Energy and carbon management optimization method and system for linkage of source network load storage and virtual power plant

Abstract

The invention discloses an energy and carbon management optimization method and system for source network load storage and virtual power plant linkage, relates to the technical field of energy management, and solves the problem of low management precision during source network load storage and virtual power plant linkage. And selecting a scheduling strategy to detect abnormity, and adjusting an output rate interval according to the storage capacity of the adjacent nodes. According to the invention, through intelligent scheduling and accurate carbon emission management, the flexibility, the stability and the low-carbon level of the energy system are improved, the problems of insufficient flexibility, low carbon emission management accuracy and insufficient global coordination ability in the prior art are solved, and high-efficiency carbon collaborative optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and power system optimization. More specifically, the present invention relates to an energy-carbon management optimization method and system for linking source, grid, load, storage and virtual power plant. Background Art

[0002] With the transformation of energy structures and the widespread adoption of distributed energy, the integration of power generation, grid-load, and storage with virtual power plants (VPPs) has become an important means of improving energy system efficiency and optimizing carbon emissions management. However, existing technologies for implementing this integration still lack energy scheduling flexibility, carbon emissions management accuracy, and multi-objective optimization capabilities, limiting their effectiveness in complex scenarios.

[0003] After searching, the patent with publication number CN103597689B discloses an "optimized load management" method, with a publication date of August 17, 2016. This technology predicts the power output of distributed power sources (such as wind or solar generators) and combines the power consumption characteristic curves of consumer devices to formulate an energy distribution plan for the future time period. However, this solution mainly focuses on the optimization of energy distribution based on time series, lacks specific considerations for carbon emission management, and fails to fully realize the coordinated linkage between source, grid, load and storage, making it difficult to meet the current energy system's demand for low carbonization and intelligence. In addition, the solution lacks support for real-time dynamic adjustment, which may affect the stability and economy of the system in the event of large load fluctuations or emergencies.

[0004] After searching, the patent with publication number CN119341092B discloses an "optimization management system for distributed photovoltaic power generation", with a publication date of April 1, 2025. This technology realizes efficient utilization of electricity based on dynamic energy management and multi-energy supply and demand interaction by real-time prediction and scheduling of the pumping and lighting load demands of the distributed photovoltaic system. However, this solution mainly focuses on the local optimization of the distributed photovoltaic system, does not fully consider the global coordination capabilities of the virtual power plant, and does not involve relevant strategies for carbon emission management. At the same time, the solution lacks flexibility in multi-energy complementarity and energy storage scheduling. It may be difficult to achieve the optimal configuration of energy in complex scenarios, especially when a high proportion of renewable energy is connected. The overall benefits of the system may be subject to certain restrictions.

[0005] The above problems indicate that the existing technology for linking power generation, grid load storage and virtual power plants still has room for improvement in terms of coordinated management of energy and carbon emissions, dynamic response capabilities, and multi-objective optimization. Therefore, the present invention provides a "method and system for optimizing energy and carbon management by linking power generation, grid load storage and virtual power plants." This aims to achieve deep synergy between power generation, grid load storage and virtual power plants by introducing intelligent energy scheduling strategies and precise carbon emission management mechanisms, thereby improving the flexibility, stability, and low-carbonization level of the energy system, and thus meeting the needs of modern energy systems for high efficiency, intelligence, and sustainable development. Summary of the Invention

[0006] To overcome the aforementioned shortcomings of the prior art, embodiments of the present invention provide an energy-carbon management optimization method and system that integrates source, grid, load, and storage with a virtual power plant. By introducing intelligent energy scheduling strategies and precise carbon emission management mechanisms, improvements are made in dynamic response capabilities, multi-objective optimization, and coordinated management of energy and carbon emissions, addressing the issues mentioned in the background art, such as insufficient flexibility, low carbon emission management precision, and lack of global coordination capabilities.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The energy and carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant includes the following steps:

[0009] Acquire real-time operating data of distributed energy equipment and classify and label the data according to energy type; calculate the activity index of each label type based on the energy output frequency of different label types within the monitoring period; combine the total energy output rate and the activity index to dynamically divide the nodes of the energy management system into basic nodes, enhanced nodes or comprehensive nodes.

