A resource hierarchical partition aggregation system based on multi-element information fusion

Abstract

The present application relates to the technical field of power system, specifically discloses a resource hierarchical zoning aggregation system based on multi-element information fusion, used to solve the problem of how to propose a flexible resource joint clearing model capable of integrating power and carbon trading market requirements, optimize the operation efficiency and economy of the power system, and realize the low-carbon target, comprising a data management and analysis center, a resource characteristic and regulation capacity module, a resource aggregation and distribution module, a market response and clearing module, and an integrated control center, proposing a resource characteristic and regulation capacity model, a power resource fusion and distribution model, and a market response and clearing model; by integrating power and carbon trading market demand, a flexible resource joint clearing model is created, effectively improving the operation efficiency and economy of the power system, and realizing the low-carbon target, through integrated data management, resource regulation analysis, optimized resource allocation, and market response strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more particularly to a resource hierarchical and partitioned aggregation system based on multi-information fusion. Background Art

[0002] In the current electricity market, medium- and long-term transactions ensure the basic power supply needs of the power system, while demand response mechanisms use incentives to adjust grid loads and maintain grid stability during peak demand. Virtual power plants, at their core, integrate diverse power resources through information and communication technologies, not only improving the flexibility and reliability of energy supply but also increasing economic benefits through market transactions. With increasing global attention to carbon emissions, the introduction of a carbon trading market has become inevitable. In this market, the buying and selling of carbon emission rights incentivizes electricity producers and consumers to adopt more environmentally friendly operations. Virtual power plants must consider carbon emission costs when dispatching power resources, which not only influences operational decisions but also provides new opportunities for virtual power plants to generate revenue through carbon credit trading. The operating model of virtual power plants is deeply influenced by both technical and environmental factors. Technical factors (resource startup time, operating curve, and ramping capability) directly influence the formulation of dispatch strategies; power market rules and carbon emission constraints define the boundaries of operation; and environmental factors, especially for renewable energy sources such as wind power and photovoltaics, whose power generation efficiency is highly dependent on weather conditions. Furthermore, effective resource aggregation requires considering not only the operational characteristics of individual resources but also the spatial and temporal distribution and interactions between them. The volatility of renewable energy requires balancing through energy storage facilities or adjustable loads. The geographical distribution of resources and the grid's carrying capacity also directly impact the formulation and implementation of aggregation strategies. The efficient operation of virtual power plants relies on the collection and accurate prediction of real-time data (real-time market prices, actual resource output, and changes in consumer demand), as well as weather forecasts. The integration and analysis of this data is crucial for developing response strategies and optimizing resource scheduling. Therefore, developing a flexible resource joint clearing model that integrates the requirements of electricity and carbon trading markets, optimizes the operational efficiency and economics of the power system, and achieves low-carbon goals, has become a pressing technical challenge. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a resource hierarchical and partitioned aggregation system based on multi-information fusion. It integrates the needs of the electricity and carbon trading markets to create a flexible resource joint clearing model, effectively improves the operating efficiency and economy of the power system, and achieves low-carbon goals. Through integrated data management, resource regulation analysis, optimized resource allocation, and market response strategies, it not only ensures the balance of electricity supply and demand, but also significantly reduces operating costs and carbon emissions, supports the green transformation and sustainable development of the electricity market, and enhances the adaptability and competitiveness of the electricity market.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A resource hierarchical and partitioned aggregation system based on multi-information fusion includes a data management and analysis center, a resource characteristics and regulation capability module, a resource aggregation and allocation module, a market response and clearing module, and an integrated control center. The data management and analysis center collects and processes power equipment performance parameters and historical operation data, extracts useful information from the data using data cleaning, processing, and analysis techniques, predicts future trends in the power market and power resources, provides data to the resource characteristics and regulation capability module to support the setting and updating of model parameters in the resource characteristics and regulation capability module, provides real-time power market and forecast data to the market response and clearing module to adjust the power market clearing strategy, and records and updates the technical characteristics and regulation capability module of each power resource. Technical parameters, analyze the regulation range and speed of each power resource, and evaluate its performance under different market conditions. The resource aggregation and allocation module divides and layers the power resources into zones and layers for aggregation according to the geographical location, connection capacity and regulation characteristics of the power resources, dynamically adjusts the allocation of power resources according to the power market demand and price signals, optimizes the spatial and functional configuration of power resources, and maximizes resource utilization efficiency. The market response and clearing module comprehensively considers power demand, supply capacity and carbon emission costs, clears the power market, adjusts the clearing strategy according to real-time market information and forecast data, optimizes the balance of power supply and demand, and the integrated control center monitors the system status and power resource allocation effect in real time to ensure that the system operates in the optimal state and provides decision-making recommendations based on data and model analysis.

[0006] As a further solution of the present invention, in the resource characteristics and regulation capability module, the resource characteristics and regulation capability model is used to describe and analyze the dynamic behavior of the real-time regulation capability changes of individual resources and resource groups in the power system. The response time constant and regulation capability increment are used to quantify the regulation behavior of power resources under external control signals or internal state changes, providing analytical data for the scheduling and management of the power system. The formula of the resource characteristics and regulation capability model is:

[0007]

[0008] Where: is the current time, For in time The power resource regulation capability is the amount of electricity that can be changed within a period of time (unit: megawatt). is the basic regulation capacity, i.e. the consumption level of the power resource without any external regulation input (unit, megawatt), is the increment of regulation capability, that is, the regulation range that the power resource can reach after receiving the command to change the output. is the resource response time constant, i.e. the time required for the power resource to reach a new steady state, and Obtained through technical data provided by the equipment manufacturer, Obtained through real-time monitoring of the equipment response process.

