Source network load storage and virtual power plant linkage energy carbon management optimization method and system

Abstract

The application discloses a source network load storage and virtual power plant linkage carbon management optimization method and system, relates to the technical field of energy management, solves the problem of low management accuracy when the source network load storage and virtual power plant are linked, and comprises the following steps: obtaining energy equipment operation data and classifying and marking, calculating an activity index to divide node types, selecting a scheduling strategy to detect abnormalities, and adjusting an output rate interval according to adjacent node storage capacity. The application can improve the flexibility, stability and low-carbon level of the energy system through intelligent scheduling and accurate carbon emission management, solve the problems of insufficient flexibility, low carbon emission management accuracy and lack of global coordination ability in the prior art, and realize efficient carbon collaborative optimization.

Description

Technical Field

[0001] This invention relates to the field of energy management and power system optimization technology, and more specifically, to a method and system for energy and carbon management optimization that integrates power generation, grid, load, storage, and virtual power plants. Background Technology

[0002] With the transformation of the energy structure and the widespread application of distributed energy, the technology of linking energy sources, grids, loads, and storage with virtual power plants has gradually become an important means to improve the efficiency of energy systems and optimize carbon emission management. However, existing technologies still have shortcomings in terms of energy dispatch flexibility, carbon emission management accuracy, and multi-objective optimization capabilities when realizing the linkage of energy sources, grids, loads, and storage with virtual power plants, which limits their application effectiveness in complex scenarios.

[0003] A search revealed a patent, CN103597689B, which discloses an "optimized load management" method, published on August 17, 2016. This technology predicts the power output of distributed power sources (such as wind or solar generators) and combines this with the electricity consumption characteristic curves of consumer devices to formulate a future power allocation plan. However, this approach primarily focuses on time-series-based power allocation optimization, lacking specific considerations for carbon emission management and failing to fully realize the synergistic linkage between power generation, grid, load, and storage, making it difficult to meet the current energy system's demands for decarbonization and intelligence. Furthermore, this approach lacks support for real-time dynamic adjustments, which may affect the system's stability and economic efficiency under conditions of large load fluctuations or unforeseen events.

[0004] A search revealed a patent, CN119341092B, which discloses an "Optimization Management System for Distributed Photovoltaic Power Generation," published on April 1, 2025. This technology achieves efficient energy utilization based on dynamic energy management and multi-energy supply-demand interaction by predicting and scheduling the water pumping and lighting load demands of distributed photovoltaic systems in real time. However, this solution primarily focuses on the local optimization of distributed photovoltaic systems, failing to fully consider the global coordination capabilities of virtual power plants or addressing carbon emission management strategies. Furthermore, this solution lacks flexibility in multi-energy complementarity and energy storage scheduling, potentially making it difficult to achieve optimal energy allocation in complex scenarios, especially with a high proportion of renewable energy integration, which could limit the overall system efficiency.

[0005] The aforementioned problems indicate that existing technologies for linking energy sources, grids, loads, storage, and virtual power plants still have room for improvement in areas such as coordinated management of energy and carbon emissions, dynamic response capabilities, and multi-objective optimization. Therefore, this invention provides a "method and system for optimizing energy and carbon management through the linkage of energy sources, grids, loads, storage, and virtual power plants." This aims to achieve deep synergy between energy sources, grids, loads, storage, and virtual power plants by introducing intelligent energy dispatch strategies and precise carbon emission management mechanisms, thereby enhancing the flexibility, stability, and decarbonization level of the energy system and meeting the demands of modern energy systems for high efficiency, intelligence, and sustainable development. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an energy and carbon management optimization method and system that integrates power generation, grid, load, storage, and virtual power plants. By introducing intelligent energy dispatch 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, thus addressing the problems of insufficient flexibility, low carbon emission management accuracy, and lack of global coordination capabilities mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An optimized method for energy and carbon management that integrates power generation, grid, load, storage, and virtual power plants includes the following steps:

[0009] The system acquires real-time operating data from distributed energy devices and categorizes and labels the data according to energy type. Based on the energy output frequency of different label types within the monitoring period, it calculates the activity index for each label type. Combining the total energy output rate and the activity index, the nodes of the energy management system are dynamically divided into basic nodes, enhanced nodes, or integrated nodes.