[0010] Select the corresponding energy scheduling strategy based on the node type, including:

[0011] Basic nodes are assigned to low-volatility tasks, enhanced nodes are assigned to medium-volatility tasks, and comprehensive nodes are assigned to high-volatility and diversified tasks;

[0012] Detect the type of adjacent nodes and the energy storage capacity of the same tag type;

[0013] Dynamically adjust the standard output rate range of the current node according to the storage capacity ratio to achieve cross-node collaborative optimization.

[0014] In a preferred embodiment, the node division includes:

[0015] Normalizing the total energy output rate using a linear normalization algorithm to generate an energy output coefficient;

[0016] The difference between the energy output coefficient and the comprehensive activity index is used as the standard value for classification, and dynamic classification is as follows:

[0017] Basic node: The standard value exceeds the preset critical value;

[0018] Enhanced node: the standard value is between 0 and the critical value;

[0019] Comprehensive node: The standard value is less than 0, and it supports the functional modes of basic nodes and enhanced nodes.

[0020] In a preferred embodiment, the activity index is calculated in the following manner:

[0021] Combine the energy output frequencies of different marker types into a frequency data set and calculate its mean and variance;

[0022] Multiply the variance by the preset variance ratio coefficient and add it to the average value to generate the activity threshold;

[0023] When the energy output frequency of the marker type exceeds the threshold, it is judged as a high-activity type, otherwise it is a low-activity type;

[0024] Dynamically adjust the standard output rate range of the current node based on the storage capacity ratio, specifically including:

[0025] Extract standard output rate intervals from historical data based on AUC curve analysis;

[0026] When the current rate deviates from the interval, the deviation coefficient between it and the center value of the interval is calculated and compared with the dynamic threshold to determine the abnormality.

[0027] In a preferred embodiment, the coordinated adjustment of adjacent nodes includes:

[0028] If the current node is a composite node and is different from the previous node type, then:

[0029] Multiply the standard output rate range of the current node by the adjustment coefficient (storage capacity ratio) to generate the adjusted rate range;

[0030] Re-evaluate exceptions and optimize scheduling strategies.

[0031] The adjustment coefficient is the ratio of the energy storage capacity of the current node to the energy storage capacity of the previous node.

[0032] In a preferred embodiment, the historical database is accessed to obtain the energy output rates of different types of nodes in the historical energy data and merged into a historical rate data set. The deviation threshold is set and the standard output rate range is calculated using the AUC curve analysis method. The steps are as follows:

[0033] Set a benchmark value and use the average value in the historical rate data set as the benchmark value;

[0034] Positive and negative classification: data in the historical rate data set that is higher than the benchmark value is marked as positive data, and data that is lower than the benchmark value is marked as negative data;

[0035] Grading judgment: positive data and negative data exceeding the preset rate threshold are labeled as high positive data and high negative data, respectively; positive data and negative data below the preset rate threshold are labeled as low positive data and low negative data, respectively;

[0036] Calculate the ratio, and calculate the high positive rate and high negative rate respectively through the ratio formula;

[0037] Ratio increment: Set M different rate thresholds to obtain multiple high positive rates and high negative rates. Differ the obtained high positive rates from large to small to obtain M-1 high positive rate differences. Differ the obtained high negative rates from large to small to obtain M-1 high negative rate differences.

[0038] Calculate the deviation threshold by using the obtained high positive rate difference and high negative rate difference;

[0039] Calculate the standard output rate range, select the maximum and minimum values of the high positive rate and the high negative rate respectively, use the maximum value of the high positive rate and the high negative rate to calculate the upper range of the standard output rate, use the minimum value of the high positive rate and the high negative rate to calculate the lower range of the standard output rate, and use the range between the upper range of the standard output rate and the lower range of the standard output rate as the standard output rate range.