[0009] As a further solution of the present invention, in the resource aggregation and allocation module, through the power resource fusion and allocation model, based on the initial power distribution and basic power resource regulation capability, for each time step, considering the power resource regulation input and market demand changes, using the differential method to update the power distribution with respect to location and time, the power resource regulation capability with respect to time, the power source item and load based on market forecast and implementation demand data, simulate the power flow and distribution process from the supply point to the demand point in the power network, apply the optimization algorithm to optimize resource allocation, reduce costs and meet power and carbon emission targets, the formula of the power resource fusion and allocation model is:

[0010]

[0011] Where: is the location of the key nodes of the power grid, About location and time The distribution of electricity, is the initial position The power distribution is collected through the power company's database and sensors installed at key nodes of the power grid. The efficiency of power transmission is obtained by analyzing the historical data of power grid operation. The source and load of electricity are obtained through market operation data and forecast models. From the initial moment to time The power distribution is affected by the cumulative effects of transmission efficiency and spatial distribution changes, taking into account the power loss during transmission and the diffusion effect of distribution.

[0012] As a further solution of the present invention, in the resource aggregation and allocation module, the power resource fusion and allocation model is obtained The process includes:

[0013] Step 1: Initialization and data preparation: Collect initial power distribution data from the power company’s database and sensors installed at key nodes of the power grid. , analyzing power transmission efficiency by analyzing historical data of power grid operation , used to analyze the loss and propagation speed of electricity in the network, and use market operation data and forecasting models to obtain electricity demand and supply conditions, including generation data and consumption data from various power resources;

[0014] Step 2: Numerical discretization: Setting the space and time The formula of the step-size discretized power resource integration and allocation model uses the forward Euler method and the central difference method to equate the time derivative and the second-order spatial derivative, where the time derivative is equivalent to , the spatial second-order derivative is equivalent to ;

[0015] Step 3: Solve the partial differential equation: Use the trapezoidal method to calculate the integral term from the initial moment to the current time. For each time step , update the power distribution using the power distribution of the previous time step and the current source terms and loads, i.e. ;

[0016] Step 4: Apply the optimization algorithm: Define the objective function of the optimization problem as minimizing operating costs and carbon emissions while satisfying power demand and operational constraints. Use a linear programming optimization algorithm to determine the optimal configuration of power resources at each time step.

[0017] Step 5: Analysis and Adjustment: Analyze the changes in power distribution, the efficiency and cost of power resource allocation, and the reduction of carbon emissions. Based on actual operating conditions and market feedback, adjust model parameters and optimization algorithms to improve the accuracy and adaptability of the model.

[0018] As a further solution of the present invention, in step 1, initial power distribution data is collected from the power company's database and sensors installed at key nodes of the power grid. The process includes:

[0019] Step 1: Equipment and sensor deployment: Identify key transmission lines, substations, and power stations in the power grid. Install current, voltage, and frequency sensors at these nodes to measure and record power flow data in real time.

[0020] Step 2: Get an immediate view of power distribution through real-time power flow data monitored by sensors. The sensor data is automatically recorded in the local control unit or directly transmitted to the central database system.

[0021] Step 3: Data collection and transmission: The collected data is transmitted to the power company's central database system via a secure network connection, using encryption technology to ensure data security during transmission. The central database system receives data streams from various sensors, performs preliminary integration and storage, cleans the collected data, eliminates erroneous readings and outliers, and synchronizes data from different sensors and timestamps.

[0022] Step 4, Initial Data Analysis: Use historical data and preliminary collected data to establish a baseline for power distribution , used to reflect the normal operating status of the power grid when there is no external intervention, analyze trends and patterns in the data, and identify typical behaviors and potential problems of the power grid;

[0023] Step 5, data integration and access: Display the power distribution data through charts and maps to ensure that each module of the system can access and use this initial power distribution data.

[0024] As a further solution of the present invention, in step 1, the power transmission efficiency is analyzed by analyzing the historical data of the power grid operation. The process includes:

[0025] Collect historical data and perform data preprocessing: Select historical operational data directly related to power transmission, including transmission line load data, transmission losses, equipment efficiency records, and maintenance records. Ensure that the selected data covers grid operation in different seasons, load conditions, and weather conditions. Remove obvious erroneous data and outliers, handle missing values, and standardize data from different sources and formats.

[0026] Parameter estimation: Use linear regression to analyze the relationship between transmission loss and load, determine the impact of load switching on transmission loss, and construct a transmission loss model;

[0027] Model calibration: Adjust and optimize the model based on actual observed transmission loss data to ensure that the model accurately reflects actual operating conditions. Cross-validate the model using historical data from different time periods to test the model's stability and predictive capabilities.

[0028] Output And continuously monitor and adjust: according to the power transmission efficiency extracted from the optimized transmission loss model , according to the newly obtained Update the power resource integration and allocation model, and regularly review and update power transmission efficiency , considering the impact of grid equipment aging and environmental changes, adjust the grid operation strategy according to the new analysis results and optimize the allocation and utilization of power resources.

[0029] As a further solution of the present invention, in step 1, the specific process of using market operation data and the forecasting model to obtain the power demand and supply situation includes:

[0030] Data Collection: Collect real-time supply and demand data from power market operators, including power consumption, power generation, and power transaction prices. Integrate historical supply and demand data, including seasonal variations, differences between working days and non-working days, and the impact of emergencies on power demand. Detailed records of historical power generation data and availability of various power generation resources are kept. Weather data related to power supply and demand is collected.

[0031] Demand forecasting: Using historical data to build machine learning models to predict electricity demand based on weather conditions, economic activity, and population growth, the models are cross-validated using historical datasets and continuously adjusted and optimized based on the latest market data and technological developments.