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

[0011] Basic nodes are assigned low-volatility tasks, enhanced nodes are assigned medium-volatility tasks, and comprehensive nodes are assigned high-volatility and diverse tasks.

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

[0013] The standard output rate range of the current node is dynamically adjusted according to the storage capacity ratio to achieve cross-node collaborative optimization.

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

[0015] The total energy output rate is standardized using a linear normalization algorithm to generate the energy output coefficient.

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

[0017] Basic node: Standard value exceeds preset threshold;

[0018] Enhanced nodes: Standard values ​​are between 0 and critical values;

[0019] Comprehensive node: 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] Merge the energy output frequencies of different label types into a frequency dataset, and calculate its mean and variance;

[0022] The variance is multiplied by a preset variance scaling factor and then added to the mean to generate the activity threshold.

[0023] When the energy output frequency of a marker type exceeds a threshold, it is determined to be a high-activity type; otherwise, it is a low-activity type.

[0024] The standard output rate range of the current node is dynamically adjusted based on the storage capacity ratio, specifically including:

[0025] Extracting the standard output rate range from historical data based on AUC curve analysis;

[0026] When the current rate deviates from the interval, calculate its deviation coefficient from the interval center value and compare it with the dynamic threshold to determine the anomaly.

[0027] In a preferred embodiment, the neighboring node coordinated adjustment includes:

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

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

[0030] Reassess the anomalies and optimize the scheduling strategy.

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

[0032] In a preferred embodiment, the historical database is accessed to obtain the output rates of different types of energy corresponding to the nodes in the historical energy data and merged into a historical rate dataset. 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 baseline value, using the average value from the historical rate dataset as the baseline value;

[0034] Positive and negative classification: In the historical rate dataset, data above the baseline value are labeled as positive data, and data below the baseline value are labeled as negative data;

[0035] The system classifies and labels positive and negative data exceeding a preset rate threshold as high positive and high negative data, respectively, and labels positive and negative data below the preset rate threshold as low positive and low negative data, respectively.

[0036] Calculate the ratios, and use the ratio formulas to calculate the high positive value rate and the high negative value rate respectively;

[0037] The ratio increment is set by setting M different rate thresholds to obtain multiple high positive rate and high negative rate. The obtained high positive rate is subtracted from the largest to the smallest to obtain M-1 high positive rate differences, and the obtained high negative rate is subtracted from the largest to the smallest to obtain M-1 high negative rate differences.

[0038] Calculate the deviation threshold using the obtained high positive value rate difference and high negative value 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 take 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 an energy dispatching anomaly has occurred, 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 the energy dispatching has not occurred.

[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] If the deviation coefficient is lower than the deviation threshold, it is determined that there is no abnormality in energy dispatch.

[0043] When the deviation coefficient exceeds the deviation threshold, an anomaly in energy dispatch is identified and marked.

[0044] In a preferred embodiment, the energy storage capacity of adjacent nodes with the same tag type is detected, and the ratio of the energy storage capacity of the current node to that 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 in combination with the types of adjacent nodes and the adjustment coefficient. The specific steps are as follows:

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

[0046] If the current node is a composite node and its type is different from that of the previous node, then the product of the current node's standard output rate range and the adjustment coefficient will be used as the adjusted standard output rate range, and the energy dispatch will be reassessed to determine whether any abnormalities have occurred.

[0047] The energy and carbon management optimization system that links the power generation, grid, load, storage and virtual power plants is used to implement the above-mentioned energy and carbon management optimization method that links the power generation, grid, load, storage and virtual power plants. It includes a data acquisition module, a node division module, an anomaly detection module and a regulation and 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 for different marked types. When the operating data is transmitted to the set nodes, the node energy output rate is collected.