[0040] In a preferred embodiment, when determining whether energy scheduling is abnormal, the energy output rate of the current node is first compared with the standard output rate range. If the energy output rate is within the standard output rate range, it is determined that there is no abnormality in energy scheduling;

[0041] When the energy output rate is outside the standard output rate range, the deviation coefficient is obtained by subtracting the energy output rate from the center value of the standard output rate range;

[0042] When the deviation coefficient is lower than the deviation threshold, it is determined that there is no abnormality in energy scheduling;

[0043] When the deviation coefficient exceeds the deviation threshold, it is judged that the energy scheduling is abnormal and marked.

[0044] In a preferred embodiment, the energy storage capacity of the same tag type in adjacent nodes is detected, and the ratio of the energy storage capacity of the current node to the energy storage capacity of the previous node is used as an adjustment coefficient. If the adjustment coefficient of the current node exceeds a preset evaluation value, the standard output rate range of the current node is adjusted based on the type of the adjacent node and the adjustment coefficient. The specific steps are as follows:

[0045] If the current node type is a basic node, an enhanced node, or the current node type is the same as the previous node type, the standard output rate range of the current node will not be adjusted;

[0046] If the type of the current node is a comprehensive node and the type of the current node is inconsistent with the type of the previous node, the product of the standard output rate range of the current node and the adjustment coefficient is used as the adjusted standard output rate range to re-judge whether there is any abnormality in the energy scheduling.

[0047] The energy-carbon management optimization system for the linkage of power generation, grid, load, and storage with virtual power plants is used to implement the above-mentioned energy-carbon management optimization method for the linkage of power generation, grid, load, and storage with virtual power plants, including a data acquisition module, a node division module, an anomaly detection module, and an adjustment optimization module;

[0048] The data acquisition module is used to filter the energy types in the operating data, mark them, and collect the total energy output rate of the energy equipment and the frequency of energy output of different marked types. When the operating data is transmitted to the set node, the node energy output rate is collected;

[0049] The node classification module is used to set activity thresholds based on the frequency of energy output of different tag types, filter out high-activity types in the operating data, and classify the current node type after evaluating the comprehensive activity index of the operating data;

[0050] The anomaly detection module calculates the standard output rate interval based on historical energy data and sets a deviation threshold. It then calculates the deviation coefficient based on the node energy output rate and the standard output rate interval. It then compares the deviation coefficient with the deviation threshold to determine whether there is an anomaly in the energy scheduling of the current node.

[0051] The regulation optimization module adjusts the standard output rate interval of the current node according to the type of adjacent nodes in the energy management system and the energy storage capacity of the same tag type in the adjacent nodes.

[0052] The technical effects and advantages of the energy-carbon management optimization method and system for linking source, grid, load, storage and virtual power plant of the present invention are as follows:

[0053] The present invention obtains the total energy output rate of energy equipment, classifies and labels different energy types in the operating data, counts the frequency of energy output of different labeled types, calculates the activity threshold and filters out high-activity types, evaluates the comprehensive activity index of the operating data, and divides the node types in the energy management system in combination with the total energy output rate and the activity index. The corresponding energy scheduling strategy is selected according to the node type, which narrows the scope of energy scheduling anomaly detection and improves scheduling efficiency; obtains the energy output rate of the corresponding node in the historical energy data, calculates the standard output rate interval and sets the deviation threshold, collects the energy output rate of the current node, determines the deviation coefficient, judges the energy scheduling anomaly and marks it, detects the energy storage capacity of the same labeled type in adjacent nodes, and adjusts the standard output rate interval of the current node in combination with the type and energy storage capacity of the adjacent nodes, thereby tracing the source of the energy scheduling problem according to the abnormal situation of the node, quickly locating the abnormal conditions of different nodes, and significantly improving the flexibility, stability and low carbonization level of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the energy-carbon management optimization method of the present invention that integrates source, grid, load, storage and virtual power plant.

[0055] Figure 2 This is a flow chart of the energy-carbon management optimization system for the linkage of source, grid, load, storage and virtual power plant of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] The present invention provides an energy-carbon management optimization method and system for linking source, grid, load and storage with virtual power plant. Figure 1 and attached Figure 2 Detailed description is given. Figure 1 The overall process from data collection to node division, anomaly detection and adjustment optimization is shown. Figure 2 The structure of the system is described, including the connection relationship between the data acquisition module 1, the node division module 2, the anomaly detection module 3 and the adjustment optimization module 4 and their functional implementation.