[0032] Supply analysis: Analyze the maximum power generation capacity and availability of various power generation resources, including the impact of maintenance and upgrade plans on power generation capacity, evaluate the power generation reliability of various resources under different weather and technical conditions, optimize the power generation resource portfolio based on power generation costs, environmental impact and market demand, and evaluate the flexibility and stability of different power generation portfolios in responding to demand fluctuations and market changes.

[0033] As a further solution of the present invention, in step 4, the step of using a linear programming optimization algorithm to determine the optimal configuration of power resources in each time step includes:

[0034] Define the optimization problem: The optimization goal is to minimize the overall operating cost and carbon emissions. The objective function is in the form of ,in For in time and location The unit electricity cost, For in time and location of electricity production, For in time and location The unit carbon emission cost, For in time and location Carbon emissions, establish supply and demand balance constraints, resource capacity constraints and environmental constraints, where the supply and demand balance constraints ensure that at any time step and any location , the electricity supplied meets local demand, resource capacity constraints ensure that the output of each resource cannot exceed the maximum capacity and minimum operating level, and environmental constraints are the carbon emission cap and the minimum proportion of renewable energy use;

[0035] Linear programming model construction and solution: define the power generation of each resource at each time point as the decision variable, determine the technical parameters related to the decision variables as the power generation efficiency, power generation cost, and availability of each resource, ensure that all constraints and objective functions meet the requirements of linear programming, perform piecewise linearization on nonlinear elements, apply the linear programming solver to input model data and parameters, run the solution process, check the feasibility and effectiveness of the solution results, conduct sensitivity analysis, evaluate the impact of changes in key parameters on the optimization results, analyze cost savings, carbon emission reduction and resource utilization, convert the optimization results into specific operation and scheduling strategies, implement them in power system operations, and regularly update model parameters and re-optimize based on actual operating data and market changes.

[0036] As a further solution of the present invention, in the market response and clearing module, the market response and clearing model comprehensively considers electricity demand, supply capacity and carbon emission costs to clear the electricity market, adjusts the clearing strategy based on real-time market information and forecast data, and optimizes the balance between electricity supply and demand. The formula of the market response and clearing model is:

[0037]

[0038] Where: is the total cost, including electricity supply cost and carbon emission cost, For in time The unit electricity supply cost, For in time The amount of electricity supply, For in time The unit carbon emission cost, For in time of carbon emissions, is the total time period considered, and Obtained from electricity market operation data, and Obtained from carbon trading market data and emission data from relevant environmental protection agencies.

[0039] As a further solution of the present invention, in the market response and clearing module, the process of solving the market response and clearing model includes:

[0040] Data integration: Integrate real-time data from electricity and carbon markets to ensure 、 、 and accuracy and timeliness;

[0041] Cost Calculation: Calculate the total cost for a given time period using Simpson's method ;

[0042] Optimization strategy: Apply linear programming optimization algorithm to adjust and resource allocation to minimize , taking into account carbon emission limits and market demand, optimize carbon emissions To meet environmental standards.

[0043] Compared with the existing technology, the present invention proposes the following system technical effects: the present invention integrates and processes performance parameters and historical operation data from power equipment through the data management and analysis center to effectively predict future trends of the power market and resources. The resource characteristics and regulation capability module further analyzes the regulation range and speed of resources to provide a scientific basis for the flexible scheduling of power resources. The resource aggregation and allocation module optimizes resource allocation and maximizes utilization efficiency according to market demand and price signals, while the market response and clearing module comprehensively integrates power demand, supply capacity and carbon emission costs to clear the market and ensure the balance of power supply and demand. The integrated control center comprehensively monitors the system operation to ensure efficient collaborative operation among modules, which not only improves the adaptability and competitiveness of the power market, but also significantly reduces operating costs and carbon emissions by optimizing resource allocation and scheduling strategies, enhances the sustainability of the power system, provides strong technical support for the modernization and green transformation of the power market, and effectively solves the integration problem of power system operation efficiency, economy and low-carbon goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The following is a system block diagram of the system proposed in the present invention. DETAILED DESCRIPTION

[0045] The following is a clear and complete description of the technical solutions of the present invention, in conjunction with the accompanying drawings. Obviously, the technical solutions described are only part of the present invention. All other technical solutions derived by persons of ordinary skill in the art based on the technical solutions of the present invention without inventive effort fall within the scope of protection of the present invention.

[0046] like Figure 1As shown, the present invention proposes a resource hierarchical and partitioned aggregation system based on multi-information fusion, including a data management and analysis center, a resource characteristics and regulation capability module, a resource aggregation and allocation module, a market response and clearing module, and an integrated control center. The data management and analysis center collects and processes power equipment performance parameters and historical operation data, uses data cleaning, processing and analysis technology to extract useful information from the data, predicts the future trend of the power market and power resources, provides data to the resource characteristics and regulation capability module to support the setting and updating of model parameters in the resource characteristics and regulation capability module, provides real-time power market and forecast data to the market response and clearing module to adjust the power market clearing strategy, and the resource characteristics and regulation capability module records and updates each power The technical parameters of resources are analyzed, the regulation range and speed of each power resource are analyzed, and its performance is evaluated under different market conditions. The resource aggregation and allocation module divides and layers the power resources into zones and layers for aggregation according to the geographical location, connection capacity and regulation characteristics of the power resources. The power resource allocation is dynamically adjusted according to the power market demand and price signals, the spatial and functional configuration of power resources is optimized, and the resource utilization efficiency is maximized. The market response and clearing module comprehensively considers power demand, supply capacity and carbon emission costs, clears the power market, adjusts the clearing strategy according to real-time market information and forecast data, optimizes the balance of power supply and demand, and the integrated control center monitors the system status and power resource allocation effect in real time to ensure that the system operates in the optimal state and provides decision-making recommendations based on data and model analysis.