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

[0050] The anomaly detection module calculates the standard output rate range and sets the deviation threshold based on historical energy data. It calculates the deviation coefficient based on the node's energy output rate and the standard output rate range, and compares the deviation coefficient with the deviation threshold to determine whether the energy scheduling of the current node is abnormal.

[0051] The adjustment and optimization module adjusts the standard output rate range of the current node based on the type of adjacent nodes in the energy management system and the energy storage capacity of the same marker type in the adjacent nodes.

[0052] The technical effects and advantages of the energy and carbon management optimization method and system of this invention, which links source, grid, load, storage, and virtual power plants, are as follows:

[0053] This invention acquires the total energy output rate of energy equipment, classifies and marks different energy types in the operational data, statistically analyzes the frequency of energy output for different marked types, calculates activity thresholds and filters out high-activity types, evaluates the comprehensive activity index of the operational data, and classifies the node types in the energy management system by combining the total energy output rate and the activity index. Based on the node type, corresponding energy dispatch strategies are selected, narrowing the scope of energy dispatch anomaly detection and improving dispatch efficiency. Furthermore, it acquires the energy output rate of corresponding nodes in historical energy data, calculates the standard output rate range and sets a deviation threshold, collects the energy output rate of the current node, determines the deviation coefficient, identifies and marks energy dispatch anomalies, detects the energy storage capacity of the same marked type in adjacent nodes, and adjusts the standard output rate range of the current node based on the type and energy storage capacity of adjacent nodes. This allows for tracing the source of energy dispatch problems based on node anomalies, quickly locating anomalies in different nodes, and significantly improving the flexibility, stability, and decarbonization level of the energy system. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the energy and carbon management optimization method of the present invention, which links source, grid, load, storage and virtual power plant.

[0055] Figure 2 This is a flowchart of the energy and carbon management optimization system that links the source, grid, load, storage, and virtual power plant of this invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] This invention provides an energy and carbon management optimization method and system that integrates source-grid-load-storage and virtual power plant linkages. Specific implementation methods are detailed in the appendix. Figure 1 and attached Figure 2 Detailed explanation follows. (Attached) Figure 1 This demonstrates the entire process from data acquisition to node partitioning, anomaly detection, and optimization. Figure 2 This describes the system structure, including the connection relationships and functional implementation between the data acquisition module 1, the node partitioning module 2, the anomaly detection module 3, and the adjustment and optimization module 4.

[0058] In practical implementation, the energy management system is a distributed energy network composed of multiple nodes that jointly maintain and manage the operational status of energy equipment. Data acquisition module 1 is responsible for acquiring real-time operational data from the distributed energy equipment and classifying and labeling different energy types within the data. This process is accomplished by connecting to monitoring software to capture and analyze the operational status of the energy equipment. The monitoring software is a tool compatible with real-time monitoring and performance analysis, used to acquire the operating parameters and energy output of the energy equipment. Simultaneously, it classifies and labels the energy types in the operational data, sets monitoring cycles, and statistically analyzes the energy output frequency of each labeled type within the monitoring cycle. The data analysis tool is software that supports data processing and statistical analysis, used to calculate the frequency of energy output for different labeled types.

[0059] In step S2, the node partitioning module 2 calculates the activity index for each marker type based on the energy output frequency of different marker types within the monitoring period. The specific process of node partitioning is as follows: First, the energy output frequencies of different marker types in the operational data are merged into a frequency dataset. The mean and variance of the frequency dataset are calculated. The variance is multiplied by a preset variance scaling factor and then added to the mean. The result is used as the activity threshold. In the operational 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 of the marker type. The average of the activity scores of all marker types is used as the comprehensive activity index of the operational data. Combining the total energy output rate of the energy equipment and the activity index, the nodes in the energy management system are divided into basic nodes, enhanced nodes, or comprehensive nodes. Specifically, the total energy output rate of the energy equipment is standardized using a linear normalization algorithm, and the standardized result is used as the energy output coefficient. The difference between the energy output coefficient and the comprehensive activity index of the operational data is used as the standard value for node partitioning. If the node partitioning standard value is less than 0, the node is classified as a comprehensive node; if the node partitioning standard value is greater than 0 and less than the preset threshold value, the node is classified as an enhanced node; if the node partitioning standard value exceeds the preset threshold value, the node is classified as a basic node.