[0058] In specific implementation, the energy management system is a distributed energy network composed of multiple nodes, which jointly maintain and manage the operating status of energy equipment. The data acquisition module 1 is responsible for obtaining the real-time operating data of distributed energy equipment and classifying and marking the different energy types in the operating data. This process is completed by connecting to the monitoring software to capture and analyze the operating status of the energy equipment. The monitoring software is a tool compatible with real-time monitoring and performance analysis, which is used to obtain the operating parameters and energy output of the energy equipment. At the same time, the energy type in the operating data is classified and marked, the monitoring period is set, and the energy output frequency of each marked type within the monitoring period is counted. The data analysis tool is a software that supports data processing and statistical analysis, and is used to calculate the frequency of energy output of different marked types.

[0059] In step S2, node classification module 2 calculates the activity index for each marker type based on the energy output frequency of different marker types during the monitoring period. The specific node classification process is as follows: First, the energy output frequencies of different marker types in the operating data are combined into a frequency dataset. The mean and variance of the frequency dataset are calculated. The variance is multiplied by a preset variance ratio coefficient and then added to the mean value. The resulting result is used as the activity threshold. In the operating data, if the energy output frequency of a marker type exceeds the activity threshold, the marker type is judged as a high-activity type; otherwise, it is judged as a low-activity type. Next, the activity index of the marker type is calculated. The ratio of the energy output frequency of the marker type to the activity threshold is used as the activity score for the marker type. The average of the activity scores of all marker types is used as the comprehensive activity index of the operating data. Based on the total energy output rate of the energy device and the activity index, nodes in the energy management system are classified as basic nodes, enhanced nodes, or comprehensive nodes. The total energy output rate of the energy device is normalized using a linear normalization algorithm, and the normalized result is used as the energy output coefficient. The difference between the energy output coefficient and the comprehensive activity index of the operating data is used as the node classification standard value. When the node division standard value is less than 0, the node is divided into a comprehensive node; when the node division standard value is greater than 0 and less than the preset critical value, the node is divided into an enhanced node; when the node division standard value exceeds the preset critical value, the node is divided into a basic node.

[0060] In step S3, anomaly detection module 3 selects the corresponding energy scheduling strategy based on the node type. If the current node is a basic node, it prioritizes energy output tasks with low volatility; if the current node is an enhanced node, it prioritizes energy output tasks with medium volatility; if the current node is a comprehensive node, it prioritizes energy output tasks with high volatility and diverse demands. Comprehensive nodes have two functional modes: basic node and enhanced node. The historical energy data is accessed from the historical database to obtain the energy output rates of different types of nodes and merged into a historical rate dataset. AUC curve analysis is used to set deviation thresholds and calculate standard output rate ranges. The specific steps are as follows: set a benchmark value, and use the average value in the historical rate data set as the benchmark value; positive and negative classification, mark the data in the historical rate data set that is higher than the benchmark value as positive data, and mark the data that is lower than the benchmark value as negative data; graded judgment, add labels to the positive data and negative data that exceed the preset rate threshold as high positive data and high negative data respectively; add labels to the positive data and negative data that are lower than the preset rate threshold as low positive data and low negative data respectively; calculate the ratio, and calculate the high positive rate and high negative rate respectively through the ratio formula; ratio increment, set M different rate thresholds to obtain multiple high positive rates and high negative rates , subtract the obtained high positive rate from large to small to obtain M-1 high positive rate differences; subtract the obtained high negative rate from large to small to obtain M-1 high negative rate differences; calculate the deviation threshold, and use the obtained high positive rate difference and high negative rate difference to calculate the deviation threshold; calculate the standard output rate interval, select the maximum and minimum values of the high positive rate and the high negative rate respectively, and use the maximum value of the high positive rate and the high negative rate to calculate the upper standard output rate interval; use the minimum value of the high positive rate and the high negative rate to calculate the lower standard output rate interval; the range between the upper standard output rate interval and the lower standard output rate interval is used as the standard output rate interval. When judging whether there is an abnormality in energy scheduling, first compare the current node energy output rate with the standard output rate range. When the energy output rate is within the standard output rate range, it is judged that there is no abnormality in energy scheduling; when the energy output rate is outside the standard output rate range, the energy output rate is subtracted from the center value of the standard output rate range to obtain the deviation coefficient; when the deviation coefficient is lower than the deviation threshold, it is judged that there is no abnormality in energy scheduling; when the deviation coefficient exceeds the deviation threshold, it is judged that there is an abnormality in energy scheduling and it is marked.

