groups make electricity consumption decisions in a complex multi-user environment and predict the load demand and change trends of the power grid based on these decision-making patterns. Through game theory models, the behavior choices of users in the face of power grid dispatching pressure can be simulated, and then the stable points of group behaviors can be analyzed, providing a basis for power grid load forecasting and dispatching optimization.

[0077] In this embodiment, game theory models are adopted for behavior modeling, and the specific process is as follows: The Nash equilibrium model in game theory is used to describe how each participant (each user in this scenario) selects the optimal electricity consumption strategy in multi-party interactive decision-making to achieve a stable equilibrium state in group electricity consumption decisions. In the Nash equilibrium state, the decision of each user is optimal, and no user can improve their utility by changing their strategy when the strategies of other users remain unchanged.

[0078] Mathematical expression of Nash equilibrium: ; Where: represents the th user's utility function in group electricity consumption decisions, measuring the user Under the given electricity consumption strategies of other users, select the electricity consumption at this time for the benefit or cost; represents the electricity consumption strategies of all users, where is the total number of users, is the electricity consumption of user ; represents the alternative electricity consumption selected by user under the given strategies of other users. At this time, is the other strategy that user can choose.

[0079] This formula represents that in the Nash equilibrium state, the utility function of the th user Under the given electricity consumption of other users, it is impossible to improve its own utility by changing its own electricity consumption strategy to improve its own utility.

[0080] The purpose of this formula is to reveal the conditions for the group's electricity consumption behavior to reach a stable state, that is, each user selects the best strategy when the strategies of other users remain unchanged, so as to ensure the stable operation of the entire system.

[0081] In addition to the Nash equilibrium model, the evolutionary game model is also introduced into this embodiment to simulate the long-term evolution process of the group's electricity consumption behavior. In the evolutionary game, the decision of each user changes continuously over time, and the user adjusts its strategy according to the historical score (such as the utility score). The system helps predict the long-term stable state of the group's electricity consumption behavior by simulating the behavior evolution of multiple generations of users.

[0082] State transition equation of the evolutionary game: ; Where: represents the electricity consumption strategy of user at time : represents the strategy adjustment parameter of user , which can be a learning rate, or represents the behavior adjustment parameter of the user under a certain specific condition; represents the strategy update function, which defines how the user updates the current strategy according to the historical behavior (i.e., the strategy at the previous moment) and the strategy adjustment parameter.

[0083] In the evolutionary game model, each user adjusts its strategy according to the historical score in each round of the game, and this process simulates the long-term evolutionary group electricity consumption behavior. Finally, through multiple rounds of games and strategy updates, the system can find a stable group electricity consumption pattern to help predict the evolutionary trend of the group behavior in the long term.

[0084] The utility function is a key component in game theory models. To more accurately simulate the electricity consumption behavior of a group, the utility function not only considers the electricity consumption of each user but also factors such as grid load, electricity price, and the mutual influence among users. The design of the utility function needs to ensure that it can truly reflect the decision-making behavior of users and is consistent among all users.

[0085] The basic form of the utility function is: ; Where: represents the utility of the -th user in the electricity consumption behavior of the group; represents the electricity consumption of the -th user; represents the impact of the electricity consumption of the -th user on user . Specifically, the electricity consumption behavior of user will have an impact on the utility of user ; represents the electricity price or electricity consumption cost related to the electricity consumption of user ; represents the impact of controlling electricity consumption on personal utility, represents the impact of controlling the behavior of other users on personal utility, represents the impact of controlling electricity price on user decision-making.

[0086] This utility function can comprehensively consider the electricity consumption cost, grid load, electricity price, and the behavior impact of other users, providing an important basis for modeling the electricity consumption behavior of the group.

[0087] These game theory models not only help identify the optimal strategies of each user in grid scheduling but also reveal the long-term evolution trend of the electricity consumption behavior of the group. By designing an appropriate utility function, the system can optimize the electricity consumption decisions of users and ensure that the electricity consumption behavior of the group operates in a stable state.

[0088] S5, Scheduling Optimization: Using the optimal control theory to optimize the grid scheduling strategy, minimizing the grid operation cost, and ensuring the balance of grid load and the optimal allocation of resources; The core of scheduling optimization is to dynamically adjust the grid scheduling strategy through the optimal control theory to minimize the grid operation cost while ensuring the balance of grid load and the optimal allocation of resources.

[0089] The scheduling optimization method not only needs to consider the electricity consumption needs of the user group, but also needs to consider the operating constraints of the power grid, resource availability, and system stability. Therefore, the combination of the optimal control method and dynamic programming helps to obtain an efficient and stable scheduling scheme in a large-scale power grid system.