[0060] In step S3, the anomaly detection module 3 selects the corresponding energy dispatch strategy based on the node type. If the current node is a basic node, low-volatility energy output tasks are prioritized; if the current node is an enhanced node, medium-volatility energy output tasks are prioritized; if the current node is a comprehensive node, high-volatility and diversified demand energy output tasks are prioritized. Comprehensive nodes include both basic and enhanced node functional modes. The module accesses the historical database to obtain the energy output rates of different types of nodes from historical energy data and merges them into a historical rate dataset. The AUC curve analysis method is used to set a deviation threshold and calculate the standard output rate range. The specific steps are as follows: 1. Set a baseline value, using the average value from the historical rate dataset as the baseline value; 2. Classify data into positive and negative values, labeling data in the historical rate dataset above the baseline value as positive data and data below the baseline value as negative data; 3. Classify data by labeling positive and negative data exceeding a preset rate threshold as high positive and high negative data, respectively; 4. Label positive and negative data below a preset rate threshold as low positive and low negative data, respectively; 5. Calculate the ratio, using the ratio formula to calculate the high positive rate and high negative rate respectively; 6. Increment the ratio by setting M different rate thresholds to obtain multiple high positive rates and high negative rates. The high positive rate differences are obtained by subtracting them sequentially from largest to smallest to obtain M-1 high positive rate differences; the high negative rate differences are obtained by subtracting them sequentially from largest to smallest to obtain M-1 high negative rate differences; the deviation threshold is calculated using the obtained high positive rate differences and high negative rate differences; the standard output rate interval is calculated by selecting the maximum and minimum values ​​of the high positive and high negative rates respectively, and using the maximum value of the high positive and high negative rates to calculate the upper interval of the standard output rate; the lower interval of the standard output rate is calculated using the minimum value of the high positive and high negative rates; the range between the upper interval and the lower interval of the standard output rate is taken as the standard output rate interval. When determining whether an energy dispatch anomaly has occurred, the current node's energy output rate is first compared with the standard output rate range. If the energy output rate is within the standard output rate range, the energy dispatch is considered normal. If 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. If the deviation coefficient is lower than the deviation threshold, the energy dispatch is considered normal. If the deviation coefficient exceeds the deviation threshold, the energy dispatch is considered abnormal and is marked.

[0061] In step S4, the adjustment and optimization module 4 detects the energy storage capacity of adjacent nodes with the same label type, and uses the ratio of the energy storage capacity of the current node to that of the previous node as the adjustment coefficient. If the current node is a basic node, an enhancement node, or its type is the same as that of the previous node, then the standard output rate range of the current node is not adjusted; if the current node is a composite node and its type is different from that of the previous node, then 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, and the energy scheduling is reassessed to determine whether any abnormalities have occurred.

[0062] The entire system's workflow is as follows: Data acquisition module 1 first acquires real-time operating data from distributed energy devices and classifies and labels different energy types within the operating data. It sets a monitoring period and statistically analyzes the energy output frequency of each labeled type within the monitoring period. Node partitioning module 2 calculates the activity index for each labeled type based on its energy output frequency within the monitoring period. Combining the total energy output rate of the energy devices with the activity index, it classifies the nodes in the energy management system into basic nodes, enhanced nodes, or integrated nodes. Anomaly detection module 3, before each energy dispatch, selects the corresponding energy dispatch strategy based on the node type, accesses the historical database, acquires the energy output rate of the corresponding node in the historical energy data, calculates the standard output rate range, collects the energy output rate of the current node, calculates its deviation coefficient from the standard output rate range, and determines whether an energy dispatch anomaly has occurred based on the deviation coefficient, marking it accordingly. Adjustment and 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 the adjustment coefficient based on the proportion of energy storage capacity, and adjusts the standard output rate range of the current node based on the types of adjacent nodes and the adjustment coefficient.

