Data sharing method, system and equipment based on virtual power plant and medium

Abstract

The invention discloses a data sharing method, system and equipment based on a virtual power plant and a medium, particularly relates to the technical field of power data collaboration, and is used for solving the problems of multi-source collaboration failure and resource misscheduling caused by data flow asynchronism of an existing static priority rule. The method comprises the following steps: acquiring operation data of distributed resources in real time through a shared communication network, marking a data stream based on a transmission delay state, generating a dynamic priority compensation coefficient for delay data in combination with a resource type and a power grid frequency modulation demand level, and fusing an initial priority rule to generate a real-time dynamic sequence; and furthermore, the influence of the scheduling instruction is simulated through the physical security constraint index of the power grid, the priority sequence is corrected in a closed-loop manner based on the security margin evaluation result, and finally a resource scheduling instruction is generated, so that efficient calling of low-cost resources and collaborative optimization of the operation reliability of the power grid in a data asynchronous scene are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power data collaboration. More specifically, the present invention relates to a data sharing method, system, device, and medium based on a virtual power plant. Background Art

[0002] A virtual power plant (VPP) realizes multi-source data sharing and collaborative decision-making by aggregating distributed energy resources (such as energy storage, photovoltaic, and controllable loads). Its scheduling efficiency highly depends on the real-time and collaborative nature of data transmission. In the prior art, the data sharing mechanism ensures the timeliness of key data streams by presetting the communication link priority (such as the energy storage data channel having priority over the load channel). Such methods are based on the synchronization assumption of real-time data streams and scheduling instructions, that is, the transmission reliability of the communication network can meet the scheduling timing requirements.

[0003] However, static priority allocation is difficult to adapt to dynamic operation scenarios. For example, in high-frequency power market trading, due to the high communication link priority of energy storage devices, their data streams are preferentially processed, while the data streams of low-cost resources such as controllable loads have the risk of transmission delay or packet loss due to communication layer restrictions, resulting in the failure of multi-source data collaboration. That is, the data streams of high-cost energy storage resources arrive on time and are preferentially invoked, while the delayed low-cost load regulation data is ignored, leading to a decrease in resource utilization efficiency and an increase in operating costs. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a data sharing method, system, device, and medium based on a virtual power plant to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A data sharing method based on a virtual power plant, comprising the following steps: S1. Obtain the real-time operation data of the distributed energy resources of the virtual power plant through a shared communication network, including resource type identifiers and adjustment cost parameters; S2. Judge the transmission delay status of the real-time operation data in the shared communication network, and generate data stream sets with delay marks and non-delay marks; S3. For the non-delay marked data, match the preset initial priority rules based on the resource type identifiers; for the delay marked data, jointly determine the attenuation factor according to the resource type identifiers and the grid frequency modulation demand level, and generate a dynamic priority compensation coefficient based on the adjustment cost parameters and the attenuation factor; S4. Integrate the initial priority rules and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence; S5. Simulate the impact of different scheduling instruction combinations on the physical security constraint indicators of the power grid based on the real-time dynamic priority sequence, and generate the safety margin evaluation results; S6. Adjust the real-time dynamic priority sequence according to the safety margin evaluation results, generate resource scheduling instructions, and distribute them to the corresponding resources for execution through the shared communication network.

[0006] In a preferred embodiment, obtain the real-time operation data of the distributed energy resources of the virtual power plant through the shared communication network, including the resource type identifier and the adjustment cost parameter, including: Obtain the real-time operation data of the distributed energy resources of the virtual power plant through the shared communication network at a preset acquisition frequency; Convert the real-time operation data into a unified data format, and the unified data format includes a resource type identifier field and an adjustment cost parameter field; Verify the validity of the resource type identifier field and the adjustment cost parameter field, and eliminate invalid data entries.

[0007] In a preferred embodiment, judge the transmission delay status of the real-time operation data in the shared communication network, and generate a data stream set of delay marks and non-delay marks, including: Obtain the time stamp of the real-time operation data, and the time stamp includes the data generation time and the reception time; Calculate the transmission time according to the data generation time and the reception time, and the transmission time is equal to the reception time minus the data generation time; Compare the transmission time with the preset delay threshold, and mark the data with the transmission time exceeding the preset delay threshold as delay-marked data, otherwise mark it as non-delay-marked data; Classify the delay-marked data and the non-delay-marked data according to the resource type identifier, and generate a delay-marked data stream set and a non-delay-marked data stream set classified by resource type.

[0008] In a preferred embodiment, for the non-delay-marked data, match the preset initial priority rule based on the resource type identifier, including: Obtain the preset initial priority rule table, and the initial priority rule table contains the mapping relationship between the resource type identifier and the initial priority value; Query the initial priority rule table according to the resource type identifier field in the non-delay-marked data, and match the corresponding initial priority value; Perform weighted summation on the matched initial priority value and the adjustment cost parameter in the non-delay-marked data to generate an initial priority score; Write the initial priority score into the priority field of the non-delay-marked data to generate a non-delay data stream set with an initial priority score; Generate a dynamic priority compensation coefficient for delayed marked data, including: Determine a reference attenuation factor according to the grid frequency regulation demand level. When the grid frequency regulation demand level is urgent, the reference attenuation factor is the first preset value; when it is normal, it is the second preset value; when it is loose, it is the third preset value; Dynamically adjust the reference attenuation factor based on the resource type identifier corresponding to the delayed marked data and the real-time schedulable capacity. If the resource type identifier is energy storage and the real-time schedulable capacity is greater than the capacity threshold, the reference attenuation factor increases the compensation amount; otherwise, it decreases the compensation amount; Take the ratio of the dynamically adjusted reference attenuation factor to the regulation cost parameter as the dynamic priority compensation coefficient; Perform a secondary correction on the dynamic priority compensation coefficient according to the grid node voltage over-limit risk level. If the grid node voltage over-limit risk level is high risk, apply an inhibition factor to the dynamic priority compensation coefficient.