[0090] The optimal control theory is a method for optimizing the control strategy of a dynamic system. In power grid scheduling, the application of the optimal control theory aims to achieve the optimal allocation of power grid resources and minimize the operating cost through a reasonable control strategy. The optimal control problem can usually be completed by solving the following optimization objective function: ; where: represents the total operating cost, which is the optimization objective of the power grid. This objective includes factors such as generation cost, load scheduling cost, equipment maintenance, etc.; represents at time the state variables of the power grid system, usually including power grid load, power demand, battery energy storage state, etc.; represents at time the control variables, that is, the power grid scheduling strategy, usually including power distribution, energy storage charge and discharge strategy, etc.; represents at time the operating cost function. This cost function usually includes costs related to generation and transmission; represents the Lagrange multiplier, which is used to describe the constraint conditions of power grid load balance; represents at time the constraint function of the power grid, such as load balance, power grid stability, etc.

[0091] The meaning of this formula is that during the entire scheduling process, the operating cost of the power grid not only depends on the current scheduling strategy , but is also affected by the system state and the constraint conditions . Through the optimal control theory, the optimal scheduling strategy can be calculated to minimize the operating cost of the system while satisfying all constraint conditions.

[0092] Dynamic programming is an optimization method that solves the optimal solution by decomposing complex problems into multiple sub-problems. In the power grid scheduling problem, the dynamic programming method can gradually optimize the decisions at each moment and finally obtain the globally optimal scheduling strategy. The Pontryagin maximum principle provides the necessary optimality conditions for the optimal control problem.

[0093] The state equation of the optimal control problem can be expressed as: ; where: represents the power grid system state At time The rate of change, i.e., the dynamic evolution of the system state; Denotes the dynamic function of the power grid system, which describes how state variables such as power grid load change with the Change of the scheduling strategy; Denotes the initial state of the system, usually the known initial power grid load or energy storage state.

[0094] Through the dynamic programming method, the system can calculate the optimal scheduling strategy at each moment, thus achieving the optimal allocation of power grid resources globally.

[0095] When solving the optimal control problem, we can also use the Pontryagin maximum principle and obtain the necessary conditions for the optimal control problem through the Lagrange multiplier method. According to the Pontryagin maximum principle, the optimal control strategy Needs to satisfy the following conditions: ; Where: Denotes the Hamiltonian function, which combines the control strategy, system state, and constraint conditions to represent the overall cost of the system; Denotes the Lagrange multiplier, usually used to represent the influence of load balance or other constraint conditions; Denotes the operating cost of the power grid at time Related to factors such as power grid load and power generation; Denotes the constraint function of the power grid at time Usually related to power grid load balance, power grid stability, etc.

[0096] By solving the minimum value of the Hamiltonian function, the optimal control strategy can be obtained, and it is ensured that while the power grid satisfies the constraint conditions, the operating cost is minimized.

[0097] The key to scheduling optimization lies in achieving dynamic adjustment. In the actual power grid, the electricity consumption behavior of users and the power grid load are constantly changing. Therefore, it is necessary to dynamically adjust the scheduling strategy through real-time feedback. The real-time optimization method based on the reinforcement learning algorithm can adjust the scheduling strategy according to the real-time state of the power grid and the changes in user behavior, so as to ensure that the power grid can reach the optimal load state at any time.

[0098] In this embodiment, the real-time optimization algorithm can be enhanced using the Q-learning algorithm. The Q-learning algorithm evaluates the optimal actions in each state by continuously updating the Q value.

[0099] Q-learning algorithm update formula: ; Wherein: : represents the Q value at time grid state and control strategy ; is the learning rate, representing the weight of new information when updating the Q value each time; is the reward function, representing the immediate reward obtained after taking a certain control strategy at time , usually related to the operating cost of the grid or the load optimization situation; is the discount factor, representing the attenuation degree of future rewards; represents the maximum Q value obtained by the optimal control strategy at the next time ; is the state of the grid at time ; is the control strategy of the grid at time ;

[0100] By continuously adjusting the Q value, the system can learn the optimal scheduling strategy in each state and make rapid responses and adjustments according to real-time data, thereby achieving the optimal allocation of grid resources.

[0101] Through the scheduling optimization steps, combining the optimal control theory, dynamic programming, and Pontryagin's maximum principle, the grid can perform optimal scheduling based on the prediction of group electricity consumption behavior, real-time feedback, and load management. The real-time optimization method based on reinforcement learning can dynamically adjust the grid scheduling strategy in a complex multi-user collaborative environment to ensure that the grid can operate efficiently and stably under any circumstances.