[0061] In step S4, the regulation optimization module 4 detects the energy storage capacity of adjacent nodes of the same tag type and uses the ratio of the energy storage capacity of the current node to the energy storage capacity of the previous node as the regulation coefficient. If the current node type is a basic node, an enhanced node, or the current node type is the same as the previous node type, the standard output rate interval of the current node is not adjusted. If the current node type is a comprehensive node and the current node type is inconsistent with the previous node type, the product of the current node's standard output rate interval and the regulation coefficient is used as the adjusted standard output rate interval, and a new determination is made as to whether there is any abnormality in energy scheduling.

[0062] The overall system workflow is as follows: Data Acquisition Module 1 first acquires real-time operating data from distributed energy devices and classifies and labels the different energy types in the operating data. It then sets a monitoring period and counts the energy output frequency of each labeled type within the monitoring period. Node Classification Module 2 calculates the activity index for each labeled type based on the energy output frequency of each labeled type within the monitoring period. Combining the total energy output rate of the energy devices with the activity index, the nodes in the energy management system are classified as basic nodes, enhanced nodes, or comprehensive nodes. Before each energy dispatch, Anomaly Detection Module 3 selects the corresponding energy dispatch strategy based on the node type, accesses the historical energy database, obtains the energy output rate of the corresponding node from historical energy data, calculates the standard output rate range, collects the energy output rate of the current node, calculates the deviation coefficient from the standard output rate range, and uses the deviation coefficient to determine whether any energy dispatch anomalies occur and flags them. Regulation Optimization Module 4 detects the types of adjacent nodes in the energy management system, analyzes the energy storage capacity of adjacent nodes with the same labeled type, calculates an adjustment coefficient based on the ratio of the energy storage capacity, and adjusts the standard output rate range of the current node based on the adjacent node types and the adjustment coefficient.

[0063] In practical applications, assume that an energy management system includes multiple distributed energy devices, including solar power generation devices, wind power generation devices, and energy storage devices. Data acquisition module 1 acquires real-time operating data from these devices and categorizes and labels the different energy types in the operating data. For example, the energy output of a solar power generation device is labeled "solar energy" and the energy output of a wind power generation device is labeled "wind energy." A monitoring period is set to one day, and the frequency of energy output for each labeled type within a day is counted. Node classification module 2 calculates the activity index for each labeled type based on the daily energy output frequency of each labeled type. Combining the total energy output rate of the energy devices with the activity index, the nodes in the energy management system are classified as basic nodes, enhanced nodes, or integrated nodes. For example, if a node has a high total energy output rate and a low activity index, it is classified as a basic node; if a node has a medium total energy output rate and a high activity index, it is classified as an enhanced node; and if a node has a low total energy output rate and a low activity index, it is classified as an integrated node. Anomaly Detection Module 3 selects the corresponding energy scheduling strategy based on the node type. For example, for basic nodes, it prioritizes energy output tasks with low volatility; for enhanced nodes, it prioritizes energy output tasks with medium volatility; and for integrated nodes, it prioritizes energy output tasks with high volatility and diverse demands. It accesses the historical database to obtain the energy output rate of the corresponding node from historical energy data and calculates the standard output rate range. It then collects the energy output rate of the current node and calculates the deviation coefficient from the standard output rate range. Based on the deviation coefficient, it determines whether energy scheduling has any anomalies and marks them. Regulation Optimization Module 4 detects the types of adjacent nodes in the energy management system, analyzes the energy storage capacity of adjacent nodes with the same tag type, calculates the adjustment coefficient based on the ratio of energy storage capacity, and adjusts the standard output rate range of the current node based on the adjacent node types and the adjustment coefficient. For example, if the current node type is an integrated node and the current node type is inconsistent with the previous node type, the product of the current node's standard output rate range and the adjustment coefficient is used as the adjusted standard output rate range, and the energy scheduling is re-determined to determine whether there are any anomalies.