[0009] In a preferred embodiment, fuse the initial priority rule and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence, including: Classify and stratify the initial priority scores of the non-delayed data stream set and the dynamic priority compensation coefficients of the delayed data stream set according to the coupling relationship between the grid frequency regulation demand level and the resource type identifier; Dynamically adjust the hierarchical priority order based on the real-time grid frequency deviation change direction: when the frequency has a positive deviation, give priority to calling energy storage resources; when it has a negative deviation, give priority to calling load resources; Sort the non-delayed data and the delayed data in a multi-dimensional cross-order according to the resource type identifier and the grid frequency regulation demand level to generate a priority subsequence in the dimension of resource type - frequency regulation level; Perform a safety screening on the priority subsequence according to the grid node voltage over-limit risk level: at a high risk level, eliminate the resource entries that may exacerbate the voltage problem; at a medium or low risk level, retain the complete priority subsequence; Merge all the safety-screened priority subsequences and generate a real-time dynamic priority sequence according to the global priority rule of the grid frequency regulation demand level and the resource type identifier.

[0010] In a preferred embodiment, simulate the impact of different scheduling instruction combinations on the grid physical security constraint index based on the real-time dynamic priority sequence to generate a safety margin evaluation result, including: Generate multiple candidate scheduling instruction combinations according to the real-time dynamic priority sequence. Each candidate scheduling instruction combination contains scheduling instructions of different resource type identifiers and corresponding regulation quantity parameters; Perform a simulation of the grid physical security constraints on each candidate scheduling instruction combination based on the current operating parameters of the grid, and predict the change value of the grid physical security constraint index after executing the candidate scheduling instruction combination; Compare the predicted change value of the grid physical security constraint index with the preset safety threshold of the grid physical security constraint index to generate a safety margin evaluation value for each candidate dispatching instruction combination; Screen the candidate dispatching instruction combinations with the safety margin evaluation value greater than or equal to the preset safety margin evaluation threshold to generate a safety margin evaluation result set.

[0011] In a preferred embodiment, adjust the real-time dynamic priority sequence according to the safety margin evaluation result, generate a resource dispatching instruction and distribute it to the corresponding resource for execution through a shared communication network, including: Adjust the real-time dynamic priority sequence according to the safety margin evaluation value in the safety margin evaluation result set. If the safety margin evaluation value of the candidate dispatching instruction combination is lower than the preset safety margin evaluation threshold, remove the corresponding resource entry from the real-time dynamic priority sequence; Generate a resource dispatching instruction based on the adjusted real-time dynamic priority sequence. The resource dispatching instruction includes a resource type identifier, an adjustment amount parameter, and an execution time window; Distribute the resource dispatching instruction to the corresponding distributed energy resource terminal device through a shared communication network; After the distributed energy resource terminal device receives the resource dispatching instruction, execute the adjustment amount parameter according to the control logic corresponding to the resource type identifier, and feedback the execution result to the virtual power plant aggregation control platform through the shared communication network.

[0012] On the other hand, the present invention provides a data sharing system based on a virtual power plant, including: Real-time acquisition module: Obtain the real-time operation data of the distributed energy resources of the virtual power plant through a shared communication network, including a resource type identifier and an adjustment cost parameter; Delay marking module: Judge the transmission delay status of the real-time operation data in the shared communication network to generate a data stream set of delay marks and non-delay marks; Dynamic compensation module: For non-delay marked data, match the preset initial priority rule based on the resource type identifier; for delay marked data, jointly determine the attenuation factor according to the resource type identifier and the grid frequency modulation demand level, and generate a dynamic priority compensation coefficient based on the adjustment cost parameter and the attenuation factor; Sequence generation module: Integrate the initial priority rule and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence; Safety evaluation module: Simulate the influence of different dispatching instruction combinations on the grid physical security constraint index based on the real-time dynamic priority sequence to generate a safety margin evaluation result; Instruction distribution module: Adjust the real-time dynamic priority sequence according to the safety margin evaluation result, generate a resource dispatching instruction and distribute it to the corresponding resource for execution through a shared communication network.

[0013] On the other hand, the present invention provides a data sharing device based on a virtual power plant, including: a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, a data sharing method based on a virtual power plant is implemented.

[0014] On the other hand, the present invention provides a data sharing medium based on a virtual power plant. A program or instruction is stored on the medium. When the program or instruction is executed by a processor, a data sharing method based on a virtual power plant is implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By optimizing the dynamic sharing mechanism of multi-source data streams, the problem of multi-source collaboration failure caused by data asynchrony under traditional static priority rules is solved. Based on the acquisition and transmission status marking of real-time operation data on the shared communication network, the system dynamically identifies delayed and non-delayed data streams, and generates a priority compensation coefficient by adjusting the cost parameter and the frequency modulation demand level, so that the delayed data of low-cost resources can still participate in the scheduling decision through dynamic weight adjustment, realizing the collaborative optimization of multi-source heterogeneous data in the transmission delay scenario and reducing the risk of resource mis-scheduling caused by data stream asynchrony; 2. Through the closed-loop linkage of data stream status marking and power grid security constraints, the guarantee ability of the data sharing process for power grid operation safety is further strengthened. After dynamically generating the priority sequence, the data sharing weight is reversely corrected based on the safety margin evaluation to ensure that the scheduling instruction not only meets the economic target but also satisfies the physical safety boundary of the power grid, improving the autonomous collaboration ability of the virtual power plant for multi-source data in the high-frequency trading scenario and enhancing the reliability of the complex power market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a data sharing method based on a virtual power plant according to the present invention; Figure 2 is a schematic structural diagram of a data sharing system based on a virtual power plant according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1: Figure 1 A data sharing method based on a virtual power plant according to the present invention is given, which includes the following steps: S1. Obtaining real-time operation data of distributed energy resources of virtual power plants through a shared communication network, including resource type identification and adjustment cost parameters; S2, determining the transmission delay state of the real-time operation data in the shared communication network, and generating a set of data streams marked with delay and those marked with non-delay; S3. For non-delayed marked data, the preset initial priority rule is matched based on the resource type identifier; for delayed marked data, the attenuation factor is determined in coordination with the resource type identifier and the grid frequency regulation demand level, and a dynamic priority compensation coefficient is generated based on the regulation cost parameter and the attenuation factor; S4, integrating the initial priority rule and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence; S5. Simulate the impact of different dispatch instruction combinations on the physical security constraint indicators of the power grid based on the real-time dynamic priority sequence to generate a safety margin assessment result; S6. Adjust the real-time dynamic priority sequence according to the safety margin assessment result, generate resource scheduling instructions and distribute them to corresponding resources for execution through the shared communication network.

