Distributed energy dispatching management method and system for virtual power plant

Abstract

The present invention relates to the technical field of distributed energy management. Disclosed are a distributed energy dispatching management method and system for a virtual power plant. The method comprises: collecting electricity price information and operation information, and performing cleaning and preliminary processing on data to ensure data quality; using a machine learning algorithm to predict load information, electricity prices and related energy prices within the scope of the virtual power plant; constructing and solving a hybrid optimization model to generate optimal power generation, power utilization and energy storage operation plans for different electricity transaction markets, so as to achieve efficient resource allocation; recombining distributed energy units on the basis of operation information data and prediction result data to form optimal resource combinations for different market transactions; executing a dispatching strategy for optimally configuring resource combinations; and generating a real-time dispatching optimization strategy on the basis of the latest operation state information. The present invention achieves dynamic optimal configuration of distributed energy units by means of the operation information data and the prediction result data.

Description

A Distributed Energy Dispatch and Management Method and System for Virtual Power Plants Technical Field

[0001] This invention relates to the field of distributed energy management technology, and in particular to a distributed energy dispatching and management method and system for a virtual power plant. Background Technology

[0002] With the application of emerging technologies such as big data, cloud computing and the Internet of Things, the potential of virtual power plants in energy management and dispatch has been further explored. Existing forecasting technologies can estimate future load and market price trends to some extent, but their accuracy and timeliness need to be improved. In particular, the limitations of such forecasts are particularly obvious when facing complex and changeable weather environments.

[0003] While existing forecasting models can provide load and electricity price forecasts within a certain range, they still have limitations in handling complex nonlinear relationships and multivariate influences. In particular, their forecasting accuracy is not high when dealing with factors such as market fluctuations and weather changes. In addition, existing dispatching strategies mostly adopt static optimization methods, which are difficult to adapt to the rapidly changing electricity market environment and cannot achieve real-time dispatching optimization. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a distributed energy dispatching and management method and system for virtual power plants to solve the problem of optimized dispatching and real-time adjustment of distributed energy resources in virtual power plants.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a distributed energy dispatch management method and system for a virtual power plant, wherein: electricity price information and operation information are collected, and the data is preprocessed;

[0008] Construct a time series forecasting model to predict load information, electricity prices, and related energy prices within the scope of a virtual power plant;

[0009] Construct a hybrid optimization model to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets;

[0010] Based on operational information and forecast results, distributed energy units are recombined to form resource combinations and data information for different electricity trading markets;

[0011] Optimize resource allocation to form a scheduling strategy, and obtain an energy management operation plan based on the latest operating status information;

[0012] Real-time scheduling optimization strategies are generated based on energy management operation plans.

[0013] As a preferred embodiment of the distributed energy dispatch management method and system for the virtual power plant described in this invention, the following features are included: the electricity price information includes real-time electricity price and historical electricity price; the operation information includes weather data, geographical data, electricity trading market data, log data of distributed energy units, and historical load; the weather data includes temperature, humidity, and wind speed; the geographical data includes the location and altitude information of the distributed energy units; the log data of the distributed energy units includes power generation, electricity consumption, electricity purchase price, and energy storage information; the electricity trading market data includes transaction volume and contract information in transaction records; the collected data is integrated into a unified data platform; and the collected data undergoes preliminary processing, including removing invalid data and handling missing values.

[0014] As a preferred embodiment of the distributed energy dispatch management method and system for the virtual power plant described in this invention, the following steps are included: constructing a time series forecasting model to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant.

[0015] Long Short-Term Memory Network was selected as the time series prediction model, and the dataset was divided into training and test sets according to the time window length and the segmentation ratio.

[0016] The training set is used to train the time series prediction model, and the test set is used to evaluate the performance of the time series prediction model and verify the model's generalization ability.

[0017] The comprehensive feature vector is used as the input feature and fed into the time series prediction model for prediction.

[0018] As a preferred embodiment of the distributed energy dispatch management method and system for the virtual power plant described in this invention, the following steps are included in constructing a hybrid optimization model to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets:

[0019] Choosing profit maximization as the objective function, a hybrid optimization model is constructed.