[0064] It can be seen from the above specific implementation methods that the present invention realizes the intelligent division and dynamic scheduling optimization of nodes in the energy management system, which significantly improves the flexibility, stability and low-carbon level of the energy system.

[0065] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0066] In step 1, data acquisition module 1 first connects to the operating status of distributed energy devices through monitoring software to obtain real-time operating data. For example, in the energy management system of a certain industrial park, the system is connected to multiple distributed energy devices, including solar power generation devices, wind power generation devices, and energy storage devices. The monitoring software captures the operating parameters of each device, such as output power, operating temperature, and load conditions, and uploads this data to data acquisition module 1. Subsequently, the operating data is classified and labeled according to the different energy types, for example, the energy output of solar power generation devices is labeled "solar energy" and the energy output of wind power generation devices is labeled "wind energy." At the same time, the monitoring period is set to one day, and data analysis tools are used to calculate the energy output frequency of each labeled type within a day. The data analysis tool calculates the high-frequency output of "solar energy" during the day and the possible fluctuating output of "wind energy" at night, thereby forming preliminary frequency distribution data.

[0067] In step 2, node partitioning module 2 further calculates the activity index for each labeled type based on the frequency distribution data provided by data acquisition module 1. For example, in the operating data of a certain node, suppose that the energy output frequency of "solar energy" is high, while the output frequency of "wind energy" is low. Node partitioning module 2 combines these frequencies into a frequency dataset and calculates their mean and variance. The variance is then multiplied by a preset variance scaling factor and added to the mean to determine the activity threshold. If the output frequency of "solar energy" exceeds the activity threshold, it is marked as a high-activity type; if the output frequency of "wind energy" is below the activity threshold, it is marked as a low-activity type. Next, an activity score is calculated for each labeled type, and the average of the activity scores for all labeled types is taken as the overall activity index. Combined with the total energy output rate of the energy equipment, the total energy output rate is normalized using a linear normalization algorithm to obtain the energy output coefficient. The difference between the energy output coefficient and the overall activity index is used as the node partitioning standard value. If a node's classification criterion value is less than 0, it is classified as a comprehensive node. If the criterion value is greater than 0 and less than a preset threshold, it is classified as an enhanced node. If the criterion value exceeds the threshold, it is classified as a basic node. For example, a node may be classified as a basic node due to the high output rate and low activity index of a solar power generation device.

[0068] In step three, the anomaly detection module 3 selects the corresponding energy scheduling strategy based on the node division results. Taking the basic node as an example, priority is given to low-volatility energy output tasks, such as providing stable power to the lighting system in the park. For enhanced nodes, priority is given to tasks with medium volatility, such as supporting the intermittent power demand of some production equipment. Comprehensive nodes undertake tasks with high volatility and diversified demands, such as dynamically adjusting the charging and discharging status of energy storage devices. In this process, the anomaly detection module 3 accesses the historical database, obtains the historical energy data of the corresponding node and merges it into a historical rate data set. Using the AUC curve analysis method, a benchmark value is set and the data in the historical rate data set that is higher than the benchmark value is marked as positive data, and the data that is lower than the benchmark value is marked as negative data. Then, graded judgments are made and labels are added, the high positive rate and the high negative rate are calculated, and finally the deviation threshold and the standard output rate range are determined. For example, if the standard output rate of a node is between 50kW and 100kW and the actual energy output rate is 80kW, it is judged to be within the normal range; if the actual output rate is 120kW, the deviation coefficient from the center value of the standard output rate interval (75kW) is calculated. If the deviation coefficient exceeds the deviation threshold, it is marked as abnormal.