[0047] The system proposed in the present invention integrates and processes performance parameters and historical operating data from power equipment through a data management and analysis center to effectively predict future trends in the power market and resources. The resource characteristics and regulation capability module further analyzes the regulation range and speed of resources to provide a scientific basis for the flexible scheduling of power resources. The resource aggregation and allocation module optimizes resource allocation and maximizes utilization efficiency based on market demand and price signals, while the market response and clearing module comprehensively considers power demand, supply capacity and carbon emission costs to clear the market and ensure the balance of power supply and demand. The integrated control center comprehensively monitors the system operation to ensure efficient collaboration between modules. This not only improves the adaptability and competitiveness of the power market, but also significantly reduces operating costs and carbon emissions by optimizing resource allocation and scheduling strategies, enhances the sustainability of the power system, and provides strong technical support for the modernization and green transformation of the power market, effectively solving the problem of integrating power system operation efficiency, economy and low-carbon goals.

[0048] It should be noted that in the resource characteristics and regulation capability module, the resource characteristics and regulation capability model is used to describe and analyze the dynamic behavior of the real-time regulation capability changes of individual resources and resource groups in the power system. The response time constant and regulation capability increment are used to quantify the regulation behavior of power resources under external control signals or internal state changes, providing analytical data for the dispatch and management of the power system. The formula of the resource characteristics and regulation capability model is:

[0049]

[0050] Where: is the current time, For in time The power resource regulation capability is the amount of electricity that can be changed within a period of time (unit: megawatt). is the basic regulation capacity, i.e. the consumption level of the power resource without any external regulation input (unit, megawatt), is the increment of regulation capability, that is, the regulation range that the power resource can reach after receiving the command to change the output. is the resource response time constant, i.e. the time required for the power resource to reach a new steady state, and Obtained through technical data provided by the equipment manufacturer, Obtained through real-time monitoring of the equipment response process.

[0051] Resource characterization and regulation capacity models play a key role in power system management and optimization, especially when integrating variable power resources such as wind and solar. By analyzing the real-time regulation capacity changes of each power resource, this model provides crucial technical support, helping the grid respond more effectively to load changes and external control signals. By leveraging response time constants and regulation capacity increments, dispatchers can predict and plan resource usage and optimize real-time dispatch to address demand fluctuations, improving system stability and reliability, reducing energy waste, and lowering operating costs. In a power market environment characterized by medium- and long-term trading and demand response mechanisms, the data provided by the model enables the grid to flexibly respond to market and operational challenges such as peak demand or volatile renewable energy output. With the global focus on reducing carbon emissions, resource characterization and regulation capacity models help operators optimize the use of low-carbon resources, reduce carbon emissions through precise scheduling, and promote opportunities for carbon credit trading. The model supports the effective integration of new technologies and renewable energy into the power system, optimizes their dispatch, and ensures a sustainable energy supply and an environmentally friendly system.

[0052] It should be noted that in the resource aggregation and allocation module, through the power resource fusion and allocation model, based on the initial power distribution and basic power resource regulation capability, for each time step, considering the power resource regulation input and market demand changes, the differential method is used to update the power distribution with respect to location and time, the power resource regulation capability with respect to time, the power source and load based on market forecast and implementation demand data, and the power flow and distribution process from the supply point to the demand point in the power network is simulated. The optimization algorithm is applied to optimize resource allocation, reduce costs and meet power and carbon emission targets. The formula of the power resource fusion and allocation model is:

[0053]

[0054] Where: is the location of the key nodes of the power grid, About location and time The distribution of electricity, is the initial position The power distribution is collected through the power company's database and sensors installed at key nodes of the power grid. The efficiency of power transmission is obtained by analyzing the historical data of power grid operation. The source and load of electricity are obtained through market operation data and forecast models. From the initial moment to time The power distribution is affected by the cumulative effects of transmission efficiency and spatial distribution changes, taking into account the power loss during transmission and the diffusion effect of distribution.

[0055] The purpose of establishing such a power resource integration and allocation model is to accurately control and optimize the flow and distribution of electricity in the power network, especially in the context of complex market environments and changing demand. The core principle of the model involves the dynamic management of electricity to ensure the efficiency and reliability of energy supply while meeting economic and environmental goals. The demand in the power market is dynamic and is affected by various factors such as weather, economic activities and seasonal changes. The model helps adjust power output to match these changes by simulating the real-time distribution of electricity. As the proportion of renewable energy increases, the power network must manage the intermittency and instability of these energy sources. The model enables the power grid to more effectively integrate renewable energy such as wind and solar energy, ensuring stable power supply through dynamic adjustment. Against the backdrop of increasing cost and environmental pressures, the model helps reduce operating costs and carbon emissions by optimizing the configuration and utilization of power resources. Through precise scheduling and resource allocation, the model improves the power grid's ability to respond to emergencies such as equipment failures or extreme weather conditions, enhancing the overall resilience of the system.