[0063] In practical applications, assume an energy management system includes multiple distributed energy devices, such as solar power generation devices, wind power generation devices, and energy storage devices. Real-time operational data of these devices is acquired through data acquisition module 1, and different energy types are categorized and labeled in the operational data. For example, the energy output of solar power generation devices is labeled as "solar energy," and the energy output of wind power generation devices is labeled as "wind energy." The monitoring period is set to one day, and the energy output frequency of each label type is statistically analyzed within that day. Node segmentation module 2 calculates the activity index for each label type based on its daily energy output frequency. 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. 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 a comprehensive node. The anomaly detection module 3 selects the corresponding energy dispatch strategy based on the node type. For example, for basic nodes, it prioritizes low-volatility energy output tasks; for enhanced nodes, it prioritizes medium-volatility energy output tasks; and for integrated nodes, it prioritizes high-volatility and diversified energy output tasks. It accesses the historical database to obtain the energy output rate of the corresponding node in historical energy data and calculates the standard output rate range. It collects the energy output rate of the current node, calculates its deviation coefficient from the standard output rate range, and judges whether energy dispatch is abnormal based on the deviation coefficient, marking it accordingly. The adjustment and 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 marked type, calculates the adjustment coefficient based on the proportion of energy storage capacity, and adjusts the standard output rate range of the current node in combination with the types of adjacent nodes and the adjustment coefficient. For example, if the current node is an integrated node and its type is inconsistent with the previous node, 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 dispatch is re-evaluated for anomalies.

[0064] As can be seen from the above specific implementation methods, the present invention realizes the intelligent partitioning 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] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0066] In step one, data acquisition module 1 first connects to the operating status of distributed energy devices via monitoring software to obtain real-time operating data. For example, in the energy management system of an industrial park, the system connects 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 status, and uploads this data to data acquisition module 1. Subsequently, the data is categorized and labeled according to different energy types, for example, the energy output of solar power generation devices is labeled as "solar energy," and the energy output of wind power generation devices is labeled as "wind energy." Simultaneously, the monitoring period is set to one day, and data analysis tools are used to statistically analyze the energy output frequency of each labeled type within a day. The data analysis tools calculate the high-frequency output of "solar energy" during the day and the possible fluctuating output of "wind energy" at night, thus forming preliminary frequency distribution data.

[0067] In step two, node partitioning module 2 further calculates the activity index for each tag type based on the frequency distribution data provided by data acquisition module 1. Taking an industrial park scenario as an example, assuming that in the operational data of a certain node, the energy output frequency of "solar energy" is relatively high, while the output frequency of "wind energy" is relatively low. Node partitioning module 2 merges these frequencies into a frequency dataset and calculates its mean and variance. Subsequently, the variance is multiplied by a preset variance scaling factor and then added to the mean to obtain 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 lower than the activity threshold, it is marked as a low-activity type. Next, the activity score for each tag type is calculated, and the average of the activity scores of all tag types is taken as the comprehensive activity index. Combining the total energy output rate of the energy equipment, the total energy output rate is standardized using a linear normalization algorithm to obtain the energy output coefficient. The difference between the energy output coefficient and the comprehensive activity index is used as the node partitioning standard value. If the criterion value of a node is less than 0, it is classified as a comprehensive node; if the criterion value is greater than 0 but 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 its solar power generation device.

[0068] In step three, the anomaly detection module 3 selects the corresponding energy dispatch strategy based on the node partitioning results. Taking basic nodes as an example, low-volatility energy output tasks are prioritized, such as providing stable power to the park's lighting system. For enhanced nodes, medium-volatility tasks are prioritized, such as supporting the intermittent power needs of some production equipment. Integrated nodes undertake tasks with high volatility and diverse demands, such as dynamically adjusting the charging and discharging status of energy storage devices. During this process, the anomaly detection module 3 accesses the historical database, obtains the historical energy data of the corresponding nodes, and merges them into a historical rate dataset. Using the AUC curve analysis method, a benchmark value is set, and data in the historical rate dataset 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. Then, the data is graded and labeled, the high positive rate and high negative rate are calculated, and finally the deviation threshold and 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 considered to be within the normal range; if the actual output rate is 120kW, the deviation coefficient between it and the center value of the standard output rate range (75kW) is calculated. If the deviation coefficient exceeds the deviation threshold, it is marked as abnormal.