[0069] In step four, the regulation optimization module 4 detects the energy storage capacity of the same marked type in the adjacent nodes, and calculates the regulation coefficient based on the ratio of the energy storage capacity of the current node to the previous node. For example, if the current node is a comprehensive node and its type is inconsistent with the previous node, the product of the standard output rate interval of the current node and the regulation coefficient is used as the adjusted standard output rate interval. Assuming that the energy storage capacity of the current node is 500kWh and the energy storage capacity of the previous node is 1000kWh, the regulation coefficient is 0.5. If the standard output rate of the current node is 50kW to 100kW, the adjusted standard output rate is 25kW to 50kW. Subsequently, it is re-determined whether there is an abnormality in the energy scheduling. For example, if the adjusted standard output rate is 25kW to 50kW, and the actual output rate is 60kW, the deviation coefficient is calculated again and it is determined whether it is abnormal.

[0070] Through the above steps, the present invention realizes the intelligent division and dynamic scheduling optimization of the energy management system. Taking the industrial park as an example, the system can dynamically adjust the node type and its energy scheduling strategy according to the output characteristics of solar energy and wind energy to ensure the stability and efficiency of energy output. At the same time, by analyzing the energy storage capacity of adjacent nodes, the system can quickly locate abnormal nodes and adjust their standard output rate range, thereby significantly improving the flexibility, stability and low carbonization level of the energy system. For example, when the wind energy output fluctuates greatly at night, the system can maintain a stable supply of electricity in the park by adjusting the charging and discharging status of the energy storage device, while reducing carbon emissions.

[0071] Any content not described in detail in the specification belongs to the prior art known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited, and conventional equipment can be used. In this technical solution, electrical control components not mentioned are not shown in the figures because they belong to the prior art and will not be described here.

[0072] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0073] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0075] 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.

[0076] 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. An energy-carbon management optimization method for linking power generation, grid, load storage, and virtual power plants, characterized by: The following steps are included: Obtain real-time operating data of distributed energy devices and classify and label the data according to energy type; calculate the activity index of each label type based on the energy output frequency of different label types during the monitoring period; Combining the total energy output rate and activity index, the nodes of the energy management system are dynamically divided into basic nodes, enhanced nodes or comprehensive nodes; Select the corresponding energy scheduling strategy based on the node type, including: Basic nodes are assigned to low-volatility tasks, enhanced nodes are assigned to medium-volatility tasks, and comprehensive nodes are assigned to high-volatility and diversified tasks; Detect the type of adjacent nodes and the energy storage capacity of the same tag type; Dynamically adjust the standard output rate range of the current node according to the storage capacity ratio to achieve cross-node collaborative optimization.

2. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 1 is characterized by: The node division includes: Normalizing the total energy output rate using a linear normalization algorithm to generate an energy output coefficient; The difference between the energy output coefficient and the comprehensive activity index is used as the standard value for classification, and dynamic classification is as follows: Basic node: The standard value exceeds the preset critical value; Enhanced node: the standard value is between 0 and the critical value; Comprehensive node: The standard value is less than 0, and it supports the functional modes of basic nodes and enhanced nodes.

3. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 2 is characterized by: The activity index is calculated as follows: Combine the energy output frequencies of different marker types into a frequency data set and calculate its mean and variance; Multiply the variance by the preset variance ratio coefficient and add it to the average value to generate the activity threshold; When the energy output frequency of the marker type exceeds the threshold, it is judged as a high-activity type, otherwise it is a low-activity type; Dynamically adjust the standard output rate range of the current node based on the storage capacity ratio, specifically including: Extract standard output rate intervals from historical data based on AUC curve analysis; When the current rate deviates from the interval, the deviation coefficient between it and the center value of the interval is calculated and compared with the dynamic threshold to determine the abnormality.

4. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 3 is characterized by: The coordinated adjustment of adjacent nodes includes: If the current node is a composite node and is different from the previous node type, then: Multiply the standard output rate range of the current node by the adjustment coefficient to generate the adjusted rate range; Re-evaluate anomalies and optimize scheduling strategies; The adjustment coefficient is the ratio of the energy storage capacity of the current node to the energy storage capacity of the previous node.

5. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 1 is characterized by: Access the historical database to obtain the energy output rates of different types of nodes in the historical energy data and merge them into a historical rate dataset. Use the AUC curve analysis method to set the deviation threshold and calculate the standard output rate range. The steps are as follows: Set a benchmark value and use the average value in the historical rate data set as the benchmark value; Positive and negative classification: data in the historical rate data set that is higher than the benchmark value is marked as positive data, and data that is lower than the benchmark value is marked as negative data; Grading judgment: positive data and negative data exceeding the preset rate threshold are labeled as high positive data and high negative data, respectively; positive data and negative data below the preset rate threshold are labeled as low positive data and low negative data, respectively; Calculate the ratio, and calculate the high positive rate and high negative rate respectively through the ratio formula; Ratio increment: Set M different rate thresholds to obtain multiple high positive rates and high negative rates. Differ the obtained high positive rates from large to small to obtain M-1 high positive rate differences. Differ the obtained high negative rates from large to small to obtain M-1 high negative rate differences. Calculate the deviation threshold by using the obtained high positive rate difference and high negative rate difference; Calculate the standard output rate range, select the maximum and minimum values of the high positive rate and the high negative rate respectively, use the maximum value of the high positive rate and the high negative rate to calculate the upper range of the standard output rate, use the minimum value of the high positive rate and the high negative rate to calculate the lower range of the standard output rate, and use the range between the upper range of the standard output rate and the lower range of the standard output rate as the standard output rate range.

6. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 3 is characterized by: When judging whether there is an abnormality in energy scheduling, first compare the current node energy output rate with the standard output rate range. If the energy output rate is within the standard output rate range, it is judged that there is no abnormality in energy scheduling; When the energy output rate is outside the standard output rate range, the deviation coefficient is obtained by subtracting the energy output rate from the center value of the standard output rate range; When the deviation coefficient is lower than the deviation threshold, it is determined that there is no abnormality in energy scheduling; When the deviation coefficient exceeds the deviation threshold, it is judged that the energy scheduling is abnormal and marked.

7. The energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to claim 1 is characterized by: Detect the energy storage capacity of the same tag type in the adjacent nodes, and use the ratio of the energy storage capacity of the current node to the energy storage capacity of the previous node as the adjustment coefficient. If the adjustment coefficient of the current node exceeds the preset evaluation value, adjust the standard output rate range of the current node based on the type of the adjacent node and the adjustment coefficient. The specific steps are as follows: If the current node type is a basic node, an enhanced node, or the current node type is the same as the previous node type, the standard output rate range of the current node will not be adjusted; If the type of the current node is a comprehensive node and the type of the current node is inconsistent with the type of the previous node, the product of the standard output rate range of the current node and the adjustment coefficient is used as the adjusted standard output rate range to re-judge whether there is any abnormality in the energy scheduling.

8. An energy-carbon management optimization system for the linkage of source, grid, load, storage and virtual power plant, used to implement the energy-carbon management optimization method for the linkage of source, grid, load, storage and virtual power plant according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, node division module, anomaly detection module and adjustment optimization module; The data acquisition module is used to filter the energy types in the operating data, mark them, and collect the total energy output rate of the energy equipment and the frequency of energy output of different marked types. When the operating data is transmitted to the set node, the node energy output rate is collected; The node classification module is used to set activity thresholds based on the frequency of energy output of different tag types, filter out high-activity types in the operating data, and classify the current node type after evaluating the comprehensive activity index of the operating data; The anomaly detection module is used to calculate the standard output rate interval based on historical energy data and set the deviation threshold. The deviation coefficient is calculated based on the node energy output rate and the standard output rate interval. The deviation coefficient is compared with the deviation threshold to determine whether there is an anomaly in the energy scheduling of the current node. The regulation optimization module is used to adjust the standard output rate interval of the current node according to the type of adjacent nodes in the energy management system and the energy storage capacity of the same tag type in the adjacent nodes.

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