[0019] Obtain real-time operating data of distributed energy resources of virtual power plants through a shared communication network, including resource type identification and adjustment cost parameters, including: Acquire real-time operation data of distributed energy resources of virtual power plants through a shared communication network at a preset collection frequency; Converting real-time operation data into a unified data format, wherein the unified data format includes a resource type identification field and an adjustment cost parameter field; Verify the validity of the resource type identification field and the adjustment cost parameter field, and eliminate invalid data entries.

[0020] The preset collection frequency is set according to the dispatch requirements of the virtual power plant, including but not limited to collecting data once per second for energy storage devices and once per minute for controllable loads. In specific implementation, the collection frequency is configured through the virtual power plant aggregation control platform, which dynamically adjusts the collection frequency according to the resource type identifier. For example, when the light intensity fluctuation of photovoltaic equipment exceeds the preset threshold, the collection frequency is automatically increased to twice per second.

[0021] The unified data format adopts the JSON structured data format. Among them, the resource type identification field is named "resource_type", and the field value is a preset enumeration type (including "energy storage", "photovoltaic", "controllable load"); the regulation cost parameter field is named "cost_parameter", and the field value is a floating point number representing the cost per unit regulation amount (unit: yuan / kWh). In specific implementation, the data format conversion is implemented through the edge computing node. After receiving the original data, the edge computing node parses the device number in the original data according to the resource type identification, and queries the pre-stored mapping table of regulation cost parameters to map the device number to the corresponding regulation cost parameter value.

[0022] The validity verification includes the following rules: the value of the resource type identification field must be within the range of the preset enumeration type; the value of the regulation cost parameter field must be greater than or equal to zero and less than the preset upper threshold (for example, 100 yuan / kWh). In specific implementation, the edge computing node executes the verification logic for each piece of data. If the resource type identification field is "energy storage" but the regulation cost parameter is greater than 50 yuan / kWh, it is determined as abnormal data and excluded; if the resource type identification field is an undefined enumeration value (such as "wind turbine"), it is marked as invalid data and discarded. After the invalid data is excluded, the remaining data entries are transmitted to the virtual power plant aggregation control platform through the shared communication network for subsequent processing.

[0023] It should be noted that the shared communication network can be a 5G / blockchain network for multi-agent collaboration.

[0024] Judge the transmission delay status of the real-time operation data in the shared communication network, and generate a data stream set of delay marks and non-delay marks, including: Obtain the timestamp of the real-time operation data, where the timestamp includes the data generation time and the reception time; Calculate the transmission time according to the data generation time and the reception time, and the transmission time is equal to the reception time minus the data generation time; Compare the transmission time with the preset delay threshold. The data with the transmission time exceeding the preset delay threshold is marked as delay-marked data, otherwise it is marked as non-delay-marked data; Classify the delay-marked data and the non-delay-marked data according to the resource type identification, and generate a delay-marked data stream set and a non-delay-marked data stream set classified by resource type. The data generation time is recorded by the terminal device of distributed energy resources when generating real-time operation data. The terminal device is built with a real-time clock chip, and the time signal of the real-time clock chip is synchronized with the reference clock of the virtual power plant aggregation control platform through the Network Time Protocol, with a time synchronization error of less than 1 millisecond. The reception time is recorded by the virtual power plant aggregation control platform when receiving data through the shared communication network, and the recording accuracy is at the millisecond level. The format of the timestamp adopts Coordinated Universal Time. The data generation time is written into the "generation_time" field of the data header, and the reception time is written into the "reception_time" field of the data tail. For example, the terminal device of a photovoltaic device generates real-time operation data when the light intensity changes by more than 5%, and writes the generation time "2023-10-05T08:00:00.500Z" into the data header; after receiving it, the aggregation control platform records the reception time "2023-10-05T08:00:01.200Z".

[0025] When calculating the transmission time in specific implementation, the virtual power plant aggregation control platform parses the "generation_time" field of the data header and the "reception_time" field of the data tail, converts the two timestamps into millisecond-level timestamps in the same time zone, and performs a subtraction operation to obtain the transmission time. For example, the generation time of an energy storage device is "2023-10-05T08:00:00.500Z", the reception time is "2023-10-05T08:00:01.200Z", and the transmission time is 700 milliseconds. If the time zones of the data generation time and the reception time are inconsistent, the aggregation control platform first converts the two timestamps into Coordinated Universal Time and then calculates the difference.

[0026] The preset delay threshold is set according to the resource type identifier: 500 milliseconds for energy storage resources, 800 milliseconds for photovoltaic resources, and 2000 milliseconds for controllable load resources. The threshold is set based on the maximum allowable delay of different types of resources. The aggregation control platform pre-stores a threshold mapping table, and the table structure includes two columns: "resource type identifier" and "preset delay threshold". For example, when the resource type identifier is "energy storage", query the mapping table to get the threshold of 500 milliseconds. When the transmission time of a certain energy storage data is 700 milliseconds, the aggregation control platform determines that it exceeds the threshold and marks it as delayed marked data; if the transmission time of a certain controllable load data is 1500 milliseconds, it is marked as non-delayed marked data.

[0027] When generating the delayed-tag data stream set and non-delayed-tag data stream set classified by resource type, the aggregation control platform extracts the value of the "resource_type" field in the real-time operation data, and stores the delayed-tag data and non-delayed-tag data into independent sets named after the resource type identifier. For example, the delayed-tag data with the resource type identifier of "energy storage" is stored in the "energy storage_delayed" set, and the non-delayed-tag data is stored in the "energy storage_non-delayed" set. Each set is stored in the JSON array format, and the array elements include the original data packet header, packet tail, and tag status field, and the value of the tag status field is "delayed" or "non-delayed". The upper limit of the storage capacity of the set is set according to the resource type. The upper limit of the energy storage type set is 1000 items, and that of the photovoltaic type is 2000 items. When the upper limit is reached, the old data is overwritten according to the first-in-first-out rule.