[0069] In step four, the adjustment and optimization module 4 detects the energy storage capacity of adjacent nodes with the same label type and calculates the adjustment coefficient based on the ratio of the energy storage capacity of the current node to that of the previous node. For example, if the current node is a composite node and its type is different from that of the previous node, the product of the current node's standard output rate range and the adjustment coefficient is used as the adjusted standard output rate range. Assuming the current node's energy storage capacity is 500 kWh and the previous node's energy storage capacity is 1000 kWh, the adjustment coefficient is 0.5. If the current node's standard output rate is 50 kW to 100 kW, the adjusted standard output rate is 25 kW to 50 kW. Subsequently, it is re-evaluated whether there is an anomaly in energy dispatch. For example, if the adjusted standard output rate is 25 kW to 50 kW, but the actual output rate is 60 kW, the deviation coefficient is recalculated and an anomaly is determined.

[0070] Through the above steps, this invention achieves intelligent partitioning and dynamic scheduling optimization of the energy management system. Taking an industrial park as an example, the system can dynamically adjust node types and their energy scheduling strategies based on the output characteristics of solar and wind energy, ensuring the stability and efficiency of energy output. Simultaneously, 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-carbon level of the energy system. For example, when wind energy output fluctuates significantly at night, the system can maintain a stable power supply within the park by adjusting the charging and discharging state of the energy storage devices, while simultaneously reducing carbon emissions.

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

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

[0073] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art 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, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0075] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0076] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for energy-carbon management optimization of source network-load-storage and virtual power plant linkage, characterized in that, The method comprises the following steps, acquiring real-time operation data of distributed energy equipment, classifying and marking the data according to energy types; calculating an activity index of each marked type based on the energy output frequency of different marked types within a monitoring period; combining the total energy output rate with the activity index to dynamically divide nodes of an energy management system into basic nodes, enhanced nodes or comprehensive nodes; selecting a corresponding energy scheduling strategy according to the node type, including: allocating low-fluctuation tasks to the basic nodes, medium-fluctuation tasks to the enhanced nodes and high-fluctuation and diversified tasks to the comprehensive nodes; detecting the types of adjacent nodes and the energy storage capacity of the same marked type; dynamically adjusting the standard output rate interval of the current node according to the storage capacity ratio to realize cross-node collaborative optimization; the node dynamic division comprises: standardizing the total energy output rate by a linear normalization algorithm to generate an energy output coefficient; taking the difference between the energy output coefficient and the comprehensive activity index as a division standard value to dynamically classify into: basic nodes: the standard value exceeds a preset critical value; enhanced nodes: the standard value is between 0 and the critical value; comprehensive nodes: the standard value is less than 0 and supports the function modes of the basic nodes and the enhanced nodes; the activity index is calculated by the following method: combining the energy output frequencies of different marked types into a frequency data set to calculate the mean and variance thereof; adding the product of the preset variance proportion coefficient and the variance to the mean to generate an activity threshold value; when the marked type energy output frequency exceeds the threshold value, it is determined as a high activity type, otherwise as a low activity type; dynamically adjusting the standard output rate interval of the current node according to the storage capacity ratio specifically comprises: extracting the standard output rate interval from historical data based on the AUC curve analysis method; when the current rate deviates from the interval, calculating the deviation coefficient of the current rate from the center value of the interval and comparing it with the dynamic threshold value to determine the abnormality; accessing a historical database to obtain different type energy output rates of the corresponding node in the historical energy data and combining them into a historical rate data set, setting the deviation threshold value and calculating the standard output rate interval by the AUC curve analysis method, the steps are as follows: setting a reference value, taking the mean value in the historical rate data set as the reference value; positive and negative classification, marking the data higher than the reference value in the historical rate data set as positive data and the data lower than the reference value as negative data; grading judgment, adding labels to the positive data and negative data exceeding the preset rate threshold value as high positive data and high negative data respectively, and adding labels to the positive data and negative data lower than the preset rate threshold value as low positive data and low negative data respectively; calculating the ratio, calculating the high positive rate and the high negative rate by the ratio formula respectively; ratio increment, setting M different rate thresholds to obtain multiple high positive rates and high negative rates, and sequentially subtracting the obtained high positive rates from large to small to obtain M-1 high positive rate differences, and sequentially subtracting the obtained high negative rates from large to small to obtain M-1 high negative rate differences; calculating the deviation threshold value by using the obtained high positive rate difference and high negative rate difference; The maximum and minimum values in the high positive value rate and the high negative value rate are selected respectively, the maximum value in the high positive value rate and the high negative value rate is used to calculate the upper interval of the standard output rate, the minimum value in the high positive value rate and the high negative value rate is used to calculate the lower interval of the standard output rate, and the range between the upper interval of the standard output rate and the lower interval of the standard output rate is taken as the standard output rate interval.