[0102] S6. Real-time optimization: Based on the reinforcement learning algorithm, adjust the scheduling strategy in a real-time environment and perform dynamic optimization according to changes in user behavior; The operation of the grid is carried out in a dynamic environment, and the load and user behavior will change continuously. Therefore, real-time optimization has become an indispensable part of the grid scheduling process. Through real-time optimization, the grid can adjust the scheduling strategy according to real-time data feedback to cope with sudden load changes, user electricity consumption fluctuations, and real-time changes in grid resources.

[0103] The core method of real-time optimization is to perform reinforcement learning through the Q-learning algorithm. This algorithm can dynamically adjust the control strategy of the grid according to the current state and scheduling requirements of the grid, realizing the optimal allocation of grid resources and load balancing. Q-learning evaluates the effects of different control strategies in each state by repeatedly updating the Q value, and finally realizes the optimal strategy.

[0104] In power grid dispatching, the Q-learning algorithm is applied to real-time optimization problems. Q-learning is a value-based reinforcement learning method that can, based on the current state of the power grid and control strategy , through exploration and learning, continuously adjust the strategy to achieve the optimization goal. Q-learning gradually optimizes the control strategy by updating the Q-value, enabling the power grid to make appropriate dispatching decisions in the face of dynamic changes.

[0105] By continuously updating the Q-value, the Q-learning algorithm can learn the optimal dispatching strategy, thereby adjusting the control scheme of the power grid in real time to cope with the dynamic changes in the actual power grid.

[0106] To ensure that the power grid dispatching strategy can adapt to real-time demand changes, the feedback of real-time data is crucial. The power grid needs to continuously monitor information such as the power consumption of each user, the power grid load, the power generation, and the energy storage status, etc., in order to adjust the dispatching strategy at each moment. The real-time data feedback enables the power grid dispatching to make optimal decisions based on the latest power grid state at each moment.

[0107] Mathematical model of real-time feedback: ; where: represents the moment the state of the power grid. This state usually includes the power grid load, power demand, power generation status, etc.; represents the function of the power grid state changing with the control strategy, describing how the power grid state changes with the adjustment of the control strategy at the moment This function is usually related to the dynamic model of the power grid; represents the error or perturbation caused by random disturbances or uncertainty factors (such as sudden changes in user behavior, external environment changes, etc.) at the moment Through this term, the model can adapt to external uncertainties and update in real time.

[0108] Through real-time feedback, the system can, based on the current power grid state and the control strategy adopted , timely adjust the dispatching decision of the power grid to ensure the stable operation of the power grid.

[0109] Real-time optimization also needs to ensure the load balance of the power grid and the optimal allocation of resources. The core constraint of power grid dispatching is load balance, that is, the total load of the power grid needs to be equal to the total power generation. Under the load balance constraint, the system needs to, through appropriate dispatching strategies, ensure the balance between power generation and load, and avoid power grid overload or energy waste.

[0110] Mathematical expression of load balance constraint: ; Where: represents the load balance function of the power grid. This function ensures that the total load of the power grid is equal to the power generation, which is a key constraint in power grid scheduling; represents the electricity consumption of the th user at time . The electricity consumption of each user will affect the total load of the power grid; represents the power generation of the power grid at time . The power generation needs to be balanced with the total load to ensure the stable operation of the power grid..

[0111] Through this load balance constraint, the system can ensure that the total load of the power grid matches the power generation at any time, preventing overload or resource waste.

[0112] By updating the Q value, real-time optimization can make optimal decisions according to the current state of the power grid at each moment, ensuring the efficient allocation of power grid resources and load balance. At the same time, the load balance constraint keeps the power grid stable during the dynamic scheduling process, avoiding overload or energy waste.

[0113] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating and analyzing the collective electricity consumption behavior in a multi-user collaborative scenario, characterized in that It includes the following steps: S1. Data collection: Collect the user's electricity consumption history data, geographical location information, number of family members, socioeconomic situation, weather data, and seasonal factors; S2. Data preprocessing: Clean and standardize the collected data, remove outliers, fill in missing values, and standardize the data to ensure data consistency; S3. Data clustering: Use clustering algorithms to group users and classify users with similar electricity consumption behaviors into one group; S4. Behavior modeling: Based on the clustering results of the data, use game theory models to simulate the interaction behaviors among group users and determine the decision-making strategies of each user in group behaviors; S5. Scheduling optimization: Use optimal control theory to optimize the power grid scheduling strategy, minimize the power grid operation cost, and ensure the balance of the power grid load and the optimal allocation of resources; S6. Real-time optimization: Based on reinforcement learning algorithms, adjust the scheduling strategy in a real-time environment and perform dynamic optimization according to changes in user behaviors.