[0028] For the non-delayed-tag data, match the preset initial priority rules based on the resource type identifier, including: Obtain the preset initial priority rule table, which contains the mapping relationship between the resource type identifier and the initial priority value; Query the initial priority rule table according to the resource type identifier field in the non-delayed-tag data to match the corresponding initial priority value; Perform a weighted sum of the matched initial priority value and the adjustment cost parameter in the non-delayed-tag data to generate an initial priority score; Write the initial priority score into the priority field of the non-delayed-tag data to generate a non-delayed data stream set with the initial priority score.

[0029] The initial priority rule table is preset by the virtual power plant operator according to the historical dispatching efficiency of the resource type and the grid regulation requirements, and is stored in the configuration database of the virtual power plant aggregation control platform. The table structure of the initial priority rule table includes two columns: "resource type identifier" and "initial priority value". Among them, the resource type identifier is an enumerated value (such as "energy storage", "photovoltaic", "controllable load"), and the initial priority value is an integer from 1 to 10. The larger the value, the higher the priority. For example, when the resource type identifier is "energy storage", the corresponding initial priority value is 8, and when the resource type identifier is "controllable load", the corresponding initial priority value is 5. The initial priority rule table is imported during the initialization stage of the virtual power plant and supports dynamic update according to the grid operation status. For example, when the grid frequency deviation continuously exceeds 0.2Hz, the initial priority value of "energy storage" is increased to 9.

[0030] When matching the corresponding initial priority value, in specific implementation, the virtual power plant aggregation control platform extracts the value of the "resource_type" field (such as "energy storage") from the non-delayed marked data, queries the initial priority rule table with this field value as the index, and returns the corresponding initial priority value (such as 8). If the value of the resource type identification field is not defined in the rule table (such as "wind turbine"), the initial priority value of this data is set to the default value of 1. For example, the resource type identification of a certain photovoltaic data is "photovoltaic", and the initial priority value 6 is obtained by querying the rule table; another data with an undefined resource type is marked with the default priority value of 1 and an alarm log is triggered.

[0031] When generating the initial priority score, the weights for weighted summation are dynamically set according to the resource type identification. The weight of the initial priority value for energy storage resources is 0.7, and the weight of the regulation cost parameter is 0.3; the weight of the initial priority value for controllable load resources is 0.4, and the weight of the regulation cost parameter is 0.6. In specific implementation, the virtual power plant aggregation control platform queries the preset weight mapping table according to the resource type identification. For example, when the resource type identification is "energy storage", the initial priority score = initial priority value × 0.7 + regulation cost parameter × 0.3. If the initial priority value of a certain energy storage data is 8 and the regulation cost parameter is 2 yuan / kWh, the initial priority score is 8 × 0.7 + 2 × 0.3 = 6.2. The weight mapping table is stored in the configuration database and supports dynamic adjustment according to the market electricity price volatility. For example, when the real-time electricity price volatility exceeds 10%, the weight of the regulation cost parameter for controllable load is increased to 0.8.

[0032] Write the initial priority score into the priority field of the non-delayed marked data to generate a set of non-delayed data streams with initial priority scores. A new field "priority_score" is added to the JSON format of the non-delayed marked data, and the field value is the calculated initial priority score. For example, the value 6.2 is written into the "priority_score" field of a certain energy storage data, and 4.8 is written into the photovoltaic data. All non-delayed data with priority scores are classified and stored in independent data stream sets according to the resource type, such as the "energy storage_non-delayed_with_score" set and the "controllable load_non-delayed_with_score" set. The storage format of the data stream set is the same as that of the delayed marked data stream set generated in step S2.

[0033] For the delayed marked data, determine the attenuation factor in coordination with the resource type identification and the grid frequency regulation demand level, and generate a dynamic priority compensation coefficient based on the regulation cost parameter and the attenuation factor, including: Determine the reference attenuation factor according to the grid frequency regulation demand level. The reference attenuation factor is the first preset value when the grid frequency regulation demand level is urgent, the second preset value when it is normal, and the third preset value when it is loose; Dynamically adjust the baseline attenuation factor based on the resource type identifier corresponding to the delayed tagged data and the real-time schedulable capacity. If the resource type identifier is energy storage and the real-time schedulable capacity is greater than the capacity threshold, the baseline attenuation factor increases by the compensation amount; otherwise, it decreases by the compensation amount. Use the ratio of the dynamically adjusted baseline attenuation factor to the adjustment cost parameter as the dynamic priority compensation coefficient. Perform a secondary correction on the dynamic priority compensation coefficient according to the grid node voltage over-limit risk level. If the grid node voltage over-limit risk level is high risk, apply an inhibition factor to the dynamic priority compensation coefficient.

[0034] Determine the baseline attenuation factor according to the grid frequency regulation demand level: For example, when the absolute value of the real-time grid frequency deviation exceeds 0.5 Hz, it is determined as the emergency level, and the baseline attenuation factor is set to 0.9; when the deviation is between 0.2 Hz and 0.5 Hz, it is the normal level, and the baseline attenuation factor is set to 0.6; when the deviation is less than 0.2 Hz, it is the loose level, and the baseline attenuation factor is set to 0.3. The values are determined by analyzing the regulation success rate and cost-benefit ratio of energy storage resources in different scenarios in the virtual power plant historical dispatch database. For example, in the past year, among 100 emergency scenarios (deviation > 0.5 Hz), the average regulation success rate of energy storage resources was 92% (i.e., more than 90%), corresponding to a baseline attenuation factor of 0.9; the regulation success rate in normal scenarios (deviation 0.2 Hz - 0.5 Hz) was 63%, corresponding to 0.6; the regulation success rate in loose scenarios (deviation < 0.2 Hz) was 28%, corresponding to 0.3. This value setting ensures that the resource priority is increased in high-success scenarios and suppressed in low-success scenarios.

[0035] For example, if the resource type identifier of the delayed tagged data is "energy storage" and its real-time schedulable capacity (SOC) is greater than 80%, the baseline attenuation factor increases by 0.2; if the SOC is less than or equal to 80%, it decreases by 0.2. The SOC threshold is determined by statistically analyzing the historical operation data of energy storage devices in the virtual power plant operation log: By analyzing 500 past dispatch records, it is found that when SOC > 80%, the average regulation amount of energy storage devices is 20% higher than when SOC ≤ 80% (for example, when SOC is 85%, the regulation amount is 100 kW, and when SOC is 75%, the regulation amount is 80 kW). To reflect the difference in regulation efficiency, the compensation amount is set to 0.2 (a 20% increase in regulation amount corresponds to a 0.2 increase in priority). For example, for an energy storage device with an SOC of 85%, the baseline attenuation factor is adjusted from 0.9 to 1.1; when the SOC is 75%, it is adjusted to 0.7. Non-energy storage resources (such as photovoltaic, load) do not have capacity adjustment because their regulation ability has no direct relationship with capacity.