2. The energy and carbon management optimization method of source network load storage and virtual power plant linkage according to claim 1, characterized in that: The adjacent node cooperative adjustment comprises: If the current node is a comprehensive node and is inconsistent with the type of the previous node, then: The standard output rate interval of the current node is multiplied by the adjustment coefficient to generate an adjusted rate interval; Redetermine the abnormality and optimize the scheduling strategy; The adjustment coefficient is the energy storage capacity ratio of the current node and the previous node.

3. The energy and carbon management optimization method of source network load storage and virtual power plant linkage according to claim 1, characterized in that: When determining whether the energy scheduling is abnormal, first compare the energy output rate of the current node with the standard output rate interval, if the energy output rate is within the standard output rate interval, then determine that the energy scheduling is not abnormal; If the energy output rate is outside the standard output rate interval, then the deviation coefficient is obtained by subtracting the center value of the standard output rate interval from the energy output rate; If the deviation coefficient is lower than the deviation threshold, then determine that the energy scheduling is not abnormal; If the deviation coefficient exceeds the deviation threshold, then determine that the energy scheduling is abnormal and mark it.

4. The energy and carbon management optimization method of source network load storage and virtual power plant linkage according to claim 1, characterized in that: Detect the energy storage capacity of the same marked type in the adjacent nodes, take 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, then adjust the standard output rate interval of the current node according to the type and adjustment coefficient of the adjacent nodes, the specific steps are as follows: If the type of the current node is a basic node, an enhanced node, or the type of the current node is consistent with the type of the previous node, then do not adjust the standard output rate interval of the current node; 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, then take the product of the standard output rate interval of the current node and the adjustment coefficient as the adjusted standard output rate interval, and redetermine whether the energy scheduling is abnormal.

5. A source-grid-load-storage and virtual power plant linkage energy-carbon management optimization system for implementing the source-grid-load-storage and virtual power plant linkage energy-carbon management optimization method of any one of claims 1-4, characterized in that, It comprises a data collection module, a node division module, an abnormality detection module, and an adjustment optimization module; The data collection module is used to mark and collect the total energy output rate of the energy equipment and the frequency of energy output of different marked types after screening the energy types in the operation data; The node division module is used to set the activity threshold according to the frequency of energy output of different marked types, screen out high activity types in the operation data, evaluate the comprehensive activity index of the operation data, and divide the type of the current node; The adjustment coefficient is the energy storage capacity ratio of the current node and the previous node. The anomaly detection module is configured to calculate a standard output rate interval and set a deviation threshold value through historical energy data, calculate a deviation coefficient according to a node energy output rate and the standard output rate interval, and compare the deviation coefficient with the deviation threshold value to determine whether an anomaly occurs in energy scheduling of the current node. The adjustment optimization module is configured to adjust the standard output rate interval of the current node according to types of adjacent nodes in the energy management system and energy storage capacities of the same marker type in the adjacent nodes.

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