[0020] Based on market rules, equipment capacity, and operational limitations, set constraints for power generation capacity, load balance, and energy storage.

[0021] Linear programming was chosen as the algorithm for solving the hybrid optimization model;

[0022] The integrated electricity price information, load demand, power generation capacity, and energy storage status data are input into the hybrid optimization model;

[0023] Transform the objective function and constraints into linear form and set the coefficient matrix;

[0024] The optimal solution is obtained by solving the hybrid optimization model using an optimization solver, which yields the best operating plan for power generation, power consumption, and energy storage.

[0025] As a preferred embodiment of the distributed energy dispatch management method and system for the virtual power plant described in this invention, the following steps are included: Based on operational information data and forecast results data, distributed energy units are recombined to form resource combinations and data information for different market transactions.

[0026] Based on the forecast results of weather data and power generation and consumption data, different types of distributed energy units are classified according to function and location to form multiple resource pools, and the resource pool is set as a collection of all distributed energy units;

[0027] Based on the results of the hybrid optimization model, each distributed energy unit is adjusted, the resource pool and optimization objective are mapped to the optimal output combination, control commands are generated and issued to each distributed energy unit to adjust its power generation, consumption and storage status.

[0028] Information from various resource pools is integrated to form resource combinations and data information for different market transactions. As a preferred embodiment of the distributed energy dispatch management method and system for the virtual power plant described in this invention, the following steps are included: executing dispatch strategies by optimizing resource combinations; optimizing and adjusting energy management operation plans based on the latest operating status information; and generating real-time dispatch optimization strategies.

[0029] Monitor the operating status of distributed energy units in real time and update operational information data;

[0030] Rerun the hybrid optimization model using the updated operational information data and adjust the energy management operation plan;

[0031] Based on the updated forecast results and real-time operating status information, adjust the real-time operating status data of the energy management operation system to generate a real-time scheduling optimization strategy.

[0032] Secondly, the present invention provides a distributed energy dispatch and management system for a virtual power plant, comprising,

[0033] The data collection and preprocessing module is responsible for collecting electricity price information and operational information, and cleaning and pre-processing the data to ensure data quality.

[0034] The predictive modeling module uses machine learning algorithms to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant, providing a basis for subsequent decision-making.

[0035] The hybrid optimization model module constructs and solves hybrid optimization models to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets, thereby achieving efficient resource allocation.

[0036] The resource reorganization module, based on operational information data and forecast results, recombines distributed energy units to form the optimal resource combination for different market transactions.

[0037] The real-time scheduling and optimization module executes scheduling strategies that optimize resource combinations. Based on the latest operational status information, it continuously optimizes and adjusts the energy management operation plan to generate real-time scheduling optimization strategies.

[0038] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the distributed energy dispatch management method and system for virtual power plants as described in the first aspect of the present invention.

[0039] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the distributed energy dispatch management method and system for virtual power plants as described in the first aspect of the present invention.

[0040] The beneficial effects of this invention are as follows: By collecting electricity price and operational information and performing preliminary data processing, the data quality is improved, laying the foundation for subsequent analysis. By constructing a time series forecasting model, load information, electricity prices, and related energy prices within the scope of the virtual power plant are predicted, achieving accurate prediction of future energy supply and demand. By constructing a hybrid optimization model, optimal power generation, consumption, and energy storage operation plans are generated for different electricity trading markets, aiming to find the optimal solution under multiple constraints and reducing operating costs. By recombining distributed energy units based on operational information data and forecast results, dynamic optimization configuration of distributed energy units is achieved. The output combination of each distributed energy unit is adjusted according to the forecast results of weather data and power generation and consumption data, enhancing the ability to respond to market changes. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 is a flowchart of the distributed energy dispatch management method of the virtual power plant in Example 1.