[0036] Take the ratio of the adjusted baseline attenuation factor to the adjustment cost parameter as the dynamic priority compensation coefficient. The adjustment cost parameter is derived from the cost per unit of adjustment amount (yuan / kWh) reported during resource registration and written into the "cost_parameter" field in a unified data format. For example, if the baseline attenuation factor of a certain energy storage device is 1.1 and the adjustment cost is 5 yuan / kWh, the compensation coefficient is 1.1 / 5 = 0.22; if the baseline attenuation factor of a certain photovoltaic device is 0.6 and the adjustment cost is 3 yuan / kWh, the compensation coefficient is 0.6 / 3 = 0.2. The calculation result is rounded to two decimal places and written into the "compensation_coefficient" field for subsequent steps to call.

[0037] Perform a secondary correction on the dynamic priority compensation coefficient according to the risk level of grid node voltage over-limit: for example, if the voltage deviation exceeds 5%, it is determined as a high-risk situation, and the dynamic priority compensation coefficient is multiplied by the suppression factor 0.5; when the deviation is between 3% and 5%, it is a medium-risk situation, multiplied by 0.8; when the deviation is less than 3%, it is a low-risk situation, multiplied by 1.0.

[0038] The suppression factor is set by analyzing the contribution of resource regulation to voltage stability in historical voltage over-limit events. For example, among 50 high-risk events with voltage deviation > 5%, the average contribution of resource regulation to voltage recovery decreases by 50%, so the suppression factor is set to 0.5; when the deviation is 3% - 5%, the contribution decreases by 20%, and the suppression factor is 0.8. For example, if the voltage deviation of a certain node is 6%, the compensation coefficient 0.22 is corrected to 0.22 × 0.5 = 0.11; when the deviation is 4%, it is corrected to 0.22 × 0.8 = 0.176. The corrected coefficient is updated to the "compensation_coefficient" field.

[0039] Fuse the initial priority rules and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence, including: Classify and stratify the initial priority scores of the non-delay data stream set and the dynamic priority compensation coefficients of the delay data stream set according to the coupling relationship between the grid frequency regulation demand level and the resource type identifier; Dynamically adjust the hierarchical priority order based on the change direction of the real-time grid frequency deviation: when the frequency has a positive deviation, give priority to calling energy storage resources; when there is a negative deviation, give priority to calling load resources; Sort the non-delay data and the delay data in multiple dimensions according to the resource type identifier and the grid frequency regulation demand level to generate a priority subsequence in the dimension of resource type - frequency regulation level; Perform a safety screening on the priority subsequence according to the risk level of grid node voltage over-limit: under the high-risk level, eliminate resource entries that may exacerbate voltage problems, and retain the complete priority subsequence at the medium and low-risk levels; Merge all the priority subsequences after security screening and generate a real-time dynamic priority sequence according to the global priority rules identified by the grid frequency regulation demand level and resource type.

[0040] According to the coupling relationship between the grid frequency regulation demand level and the resource type identifier, classify and stratify the initial priority scores of the non-delayed data stream set and the dynamic priority compensation coefficients of the delayed data stream set. The classification and stratification rules are as follows: when the grid frequency regulation demand level is urgent, classify the initial priority scores and dynamic priority compensation coefficients of energy storage resources into the first layer, and load resources into the second layer; under the normal level, all resource types are mixed into the same layer according to the numerical size of the initial priority scores and compensation coefficients; under the loose level, classify them into different layers according to the ascending order of the regulation cost parameters. For example, under the urgent level, the initial priority score (such as 8.0) and compensation coefficient (such as 0.22) of energy storage resources are both classified into the first layer, and the score (such as 5.0) and coefficient (such as 0.15) of controllable loads are classified into the second layer.

[0041] Dynamically adjust the hierarchical priority order based on the change direction of the real-time grid frequency deviation: when the frequency deviation is positive (i.e., the frequency is higher than the rated value), give priority to calling energy storage resources to absorb excess electric energy; when the frequency deviation is negative (the frequency is lower than the rated value), give priority to calling controllable load resources to cut electricity demand. For example, when a positive frequency deviation of 0.3 Hz is detected, place the first-layer subsequence of energy storage resources at the top of the sorting queue; if the frequency deviation is -0.4 Hz, place the second-layer subsequence of load resources at the top. The frequency deviation direction is determined by comparing the real-time frequency value with the rated value (such as 50 Hz or 60 Hz), and the deviation calculation method is the current frequency value minus the rated value.

[0042] Perform multi-dimensional cross-sorting on non-delayed data and delayed data according to the resource type identifier and the grid frequency regulation demand level to generate a priority subsequence in the dimension of resource type - frequency regulation level. The cross-sorting rules are as follows: under the same resource type, data entries at the urgent level take precedence over those at the normal level, and the normal level takes precedence over the loose level; under the same frequency regulation level, non-delayed data takes precedence over delayed data. For example, the non-delayed data (initial score 8.0) of energy storage resources at the urgent level is ranked before the delayed data (compensation coefficient 0.22) at the urgent level, and the non-delayed data (score 6.0) at the normal level is ranked before the delayed data (coefficient 0.18) at the normal level. All subsequences are stored in the JSON array format, and the elements in the array include fields such as resource identifier, priority value, and frequency regulation level identifier.

[0043] Perform a safety screening on the priority subsequence according to the risk level of grid node voltage over-limit: If the execution of the scheduling instruction for a certain resource may exacerbate the voltage over-limit problem, then remove the resource entry from the subsequence at the high risk level. For example, when the voltage deviation of a node has reached 5.2%, if the charge and discharge operation of a certain energy storage device will further increase the voltage to 5.8%, it is determined as high risk and removed; if the voltage deviation is 3.5%, then the entry is retained. The voltage over-limit risk prediction is achieved through historical data analysis. Statistically analyze the influence trend of the adjustment behavior of similar resources in similar voltage deviation scenarios on the voltage. If more than 80% of the cases lead to an increase in the deviation, it is marked as a high-risk resource.