[0056] The model is based on partial differential equations to describe the changes in power in space and time. The calculation of the spatial second-order derivative and time derivative of simulates the flow and diffusion of electricity in the power grid. The model uses the diffusion process to describe the transmission efficiency and loss of electricity. Here, Represents the loss rate of electricity during transmission, affects how electricity is distributed and diffused in space, and applies linear programming techniques to optimize the allocation of power resources to ensure that while meeting demand, operating costs and environmental impacts are minimized. Power distribution refers to the state of power at a specific point or region in a power grid over a period of time, including the amount of power supplied and consumed. It describes the flow of power from power stations through the transmission network to the consumer, including the amount of power (e.g., megawatts) and the direction (direction of power flow). Power distribution also involves power quality factors, such as frequency and voltage stability. In a power grid, a stable power supply depends not only on quantity but also on its quality, including stable frequency and appropriate voltage levels, which are critical to maintaining safe and efficient grid operation. The spatial characteristics of power distribution refer to how power is distributed across different geographic locations throughout the grid. For example, the central area of ​​a large city has high power demand, while remote areas have lower demand. The spatial characteristics of power distribution are crucial for power grid planning and management, and the rational layout of transmission lines and substations is one of the key tasks. The temporal dynamics of power distribution describe how power demand and supply vary over time. Residential areas experience increased power demand at night, while industrial areas experience higher demand during working hours. Power grids must be able to cope with these daily and seasonal variations in demand to ensure a continuous and reliable power supply.

[0057] It should be noted that in the resource aggregation and allocation module, the power resource fusion and allocation model acquisition The process includes:

[0058] Step 1: Initialization and data preparation: Collect initial power distribution data from the power company’s database and sensors installed at key nodes of the power grid. , analyzing power transmission efficiency by analyzing historical data of power grid operation , used to analyze the loss and propagation speed of electricity in the network, and use market operation data and forecasting models to obtain electricity demand and supply conditions, including generation data and consumption data from various power resources;

[0059] Step 2: Numerical discretization: Setting the space and time The formula of the step-size discretized power resource integration and allocation model uses the forward Euler method and the central difference method to equate the time derivative and the second-order spatial derivative, where the time derivative is equivalent to , the spatial second-order derivative is equivalent to ;

[0060] Step 3: Solve the partial differential equation: Use the trapezoidal method to calculate the integral term from the initial moment to the current time. For each time step , update the power distribution using the power distribution of the previous time step and the current source terms and loads, i.e. ;

[0061] Step 4: Apply the optimization algorithm: Define the objective function of the optimization problem as minimizing operating costs and carbon emissions while satisfying power demand and operational constraints. Use a linear programming optimization algorithm to determine the optimal configuration of power resources at each time step.

[0062] Step 5: Analysis and Adjustment: Analyze the changes in power distribution, the efficiency and cost of power resource allocation, and the reduction of carbon emissions. Based on actual operating conditions and market feedback, adjust model parameters and optimization algorithms to improve the accuracy and adaptability of the model.

[0063] The five-step power resource integration and allocation model described above enables efficient management and optimization of power distribution, allowing the system to dynamically adjust power resources in a scientific and precise manner to match real-time market demand and supply. While also considering economic and environmental factors and employing numerical discretization and optimization algorithms, this model not only optimizes the use of power resources, reduces energy waste and operating costs, but also reduces carbon emissions, supporting sustainable development goals. Furthermore, the data and analysis provided by the model help enhance the resilience of the power grid, prevent and respond to system imbalances, ensure the stability and reliability of power supply, and thus enhance the adaptability and competitiveness of the entire power market.

[0064] It should be noted that in step 1, initial power distribution data is collected from the power company's database and sensors installed at key nodes of the power grid. The process includes:

[0065] Step 1: Equipment and sensor deployment: Identify key transmission lines, substations, and power stations in the power grid. Install current, voltage, and frequency sensors at these nodes to measure and record power flow data in real time.

[0066] Step 2: Get an immediate view of power distribution through real-time power flow data monitored by sensors. The sensor data is automatically recorded in the local control unit or directly transmitted to the central database system.

[0067] Step 3: Data collection and transmission: The collected data is transmitted to the power company's central database system via a secure network connection, using encryption technology to ensure data security during transmission. The central database system receives data streams from various sensors, performs preliminary integration and storage, cleans the collected data, eliminates erroneous readings and outliers, and synchronizes data from different sensors and timestamps.

[0068] Step 4, Initial Data Analysis: Use historical data and preliminary collected data to establish a baseline for power distribution , used to reflect the normal operating status of the power grid when there is no external intervention, analyze trends and patterns in the data, and identify typical behaviors and potential problems of the power grid;

[0069] Step 5, data integration and access: Display the power distribution data through charts and maps to ensure that each module of the system can access and use this initial power distribution data.

[0070] By deploying sensors at key nodes and monitoring power flow in real time, power companies can obtain real-time and detailed data on grid operations, making grid operations more transparent and management more precise, helping to promptly identify and resolve grid problems and improve overall operational efficiency. The power system can allocate resources and manage loads more efficiently, which not only helps reduce energy waste, but also optimizes the balance between power supply and demand, reducing the pressure on the power grid. Systematic data collection and analysis provide in-depth insights into power grid performance, enabling power companies to prevent potential power grid failures and respond to emergencies, thereby enhancing the overall resilience and reliability of the power grid, making more scientific decision-making support, and formulating more effective market strategies and operation plans. Data-driven decision-making not only improves the effectiveness of strategies, but also reduces the risks based on intuition or incomplete information. In the context of global carbon reduction, accurate power flow data helps companies monitor and manage their carbon emissions to ensure compliance with relevant regulations.

[0071] It should be noted that in step 1, the power transmission efficiency is analyzed by analyzing the historical data of power grid operation. The process includes:

[0072] Collect historical data and perform data preprocessing: Select historical operational data directly related to power transmission, including transmission line load data, transmission losses, equipment efficiency records, and maintenance records. Ensure that the selected data covers grid operation in different seasons, load conditions, and weather conditions. Remove obvious erroneous data and outliers, handle missing values, and standardize data from different sources and formats.