[0043] Figure 2 is a flowchart of the distributed energy dispatch management system of the virtual power plant in Example 1. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Example 1, referring to Figures 1 and 2, is the first embodiment of the present invention. This embodiment provides a distributed energy dispatch management method and system for a virtual power plant, including the following steps:

[0046] S1. Collect electricity price information and operational information, and preprocess the data;

[0047] Among them, electricity price information includes real-time electricity price and historical electricity price; operation information includes weather data, geographic data, electricity market data, log data of distributed energy units and historical load; weather data includes temperature, humidity and wind speed; geographic data includes the location and altitude information of distributed energy units; log data of distributed energy units includes power generation, electricity consumption, electricity purchase price and energy storage information; and electricity market data includes transaction volume and contract information in transaction records.

[0048] Furthermore, electricity price information was collected. Real-time electricity price data is updated every 15 minutes, reflecting the current demand situation in the electricity market. Historical electricity price data covers the average daily electricity price over the past year. By comparing real-time electricity prices with historical electricity prices, we can gain insight into the fluctuation patterns of market prices.

[0049] Comprehensive operational information was collected, and weather data was updated hourly, enabling better resource allocation within the region.

[0050] Electricity market data includes transaction volume and contract information from transaction records. Transaction volume data reflects the level of activity in the electricity market, while contract information provides details about long-term supply agreements and plays an important role in developing long-term energy management strategies.

[0051] Log data from distributed energy units, updated hourly, reflects the actual operating status of the distributed energy units, while energy storage information helps assess the charging and discharging status of energy storage devices.

[0052] After collecting all the data, it is integrated into a unified data platform to query and process data from different sources;

[0053] Invalid data was removed from the collected data. All data was checked and data outside the normal range was deleted to ensure the authenticity and reliability of the data. Next, missing values ​​were handled. For missing data points, interpolation methods were used to fill in the missing values ​​to avoid analytical bias caused by missing data and to ensure the data quality of subsequent analysis and modeling.

[0054] S2. Construct a time series forecasting model to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant, including the following steps:

[0055] Long Short-Term Memory Network was selected as the time series prediction model, and the dataset was divided into training and test sets according to the time window length and the segmentation ratio.

[0056] The training set is used to train the time series prediction model, and the test set is used to evaluate the performance of the time series prediction model and verify the model's generalization ability.

[0057] Using a comprehensive feature vector as input features, this data is fed into a time series prediction model for prediction. The formula is as follows.

[0058] Where Y is the predicted value, q is the number of model parameters, and g l (X l ) represents the nonlinear value of the l-th parameter, λ is the decay factor, t is the current time, t0 is the start time, x is the target variable, μ is the mean of the target variable, σ is the standard deviation of the target variable, and T is the time span.

[0059] S3. Construct a hybrid optimization model to generate optimal power generation, consumption, and energy storage operation plans for different electricity trading markets, including the following steps.

[0060] Choosing profit maximization as the objective function, a hybrid optimization model is constructed, with the following formula:

[0061] Where Π represents total profit, and P m Let Q be the electricity price in the m-th market. m For the electricity sold in the m-th market, C m (Q m To sell Q in the m-th market m The cost of electricity, where N is the number of energy storage devices, and C is the cost of electricity. f,n The fixed operating cost of the nth energy storage device;

[0062] Based on market rules, equipment capacity, and operational limitations, set constraints for power generation capacity, load balance, and energy storage.

[0063] Linear programming was chosen as the algorithm for solving the hybrid optimization model;

[0064] The integrated electricity price information, load demand, power generation capacity, and energy storage status data are input into the optimization model;

[0065] Transform the objective function and constraints into linear form and set the coefficient matrix;

[0066] The optimal solution is obtained by solving the hybrid optimization model using an optimization solver, which yields the best operating plan for power generation, consumption, and energy storage status.

[0067] Based on the objective function described above, a mixed-integer nonlinear programming model is constructed, which includes the following constraints:

[0068] Power generation limit, power generation P g (t) must not exceed the maximum power generation P mg ; 0≤P g (t)≤P mg ;

[0069] Power consumption limit, power consumption P l (t) must not exceed the maximum electricity consumption P ml ; 0≤P l (t)≤P ml

[0070] Energy storage state limitation, energy storage state P s (t) must not exceed the maximum energy storage capacity E me ; 0≤E e (t)≤E me

[0071] Energy is conserved; changes in the state of energy storage are determined by power generation and power consumption. e (t)=E e (t-1)+P g (t)-P l (t);

[0072] The optimal solution is obtained by solving the hybrid optimization model using an optimization solver, which yields the best operating plan for power generation, consumption, and energy storage status.