[0044] Merge all the priority subsequences after safety screening, and generate a real-time dynamic priority sequence according to the global priority rules of the grid frequency regulation demand level and resource type identification. The merging rule is: The emergency level subsequence as a whole takes precedence over the normal level, and the normal level takes precedence over the loose level; under the same frequency regulation level, they are arranged in the preset order according to the resource type identification (such as energy storage > photovoltaic > load). For example, the energy storage subsequence at the emergency level is ranked before all the normal level subsequences, and the load subsequence at the normal level is ranked after all the subsequences at the loose level. In the finally generated real-time dynamic priority sequence, each piece of data includes fields such as resource identification, priority value, frequency regulation level identification, and safety screening status.

[0045] Based on the real-time dynamic priority sequence, simulate the influence of different scheduling instruction combinations on the grid physical security constraint indicators, and generate a safety margin evaluation result, including: Generate multiple candidate scheduling instruction combinations according to the real-time dynamic priority sequence. Each candidate scheduling instruction combination includes scheduling instructions of different resource type identifications and corresponding adjustment parameter values; Based on the current operating parameters of the grid, perform a simulation of the grid physical security constraints for each candidate scheduling instruction combination, and predict the change value of the grid physical security constraint indicators after executing the candidate scheduling instruction combination; Compare the predicted change value of the grid physical security constraint indicators with the preset safety threshold of the grid physical security constraint indicators to generate a safety margin evaluation value for each candidate scheduling instruction combination; Screen out the candidate scheduling instruction combinations with safety margin evaluation values greater than or equal to the preset safety margin evaluation threshold to generate a set of safety margin evaluation results.

[0046] The real-time dynamic priority sequence is derived from the priority sorting result generated in step S4. The candidate scheduling instruction combinations are selected in descending order of priority, and each combination contains at least two scheduling instructions with resource type identifiers. For example, the first 5 resource entries are selected from the priority sequence to generate a scheduling instruction combination including energy storage, photovoltaic, and controllable load. The adjustment parameter of the energy storage instruction is the charge-discharge power value (such as 100 kW), the photovoltaic instruction is the output adjustment value (such as 50 kW), and the load instruction is the reduction value (such as 80 kW). The adjustment parameter is set according to the historical adjustment ability range corresponding to the resource type identifier. The charge-discharge power value of the energy storage device does not exceed 90% of its rated capacity, and the photovoltaic output adjustment value does not exceed the maximum adjustable amount under the current light condition.

[0047] The current operating parameters of the power grid include the real-time values of node voltage, line current, and power grid frequency, which are from the real-time data acquisition interface of the power grid monitoring system. The simulation process uses a power flow calculation method based on Kirchhoff's laws and power balance equations to predict the voltage deviation values of each node, the change in line load rate, and the change in power grid frequency deviation after executing the scheduling instructions. For example, after simulating a certain candidate scheduling instruction combination, it is predicted that the voltage of node A will rise from 10 kV to 10.5 kV (deviation 5%), the load rate of line L1 will increase from 80% to 85%, and the power grid frequency will drop from 50.0 Hz to 49.8 Hz (deviation -0.2 Hz).

[0048] Compare the predicted change values of the power grid physical security constraint indicators with the preset safety thresholds of the power grid physical security constraint indicators to generate the safety margin evaluation value for each candidate scheduling instruction combination. The safety thresholds of the power grid physical security constraint indicators include the node voltage deviation safety threshold (such as ±5%), the line load rate safety threshold (such as 90%), and the power grid frequency deviation safety threshold (such as ±0.5 Hz).

[0049] The calculation method of the safety margin evaluation value is as follows: Take the percentage of the difference between the node voltage deviation safety threshold and the predicted voltage deviation value in the safety threshold, the percentage of the difference between the line load rate safety threshold and the predicted load rate value in the safety threshold, and the percentage of the difference between the power grid frequency deviation safety threshold and the predicted frequency deviation value in the safety threshold. The minimum value of the three is used as the safety margin evaluation value for this combination. For example, for a certain combination, the predicted voltage deviation is 4% (the difference percentage is (5% - 4%) / 5% = 20%), the load rate is 88% (the difference percentage is (90% - 88%) / 90% ≈ 2.2%), and the frequency deviation is -0.3 Hz (the difference percentage is (0.5 - 0.3) / 0.5 = 40%). Then the safety margin evaluation value is 2.2%.

[0050] Screen candidate scheduling instruction combinations with safety margin evaluation values greater than or equal to the preset safety margin evaluation threshold to generate a set of safety margin evaluation results. The preset safety margin evaluation threshold is set according to the minimum margin value of safe operations in historical scheduling records. For example, the margin value distribution of fault-free scheduling instructions in the past year is statistically analyzed, and the 90th percentile (e.g., 10%) is taken as the threshold. If the safety margin evaluation value of a candidate scheduling instruction combination is less than 10%, it is marked as an out-of-limit risk and excluded; those greater than or equal to 10% are retained in the set of safety margin evaluation results. For example, if the safety margin evaluation value of a combination is 8%, it is excluded; if another combination is 12%, it is retained. Each piece of data in the set of safety margin evaluation results includes the candidate scheduling instruction combination identifier (such as combination number 001), the safety margin evaluation value (such as 12%), and the out-of-limit risk flag field (such as "safe" or "out-of-limit"), which is called when generating the final scheduling instruction in the subsequent steps.

[0051] Grid physical security constraint indicators include node voltage deviation, line load rate, and frequency deviation, which are defined as follows: Node voltage deviation is the percentage difference between the actual node voltage and the rated voltage; Line load rate is the percentage ratio of the actual line current to the rated current-carrying capacity; Frequency deviation is the absolute value of the difference between the actual grid frequency and the rated frequency. The safety thresholds are set according to the equipment tolerance limits in historical operation data. For example, the safety threshold for node voltage deviation is ±5% of the rated value (based on the insulation tolerance of the transformer), the line load rate threshold is 90% (based on the thermal stability limit of the line), and the frequency deviation threshold is ±0.5 Hz (based on the dead band of the generator set for frequency regulation). The predicted values of the indicators are obtained by simulating the changes in the grid state after the execution of the scheduling instructions through power flow calculations.