[0073] Parameter estimation: Use linear regression to analyze the relationship between transmission loss and load, determine the impact of load switching on transmission loss, and construct a transmission loss model;

[0074] Model calibration: Adjust and optimize the model based on actual observed transmission loss data to ensure that the model accurately reflects actual operating conditions. Cross-validate the model using historical data from different time periods to test the model's stability and predictive capabilities.

[0075] Output And continuously monitor and adjust: according to the power transmission efficiency extracted from the optimized transmission loss model , according to the newly obtained Update the power resource integration and allocation model, and regularly review and update power transmission efficiency , considering the impact of grid equipment aging and environmental changes, adjust the grid operation strategy according to the new analysis results and optimize the allocation and utilization of power resources.

[0076] Analyze and optimize power transmission efficiency through the above detailed process , the power system can achieve more precise power management and dispatch. This approach not only improves the energy efficiency and reliability of the power grid, but also helps to reduce operating costs and energy waste. Regular update and optimization It also ensures that the power grid can adapt to equipment aging and environmental changes, further optimize resource allocation, and support the sustainable development of the power grid.

[0077] It should be noted that in step 1, the specific process of using market operation data and forecasting models to obtain electricity demand and supply conditions includes:

[0078] Data Collection: Collect real-time supply and demand data from power market operators, including power consumption, power generation, and power transaction prices. Integrate historical supply and demand data, including seasonal variations, differences between working days and non-working days, and the impact of emergencies on power demand. Detailed records of historical power generation data and availability of various power generation resources are kept. Weather data related to power supply and demand is collected.

[0079] Demand forecasting: Using historical data to build machine learning models to predict electricity demand based on weather conditions, economic activity, and population growth, the models are cross-validated using historical datasets and continuously adjusted and optimized based on the latest market data and technological developments.

[0080] Supply analysis: Analyze the maximum power generation capacity and availability of various power generation resources, including the impact of maintenance and upgrade plans on power generation capacity, evaluate the power generation reliability of various resources under different weather and technical conditions, optimize the power generation resource portfolio based on power generation costs, environmental impact and market demand, and evaluate the flexibility and stability of different power generation portfolios in responding to demand fluctuations and market changes.

[0081] By capturing and analyzing electricity demand and supply through this comprehensive process, power companies can accurately forecast market demand and optimize the allocation of generation resources, thereby improving energy efficiency and system economics. The use of real-time and historical data enhances the accuracy of forecasting models, making grid operations more flexible and responsive, while ensuring the stability and reliability of power supply and reducing the risk of unplanned outages and resource waste.

[0082] It should be noted that in step 4, the steps of using the linear programming optimization algorithm to determine the optimal configuration of power resources in each time step include:

[0083] Define the optimization problem: The optimization goal is to minimize the overall operating cost and carbon emissions. The objective function is in the form of ,in For in time and location The unit electricity cost, For in time and location of electricity production, For in time and location The unit carbon emission cost, For in time and location Carbon emissions, establish supply and demand balance constraints, resource capacity constraints and environmental constraints, where the supply and demand balance constraints ensure that at any time step and any location , the electricity supplied meets local demand, resource capacity constraints ensure that the output of each resource cannot exceed the maximum capacity and minimum operating level, and environmental constraints are the carbon emission cap and the minimum proportion of renewable energy use;

[0084] Linear programming model construction and solution: Define the power generation of each resource at each time point as the decision variable, determine the technical parameters related to the decision variable as the power generation efficiency, power generation cost, and availability of each resource, ensure that all constraints and objective functions meet the requirements of linear programming, perform piecewise linearization on non-linear elements, input model data and parameters using a linear programming solver, run the solution process, check the feasibility and effectiveness of the solution, perform sensitivity analysis to evaluate the impact of key parameter changes on the optimization results, analyze cost savings, carbon emission reductions, and resource utilization, convert the optimization results into specific operation and scheduling strategies, implement them in the power system operation, and regularly update model parameters and re-optimize based on actual operation data and market changes.

[0085] Using linear programming optimization algorithms to determine the optimal configuration of power resources at each time step brings multiple benefits: first, it minimizes operating costs and carbon emissions, supporting environmental sustainability; second, it ensures precise balance between power supply and demand, improving the stability and efficiency of the power grid. In addition, by continuously optimizing and updating the model, the power system can flexibly respond to market and environmental changes, enhancing its adaptability and competitiveness, thereby effectively improving the economic and environmental performance of the entire power system.

[0086] It should be noted that in the market response and clearing module, the market response and clearing model comprehensively considers power demand, supply capacity, and carbon emission cost to clear the power market, adjusts the clearing strategy based on real-time market information and forecast data, optimizes power supply and demand balance, and the formula of the market response and clearing model is:

[0087]

[0088] In the formula: is the total cost, including power supply cost and carbon emission cost, is the unit power supply cost at time , is the power supply amount at time , is the unit carbon emission cost at time , is the carbon emission amount at time , is the total time period considered, and are obtained from power market operation data, and are obtained from carbon trading market data and related environmental protection agencies' emission data.

[0089] By using market response and clearing models to integrate electricity demand, supply and carbon emission costs, the electricity market can effectively achieve clearing, optimize the balance of electricity supply and demand, and increase the economy and environmental sustainability of the power system. By incorporating carbon emission costs into the decision-making process, the use of more environmentally friendly energy solutions is encouraged. The model's real-time data analysis and prediction capabilities ensure a rapid response to market changes, improve market efficiency, reduce operating costs, and promote investment in low-carbon technologies and clean energy, thereby supporting the realization of global carbon emission reduction goals.