[0073] Set the initial energy storage state to 0 and initialize other variables. Input the optimization model into the optimization solver. The solver finds the optimal solution that minimizes the total cost while satisfying all constraints. The solver outputs the best operating plan for power generation, power consumption and energy storage state, and the solution results are obtained.

[0074] Based on the solution results, optimal power generation, power consumption, and energy storage operation plans are generated for different electricity trading markets.

[0075] According to P g The optimal solution of (t) is used to generate the optimal operation plan for power generation;

[0076] According to P l The optimal solution of (t) is used to generate the optimal operation plan for electricity consumption;

[0077] According to E me The optimal solution is obtained, and the optimal operation plan for the energy storage state is generated.

[0078] S4. Based on operational information data and forecast results, reorganize distributed energy units to form resource combinations and data information for different market transactions, including the following steps:

[0079] Based on the forecast results of weather data and power generation and consumption data, different types of distributed energy units are classified according to function and location to form multiple resource pools, and the resource pool is set as a collection of all distributed energy units;

[0080] Distributed energy units are classified according to their main functions:

[0081] Solar photovoltaic panels primarily function to generate electricity.

[0082] Wind turbines primarily function to generate electricity;

[0083] Energy storage devices primarily function to store and release electrical energy.

[0084] Demand response load, whose main function is to adjust electricity consumption in response to changes in market prices;

[0085] Distributed energy units are classified according to their primary location:

[0086] Urban centers typically experience high loads and have high electricity demand.

[0087] Suburbs typically have lower loads and more renewable energy facilities;

[0088] In rural areas, the load is typically low, and renewable energy facilities may be more dispersed.

[0089] Based on the results of the hybrid optimization model, each distributed energy unit is adjusted, the resource pool and optimization objective are mapped to the optimal output combination, control commands are generated and issued to each distributed energy unit to adjust its power generation and consumption;

[0090] By integrating the information from various resource pools, resource combinations and data information can be formed for different market transactions.

[0091] S5. Utilize optimized resource allocation and execution scheduling strategies. Based on the latest operational status information, optimize and adjust the energy management operation plan to generate a real-time scheduling optimization strategy, including the following steps.

[0092] Monitor the operating status of distributed energy units in real time and update operational information data;

[0093] The hybrid optimization model is rerun using updated operational information data to adjust the energy management operation plan; based on the updated forecast results and real-time operational status information, the energy management operation plan is adjusted to generate a real-time scheduling optimization strategy.

[0094] This embodiment also provides a distributed energy dispatch and management system for a virtual power plant, including: a data collection and preprocessing module, which is responsible for collecting electricity price information and operation information, and cleaning and pre-processing the data to ensure data quality.

[0095] The predictive modeling module uses machine learning algorithms to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant, providing a basis for subsequent decision-making.

[0096] The hybrid optimization model module constructs and solves hybrid optimization models to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets, thereby achieving efficient resource allocation.

[0097] The resource reorganization module, based on operational information data and forecast results, recombines distributed energy units to form the optimal resource combination for different market transactions.

[0098] This embodiment also provides a computer device applicable to the distributed energy dispatch management method and system of virtual power plants, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distributed energy dispatch management method and system of virtual power plants as proposed in the above embodiments.

[0099] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0100] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the distributed energy dispatch management method and system for realizing a virtual power plant as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0101] In summary, this invention improves data quality by collecting electricity price and operational information and performing preliminary data processing, laying the foundation for subsequent analysis. It utilizes a time-series forecasting model to predict load information, electricity prices, and related energy prices within the virtual power plant area, achieving accurate predictions of future energy supply and demand. Furthermore, it generates optimal power generation, consumption, and energy storage operation plans for different electricity trading markets by constructing a hybrid optimization model, seeking optimal solutions under various constraints and reducing operating costs. Finally, it recombines distributed energy units based on operational information and forecast results, achieving dynamic optimization of distributed energy unit configuration. Finally, it adjusts the output combination of each distributed energy unit according to weather data and power generation / consumption forecasts, enhancing the ability to respond to market changes.