[0052] Adjust the real-time dynamic priority sequence according to the safety margin evaluation results, generate resource scheduling instructions, and distribute them to the corresponding resources for execution through a shared communication network, including: Adjust the real-time dynamic priority sequence according to the safety margin evaluation values in the set of safety margin evaluation results. If the safety margin evaluation value of a candidate scheduling instruction combination is lower than the preset safety margin evaluation threshold, the corresponding resource entry is excluded from the real-time dynamic priority sequence; Generate resource scheduling instructions based on the adjusted real-time dynamic priority sequence. The resource scheduling instructions include resource type identifiers, adjustment parameter values, and execution time windows; Distribute the resource scheduling instructions to the corresponding distributed energy resource terminal devices through a shared communication network; After the distributed energy resource terminal device receives the resource scheduling instructions, execute the adjustment parameter values according to the control logic corresponding to the resource type identifier, and feedback the execution results to the virtual power plant aggregation control platform through the shared communication network. The preset safety margin evaluation threshold is 10%, which is set according to the lowest margin quantile of safe operations in historical dispatching records. For example, after statistically analyzing the margin value distribution of fault-free dispatching instructions in the past year, the 90% quantile is taken. For example, if the safety margin evaluation value of a candidate dispatching instruction combination is 8%, all resource entries included in this combination are deleted from the real-time dynamic priority sequence; if the evaluation value is 12%, it is retained. The adjustment process of the real-time dynamic priority sequence is carried out in the memory of the virtual power plant aggregation control platform. The adjusted sequence retains the resource type identifier, adjustment parameter, and priority value field.

[0053] The execution time window is set according to the preset data acquisition frequency in step S1. For example, when the data acquisition frequency is once every 5 minutes, the execution time window is set to a 5-minute interval after the start time point of the next acquisition cycle. The adjustment parameter is extracted from the priority sequence and converted into a control instruction recognizable by the device. For example, the charge and discharge power instruction for the energy storage device is "charge_power = 100kW", and the output adjustment instruction for the photovoltaic device is "active_power = 50kW". The encapsulation format of the resource scheduling instruction is a JSON string, which includes the resource type identifier, adjustment parameter, execution time window, and instruction signature field. The instruction signature is used to verify the legality of the instruction source.

[0054] The distribution protocol of the shared communication network matches the preset communication interface and data encapsulation format according to the resource type identifier. For example, when the resource type identifier is "energy storage", the Modbus TCP protocol is adopted and distributed through a specific IP port, and the data encapsulation format is a hexadecimal code stream; when the resource type identifier is "photovoltaic", the MQTT protocol is adopted and distributed through the topic subscription mechanism, and the data encapsulation format is UTF-8 encoded JSON. The communication interface and data format are predefined during the resource registration phase and stored in the communication configuration database of the virtual power plant aggregation control platform. During the distribution process, the aggregation control platform queries the database according to the resource type identifier and sends instructions.

[0055] The control logic includes the charge and discharge power control of the energy storage device, the inverter output regulation of the photovoltaic device, and the digital input / output control of the load device. For example, after the energy storage device parses "charge_power=100kW" in the instruction, it adjusts the output power of the converter to the target value; after the photovoltaic device parses "active_power=50kW", it limits the maximum output power of the inverter to 50kW. The execution results include the resource identifier, the actual adjustment value, the execution status (success / failure), and the timestamp, which are encapsulated in JSON format and returned through the original communication protocol. For example, the energy storage device returns "{‘resource_id’: ‘ESS001’, ‘actual_power’: 98kW, ‘status’: ‘success’, ‘timestamp’: ‘2023-10-05T08:00:00Z’}". The feedback data is stored in the historical execution log database of the virtual power plant aggregation control platform for subsequent scheduling optimization and anomaly analysis.

[0056] Embodiment 2: Figure 2 A schematic structural diagram of a data sharing system based on a virtual power plant according to the present invention is given. A data sharing system based on a virtual power plant includes: Real-time acquisition module: Obtain the real-time operation data of the distributed energy resources of the virtual power plant through a shared communication network, including resource type identifiers and adjustment cost parameters; Delay marking module: Judge the transmission delay status of the real-time operation data in the shared communication network, and generate a data stream set of delay marks and non-delay marks; Dynamic compensation module: For non-delay marked data, match the preset initial priority rules based on the resource type identifier; for delay marked data, jointly determine the attenuation factor according to the resource type identifier and the grid frequency regulation demand level, and generate a dynamic priority compensation coefficient based on the adjustment cost parameter and the attenuation factor; Sequence generation module: Integrate the initial priority rules and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence; Security assessment module: Based on the real-time dynamic priority sequence, simulate the impact of different scheduling instruction combinations on the physical security constraint indicators of the power grid, and generate a security margin assessment result; Instruction distribution module: Adjust the real-time dynamic priority sequence according to the security margin assessment result, generate a resource scheduling instruction, and distribute it to the corresponding resource for execution through a shared communication network.

[0057] Embodiment 3: A data sharing device based on a virtual power plant includes: a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, a data sharing method based on a virtual power plant is implemented.

[0058] Embodiment 4: A data sharing medium based on a virtual power plant, on which programs or instructions are stored, and when the programs or instructions are executed by a processor, a data sharing method based on a virtual power plant is implemented.

[0059] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0060] It should be noted that the present invention can be deployed on the device itself to achieve an embedded application, or can also run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0062] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0063] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0064] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0066] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0067] As described above, the above are only the specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0068] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data sharing method based on a virtual power plant, characterized in that: The steps include: S1. Obtaining real-time operation data of distributed energy resources of virtual power plants through a shared communication network, including resource type identification and adjustment cost parameters; S2, determining the transmission delay state of the real-time operation data in the shared communication network, and generating a set of data streams marked with delay and those marked with non-delay; S3. For non-delayed marked data, the preset initial priority rule is matched based on the resource type identifier; for delayed marked data, the attenuation factor is determined in coordination with the resource type identifier and the grid frequency regulation demand level, and a dynamic priority compensation coefficient is generated based on the regulation cost parameter and the attenuation factor; S4, integrating the initial priority rule and the dynamic priority compensation coefficient to generate a real-time dynamic priority sequence; S5. Simulate the impact of different dispatch instruction combinations on the physical security constraint indicators of the power grid based on the real-time dynamic priority sequence to generate a safety margin assessment result; S6. Adjust the real-time dynamic priority sequence according to the safety margin assessment result, generate resource scheduling instructions and distribute them to corresponding resources for execution through the shared communication network.