2. The method for simulating and analyzing the collective electricity consumption behavior in a multi-user collaborative scenario according to claim 1, wherein, The data collection includes: Collect the user's electricity consumption behavior data, historical electricity demand, user socioeconomic information, meteorological conditions, and seasonal impact data; Collect and update the user's electricity consumption behavior data in real time, including real-time data feedback from smart meters and home appliance automation systems; Collect environmental information related to the power grid, including weather changes, power load status, and daily electricity consumption patterns.

3. The method for simulating and analyzing the collective electricity consumption behavior for a multi-user collaborative scenario according to claim 1, wherein The data cleaning and standardization processing includes: Clean the collected multi-dimensional data, remove missing values and noisy data, and fill in the missing data; Perform standardization processing on the data, convert data from different sources to a unified scale for subsequent analysis and modeling; Use data normalization techniques to convert different data types into standardized numerical values to ensure data consistency.

4. The group electricity consumption behavior simulation analysis method for multi-user collaborative scenarios according to claim 1, characterized in that, The clustering algorithms include: Use the K-means clustering algorithm to classify user data and classify users with similar electricity consumption behaviors into one category to reduce computational complexity; Based on the clustering results, conduct behavior analysis of user groups and identify the electricity consumption demands and scheduling patterns of different groups.

5. The method for simulating and analyzing the group electricity consumption behavior for a multi-user collaboration scenario according to claim 1, wherein The game theory models include: Use the Nash equilibrium model to simulate the decision-making behaviors of multiple users when facing power grid scheduling pressure; By defining the utility function of each user, describe its decision-making goals and constraints in group electricity consumption behaviors; Simulate the interaction effects among multiple users, optimize the user decision-making strategies through the game process, and calculate the stable points of group electricity consumption behaviors.

6. The method for simulating and analyzing the collective electricity consumption behavior for a multi-user collaboration scenario according to claim 1, wherein The optimal control theory includes: Define the scheduling goal of the power grid, minimize the operation cost of the power grid, and meet the resource constraints of the power grid; Based on the dynamic programming method in optimal control theory, solve the power grid scheduling strategy and dynamically adjust the load distribution; Through the Pontryagin maximum principle, optimize the scheduling strategy to ensure the balance of the power grid load and maximize the energy utilization efficiency.

7. The group electricity consumption behavior simulation analysis method for multi-user collaborative scenarios according to claim 1, characterized in that The reinforcement learning algorithms include: Use the Q-learning algorithm, combined with real-time data feedback, to dynamically adjust the power grid scheduling strategy; By updating the Q-value function, optimize the user electricity consumption behavior and the power grid resource allocation to ensure the operation of the power grid. Introduce a deep Q-network model to enhance the ability of Q-learning and handle high-dimensional state spaces and action spaces.

8. The group electricity consumption behavior simulation analysis method for multi-user collaborative scenarios according to claim 1, characterized in that The optimization of the power grid scheduling strategy includes: Based on the prediction results of the game theory model and the reinforcement learning algorithm, adjust the power grid scheduling strategy in real time; Under the condition of uneven power grid load, dynamically optimize the resource allocation to balance the power grid load; Use the reinforcement learning model to automatically adjust the power consumption strategy according to the real-time changes of the power grid load and optimize the scheduling effect of the power grid.

9. The group electricity consumption behavior simulation analysis method for multi-user collaborative scenarios according to claim 8, characterized in that The real-time feedback mechanism includes: Collect power grid and user power consumption data and update the input of the simulation model in real time; Based on the real-time power grid load, user behavior feedback and environmental changes, automatically adjust the power grid scheduling strategy to cope with load fluctuations and emergencies; Provide a real-time decision support system to ensure that the power grid can respond quickly and optimize the scheduling plan.

10. The method for simulating and analyzing the group electricity consumption behavior for a multi - user collaborative scenario according to claim 9, wherein The decision support system includes: Provide a visual display of the power grid scheduling plan to help power grid managers grasp the scheduling results in real time; Provide intelligent scheduling optimization suggestions and dynamically adjust the power grid based on the simulation model; Through the interactive interface, power grid managers can adjust the power grid strategy according to the real-time feedback.

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