[0102] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the distributed energy dispatch management method and system of virtual power plant are given.

[0103] First, the collected data was cleaned and missing values ​​were removed to ensure data quality. A prediction model was built using long short-term memory networks in machine learning to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant. When building the prediction model, a comprehensive feature vector was used as input features to extract features and enhance the accuracy of the prediction model.

[0104] Based on the prediction results, a mixed-integer nonlinear programming model was constructed to optimize the power generation, power consumption and energy storage operation plan of the virtual power plant. The objective function of the model is to minimize the total cost.

[0105] Based on operational information data and forecast results, the distributed energy units were recombined to form resource combinations and data information for different market transactions. The output combination of each distributed energy unit was adjusted through the results of the hybrid optimization model, and control commands were generated and sent to each distributed energy unit to adjust its power generation and consumption.

[0106] By optimizing the resource combination and executing the scheduling strategy, the energy management operation plan was optimized and adjusted based on the latest operating status information, and a real-time scheduling optimization strategy was generated. Throughout the process, the operating status of the distributed energy units was monitored in real time, and the operation information was updated based on real-time data, thereby ensuring the real-time performance and effectiveness of the scheduling strategy.

[0107] The details are shown in Table 1 below:

[0108] Table 1 Experimental Record Sheet

[0109] Data Analysis:

[0110] By comparing the data of the embodiment with those of control groups A and B, it can be seen that the predicted load in the embodiment is very close to the actual load, with an error of only 2kWh. In control group A, the predicted load differs from the actual load by 20kWh, and in control group B, the predicted load differs from the actual load by 10kWh. Through the method of the present invention, the prediction model can more accurately predict load information, thereby helping virtual power plants to better adjust power generation and consumption plans and reduce energy waste or shortages caused by inaccurate predictions.

[0111] Furthermore, by generating optimal power generation, power consumption, and energy storage operation plans through a hybrid optimization model, the virtual power plant can manage its internal resources more effectively. In the embodiment, through the optimized scheduling strategy, the virtual power plant saved approximately 10% of its operating costs within a week, while the cost savings for control groups A and B were 5% and 7%, respectively. This demonstrates the superiority of the method of the present invention in improving economic efficiency.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed energy scheduling management method of a virtual power plant, characterized by, include: S1. Collect electricity price information and operational information, and preprocess the data; S2. Construct a time series forecasting model to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant; S3. Construct a hybrid optimization model to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets; S4. Based on operational information and forecast results, reorganize distributed energy units to form resource combinations and data information for different electricity trading markets; S5. Optimize resource allocation to form a scheduling strategy, obtain the energy management operation plan based on the latest operating status information, and generate a real-time scheduling optimization strategy based on the energy management operation plan; In S3, a hybrid optimization model is constructed to generate optimal power generation, consumption, and energy storage operation plans for different electricity trading markets, including the following steps. The mixed optimization model is constructed by selecting maximum profit as a target function, and a formula is where Π is the total profit, P m is the price of electricity in the mth market, Q m is the amount of electricity sold in the mth market, C m (Q m ) is the cost of selling Q m amount of electricity in the mth market, N is the number of energy storage devices, C f,n is the fixed operating cost of the nth energy storage device; Based on market rules, equipment capacity, and operational limitations, set constraints for power generation capacity, load balance, and energy storage. Linear programming was chosen as the algorithm for solving the hybrid optimization model; The integrated electricity price information, load demand, power generation capacity, and energy storage status data are input into the hybrid optimization model; Transform the objective function and constraints into linear form and set the coefficient matrix; The optimal solution is obtained by solving the hybrid optimization model using an optimization solver, resulting in the best operating plan for power generation, power consumption, and energy storage. Based on the objective function described above, a mixed-integer nonlinear programming model is constructed, which includes the following constraints: Generation capacity limit, generation capacity P g (t) not to exceed the maximum generation capacity P mg ; 0 < P g (t) < P mg ; Power consumption limit, power consumption P l (t) does not exceed the maximum power consumption P ml ; 0 < P l (t) < P ml Energy storage state limit, energy storage state P s (t) not to exceed the maximum energy storage capacity E me ; 0 < E e (t) < E me Energy is conserved, and changes in the state of energy storage are determined by power generation and power consumption. E e (t) = E e (t - 1) + P g (t) - P l (t); The optimal solution is obtained by solving the hybrid optimization model using an optimization solver, which yields the best operating plan for power generation, consumption, and energy storage status. Set the initial energy storage state to 0 and initialize other variables. Input the optimization model into the optimization solver. The solver finds the optimal solution that minimizes the total cost while satisfying all constraints. The solver outputs the best operating plan for power generation, power consumption and energy storage state, and the solution results are obtained. Based on the solution results, optimal power generation, power consumption, and energy storage operation plans are generated for different electricity trading markets. According to P g (t) the optimal solution, the optimal operation plan of power generation capacity; According to P l (t) the optimal solution, the optimal operation plan of power consumption is generated; According to E me The optimal solution of the optimal operation plan of the energy storage state is generated.