[0090] It should be noted that in the market response and clearing module, the solution process of the market response and clearing model includes:

[0091] Data integration: Integrate real-time data from electricity and carbon markets to ensure 、 、 and accuracy and timeliness;

[0092] Cost Calculation: Calculate the total cost for a given time period using Simpson's method ;

[0093] Optimization strategy: Apply linear programming optimization algorithm to adjust and resource allocation to minimize , taking into account carbon emission limits and market demand, optimize carbon emissions To meet environmental standards.

[0094] By solving market response and clearing models, electricity markets can effectively integrate real-time data and ensure accurate decision-making. Using mathematical methods such as the Simpson method and linear programming to optimize power supply and carbon emissions helps minimize costs and meet environmental standards. This not only improves the market's economic efficiency, but also optimizes resource utilization, supports sustainable development goals, and enhances the power system's adaptability to market changes.

[0095] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0096] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A resource layered and partitioned aggregation system based on multi-information fusion, characterized by: It includes a data management and analysis center, a resource characteristics and regulation capability module, a resource aggregation and allocation module, a market response and clearing module, and an integrated control center. The data management and analysis center collects and processes power equipment performance parameters and historical operating data, uses data cleaning, processing and analysis techniques to extract useful information from the data, predicts future trends in the power market and power resources, and provides data to the resource characteristics and regulation capability module to support the setting and updating of model parameters in the resource characteristics and regulation capability module. It provides real-time power market and forecast data to the market response and clearing module to adjust the power market clearing strategy. The resource characteristics and regulation capability module records and updates the technical parameters of each power resource. , analyze the regulation range and speed of each power resource, and evaluate its performance under different market conditions. The resource aggregation and allocation module divides and layers the power resources into zones and layers for aggregation according to the geographical location, connection capacity and regulation characteristics of the power resources. It dynamically adjusts the allocation of power resources according to the power market demand and price signals, optimizes the spatial and functional configuration of power resources, and maximizes resource utilization efficiency. The market response and clearing module comprehensively considers power demand, supply capacity and carbon emission costs, clears the power market, adjusts the clearing strategy according to real-time market information and forecast data, optimizes the balance of power supply and demand, and the integrated control center monitors the system status and power resource allocation effect in real time to ensure that the system operates in the optimal state; In the resource characteristics and regulation capability module, the resource characteristics and regulation capability model is used to describe and analyze the dynamic behavior of the real-time regulation capability changes of individual resources and resource groups in the power system. The response time constant and regulation capability increment are used to quantify the regulation behavior of power resources under external control signals or internal state changes, providing analytical data for the dispatch and management of the power system. The formula of the resource characteristics and regulation capability model is: Where: is the current time, For in time The power resource regulation capability, that is, the amount of electricity that the power resource can change within a period of time, The basic regulation capability is the consumption level of power resources without any external regulation input. is the increment of regulation capability, that is, the regulation range that the power resource can reach after receiving the command to change the output. is the resource response time constant, i.e. the time required for the power resource to reach a new steady state, and Obtained through technical data provided by the equipment manufacturer, Obtained through real-time monitoring of the equipment response process; In the resource aggregation and allocation module, the power resource fusion and allocation model is used. Based on the initial power distribution and basic power resource regulation capabilities, for each time step, the power resource regulation input and market demand changes are considered. The differential method is used to update the power distribution with respect to location and time, the power resource regulation capabilities with respect to time, the power source items and loads based on market forecasts and implementation demand data. The power flow and distribution process from the supply point to the demand point in the power network is simulated. The optimization algorithm is applied to optimize resource allocation, reduce costs and meet power and carbon emission targets. The formula of the power resource fusion and allocation model is: Where: is the location of the key nodes of the power grid, About location and time The distribution of electricity, is the initial position The power distribution is collected through the power company's database and sensors installed at key nodes of the power grid. The efficiency of power transmission is obtained by analyzing the historical data of power grid operation. The source and load of electricity are obtained through market operation data and forecast models. From the initial moment to time The cumulative impact of power distribution changes due to transmission efficiency and spatial distribution is considered, taking into account the power loss during transmission and the diffusion effect of distribution. In the resource aggregation and allocation module, the power resource fusion and allocation model is obtained The process includes: Step 1: Initialization and data preparation: Collect initial power distribution data from the power company’s database and sensors installed at key nodes of the power grid. , analyzing power transmission efficiency by analyzing historical data of power grid operation , used to analyze the loss and propagation speed of electricity in the network, and use market operation data and forecasting models to obtain electricity demand and supply conditions, including generation data and consumption data from various power resources; Step 2: Numerical discretization: Setting the space and time The formula of the step-size discretized power resource integration and allocation model uses the forward Euler method and the central difference method to equate the time derivative and the second-order spatial derivative, where the time derivative is equivalent to , the spatial second-order derivative is equivalent to ; Step 3: Solve the partial differential equation: Use the trapezoidal method to calculate the integral term from the initial moment to the current time. For each time step , update the power distribution using the power distribution of the previous time step and the current source terms and loads, i.e. ; Step 4: Apply the optimization algorithm: Define the objective function of the optimization problem as minimizing operating costs and carbon emissions while satisfying power demand and operational constraints. Use a linear programming optimization algorithm to determine the optimal configuration of power resources at each time step. Step 5: Analysis and Adjustment: Analyze the changes in power distribution, the efficiency and cost of power resource allocation, and the reduction of carbon emissions. Based on actual operating conditions and market feedback, adjust model parameters and optimization algorithms to improve the accuracy and adaptability of the model.

2. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 1 is characterized in that: In step 1, initial power distribution data is collected from the power company’s database and sensors installed at key nodes of the power grid. The process includes: Step 1: Equipment and sensor deployment: Identify key transmission lines, substations, and power stations in the power grid. Install current, voltage, and frequency sensors at these nodes to measure and record power flow data in real time. Step 2: Get an immediate view of power distribution through real-time power flow data monitored by sensors. The sensor data is automatically recorded in the local control unit or directly transmitted to the central database system; Step 3: Data collection and transmission: The collected data is transmitted to the power company's central database system via a secure network connection, using encryption technology to ensure data security during transmission. The central database system receives data streams from various sensors, performs preliminary integration and storage, cleans the collected data, eliminates erroneous readings and outliers, and synchronizes data from different sensors and timestamps. Step 4, Initial Data Analysis: Use historical data and preliminary collected data to establish a baseline for power distribution , used to reflect the normal operating status of the power grid when there is no external intervention, analyze trends and patterns in the data, and identify typical behaviors and potential problems of the power grid; Step 5, data integration and access: Display the power distribution data through charts and maps to ensure that each module of the system can access and use this initial power distribution data.

3. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 1 is characterized in that: In step 1, the power transmission efficiency is analyzed by analyzing the historical data of the grid operation. The process includes: Collect historical data and perform data preprocessing: Select historical operational data directly related to power transmission, including transmission line load data, transmission losses, equipment efficiency records, and maintenance records. Ensure that the selected data covers grid operation in different seasons, load conditions, and weather conditions. Remove obvious erroneous data and outliers, handle missing values, and standardize data from different sources and formats. Parameter estimation: Use linear regression to analyze the relationship between transmission loss and load, determine the impact of load switching on transmission loss, and construct a transmission loss model; Model calibration: Adjust and optimize the model based on actual observed transmission loss data to ensure that the model accurately reflects actual operating conditions. Cross-validate the model using historical data from different time periods to test the model's stability and predictive capabilities. Output And continuously monitor and adjust: according to the power transmission efficiency extracted from the optimized transmission loss model , according to the newly obtained Update the power resource integration and allocation model, and regularly review and update power transmission efficiency , considering the impact of grid equipment aging and environmental changes, adjust the grid operation strategy according to the new analysis results and optimize the allocation and utilization of power resources.

4. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 1 is characterized in that: In step 1, the specific process of using market operation data and forecasting models to obtain electricity demand and supply conditions includes: Data Collection: Collect real-time supply and demand data from power market operators, including power consumption, power generation, and power transaction prices. Integrate historical supply and demand data, including seasonal variations, differences between working days and non-working days, and the impact of emergencies on power demand. Detailed records of historical power generation data and availability of various power generation resources are kept. Weather data related to power supply and demand is collected. Demand forecasting: Using historical data to build machine learning models to predict electricity demand based on weather conditions, economic activity, and population growth, the models are cross-validated using historical datasets and continuously adjusted and optimized based on the latest market data and technological developments. Supply analysis: Analyze the maximum power generation capacity and availability of various power generation resources, including the impact of maintenance and upgrade plans on power generation capacity, evaluate the power generation reliability of various resources under different weather and technical conditions, optimize the power generation resource portfolio based on power generation costs, environmental impact and market demand, and evaluate the flexibility and stability of different power generation portfolios in responding to demand fluctuations and market changes.

5. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 1 is characterized in that: In step 4, the steps of using a linear programming optimization algorithm to determine the optimal configuration of power resources in each time step include: Define the optimization problem: The optimization goal is to minimize the overall operating cost and carbon emissions. The objective function is in the form of ,in For in time and location The unit electricity cost, For in time and location of electricity production, For in time and location The unit carbon emission cost, For in time and location Carbon emissions, establish supply and demand balance constraints, resource capacity constraints and environmental constraints, where the supply and demand balance constraints ensure that at any time step and any location , the electricity supplied meets local demand, resource capacity constraints ensure that the output of each resource cannot exceed the maximum capacity and minimum operating level, and environmental constraints are the carbon emission cap and the minimum proportion of renewable energy use; Linear programming model construction and solution: define the power generation of each resource at each time point as the decision variable, determine the technical parameters related to the decision variables as the power generation efficiency, power generation cost, and availability of each resource, ensure that all constraints and objective functions meet the requirements of linear programming, perform piecewise linearization on nonlinear elements, apply the linear programming solver to input model data and parameters, run the solution process, check the feasibility and effectiveness of the solution results, conduct sensitivity analysis, evaluate the impact of changes in key parameters on the optimization results, analyze cost savings, carbon emission reduction and resource utilization, convert the optimization results into specific operation and scheduling strategies, implement them in power system operations, and regularly update model parameters and re-optimize based on actual operating data and market changes.

6. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 1 is characterized in that: In the market response and clearing module, the market response and clearing model comprehensively considers electricity demand, supply capacity, and carbon emission costs to clear the electricity market. It adjusts the clearing strategy based on real-time market information and forecast data to optimize the balance between electricity supply and demand. The formula of the market response and clearing model is: Where: is the total cost, including electricity supply cost and carbon emission cost, For in time The unit electricity supply cost, For in time The amount of electricity supply, For in time The unit carbon emission cost, For in time of carbon emissions, is the total time period considered, and Obtained from electricity market operation data, and Obtained from carbon trading market data and emission data from relevant environmental protection agencies.

7. The resource layering and partitioning aggregation system based on multi-information fusion according to claim 6 is characterized in that: In the market response and clearing module, the process of solving the market response and clearing model includes: Data integration: Integrate real-time data from electricity and carbon markets to ensure 、 、 and accuracy and timeliness; Cost Calculation: Calculate the total cost for a given time period using Simpson's method ; Optimization strategy: Apply linear programming optimization algorithm to adjust and resource allocation to minimize , taking into account carbon emission limits and market demand, optimize carbon emissions To meet environmental standards.

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