2. The method of claim 1, wherein the method further comprises: The electricity price information includes real-time and historical electricity prices. The operational information includes weather data, geographic data, electricity trading market data, log data of distributed energy units, and historical load. The weather data includes temperature, humidity, and wind speed. The geographic data includes the location and altitude information of the distributed energy units. The log data of the distributed energy units includes power generation, electricity consumption, electricity purchase price, and energy storage information. The electricity trading market data includes transaction volume and contract information in transaction records. The collected data is integrated into a unified data platform, and preliminary processing is performed on the collected data, including removing invalid data and handling missing values.

3. The distributed energy dispatch and management method for virtual power plants as described in claim 2, characterized in that: Based on operational information data and forecast results, distributed energy units are recombined to form resource combinations and data information for different market transactions, including the following steps: Based on the forecast results of weather data and power generation and consumption data, different types of distributed energy units are classified according to function and location to form multiple resource pools, and the resource pool is set as a collection of all distributed energy units; Based on the results of the hybrid optimization model, each distributed energy unit is adjusted, the resource pool and optimization objective are mapped to the optimal output combination, control commands are generated and issued to each distributed energy unit to adjust its power generation, consumption and storage status. By integrating the information from various resource pools, resource combinations and data information can be formed for different market transactions.

4. The distributed energy dispatch and management method for virtual power plants as described in claim 3, characterized in that: By optimizing resource allocation and executing scheduling strategies, and based on the latest operational status information, the energy management operation plan is optimized and adjusted to generate a real-time scheduling optimization strategy. Includes the following steps, Monitor the operating status of distributed energy units in real time and update operational information data; Rerun the hybrid optimization model using the updated operational information data and adjust the energy management operation plan; Based on the updated forecast results and real-time operating status information, adjust the real-time operating status data of the energy management operation system to generate a real-time scheduling optimization strategy.

5. A distributed energy scheduling management system of a virtual power plant based on the distributed energy scheduling management method of the virtual power plant according to any one of claims 1 to 4, characterized by include: The data collection and preprocessing module is responsible for collecting electricity price information and operational information, and cleaning and pre-processing the data to ensure data quality. The predictive modeling module uses machine learning algorithms to predict load information, electricity prices, and related energy prices within the scope of the virtual power plant, providing a basis for subsequent decision-making. The hybrid optimization model module constructs and solves hybrid optimization models to generate optimal power generation, power consumption, and energy storage operation plans for different electricity trading markets, thereby achieving efficient resource allocation. The resource reorganization module, based on operational information data and forecast results, recombines distributed energy units to form the optimal resource combination for different market transactions. The real-time scheduling and optimization module executes scheduling strategies that optimize resource combinations. Based on the latest operational status information, it continuously optimizes and adjusts the energy management operation plan to generate real-time scheduling optimization strategies.

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