2. A data sharing method based on a virtual power plant according to claim 1, characterized in that: Obtain real-time operating data of distributed energy resources of virtual power plants through a shared communication network, including resource type identification and adjustment cost parameters, including: Acquire real-time operation data of distributed energy resources of virtual power plants through a shared communication network at a preset collection frequency; Converting real-time operation data into a unified data format, wherein the unified data format includes a resource type identification field and an adjustment cost parameter field; Verify the validity of the resource type identification field and the adjustment cost parameter field, and eliminate invalid data entries.

3. A data sharing method based on a virtual power plant according to claim 2, characterized in that: Determine the transmission delay state of real-time operation data in a shared communication network, and generate a set of data streams with delay marks and non-delay marks, including: Get the timestamp of real-time running data, which includes the time when the data was generated and when it was received; The transmission time is calculated based on the data generation time and the reception time. The transmission time is equal to the reception time minus the data generation time. Compare the transmission time with a preset delay threshold, and mark the data whose transmission time exceeds the preset delay threshold as delayed marked data, otherwise mark it as non-delay marked data; The delay marked data and the non-delay marked data are classified according to the resource type identifier to generate a delay marked data flow set and a non-delay marked data flow set classified by resource type.

4. The data sharing method based on a virtual power plant according to claim 3 is characterized in that: For non-delayed tagged data, the preset initial priority rules are matched based on the resource type identifier, including: Obtain a preset initial priority rule table, the initial priority rule table including a mapping relationship between a resource type identifier and an initial priority value; Query the initial priority rule table according to the resource type identification field in the non-delayed tag data, and match the corresponding initial priority value; The matched initial priority value and the adjustment cost parameter in the non-delayed marked data are weightedly summed to generate an initial priority score; Writing the initial priority score into the priority field of the non-delayed marked data to generate a set of non-delayed data flows with the initial priority score; Generate dynamic priority compensation coefficients for delayed marking data, including: Determine a reference attenuation factor according to the power grid frequency regulation demand level, the reference attenuation factor being a first preset value when the power grid frequency regulation demand level is emergency, a second preset value when the power grid frequency regulation demand level is normal, and a third preset value when the power grid frequency regulation demand level is loose; The benchmark attenuation factor is dynamically adjusted based on the resource type identification and real-time dispatchable capacity corresponding to the delay mark data. If the resource type identification is energy storage and the real-time dispatchable capacity is greater than the capacity threshold, the benchmark attenuation factor increases the compensation amount, otherwise it decreases the compensation amount. The ratio of the dynamically adjusted baseline attenuation factor to the adjustment cost parameter is used as a dynamic priority compensation coefficient; The dynamic priority compensation coefficient is corrected twice according to the grid node voltage over-limit risk level. If the grid node voltage over-limit risk level is high risk, an inhibition factor is applied to the dynamic priority compensation coefficient.

5. A data sharing method based on a virtual power plant according to claim 4, characterized in that: The initial priority rules and dynamic priority compensation coefficients are integrated to generate a real-time dynamic priority sequence, including: According to the coupling relationship between the power grid frequency regulation demand level and the resource type identification, the initial priority score of the non-delayed data flow set and the dynamic priority compensation coefficient of the delayed data flow set are classified and layered; Dynamically adjust the hierarchical priority order based on the direction of real-time grid frequency deviation changes: prioritize energy storage resources when the frequency deviation is positive, and prioritize load resources when the frequency deviation is negative; The non-delayed data and the delayed data are cross-sorted in multiple dimensions according to the resource type identifier and the power grid frequency regulation demand level to generate a priority subsequence in the resource type-frequency regulation level dimension; The priority subsequences are screened for safety based on the voltage over-limit risk level of the power grid nodes: resource entries that may aggravate voltage problems are eliminated at high risk levels, and the entire priority subsequence is retained at medium and low risk levels; All priority subsequences after security screening are merged, and a real-time dynamic priority sequence is generated according to the global priority rules identified by the power grid frequency regulation demand level and resource type.

6. A data sharing method based on a virtual power plant according to claim 5, characterized in that: Based on the real-time dynamic priority sequence, the impact of different dispatch instruction combinations on the physical security constraint indicators of the power grid is simulated to generate safety margin assessment results, including: Generate multiple candidate scheduling instruction combinations according to the real-time dynamic priority sequence, each candidate scheduling instruction combination includes scheduling instructions with different resource type identifiers and corresponding adjustment amount parameters; Based on the current operating parameters of the power grid, a power grid physical safety constraint simulation is performed on each candidate dispatch instruction combination to predict the change value of the power grid physical safety constraint indicator after the candidate dispatch instruction combination is executed; Compare the predicted change value of the power grid physical security constraint indicator with the preset power grid physical security constraint indicator safety threshold to generate a safety margin evaluation value for each candidate dispatch instruction combination; The candidate scheduling instruction combinations whose safety margin evaluation values ​​are greater than or equal to a preset safety margin evaluation threshold are screened to generate a safety margin evaluation result set.

7. A data sharing method based on a virtual power plant according to claim 6, characterized in that: Adjust the real-time dynamic priority sequence according to the safety margin assessment results, generate resource scheduling instructions and distribute them to the corresponding resources for execution through the shared communication network, including: The real-time dynamic priority sequence is adjusted according to the safety margin evaluation value in the safety margin evaluation result set. If the safety margin evaluation value of the candidate scheduling instruction combination is lower than the preset safety margin evaluation threshold, the corresponding resource entry is removed from the real-time dynamic priority sequence; Generate a resource scheduling instruction based on the adjusted real-time dynamic priority sequence, the resource scheduling instruction including a resource type identifier, an adjustment amount parameter, and an execution time window; Distribute resource scheduling instructions to corresponding distributed energy resource terminal devices through a shared communication network; After the distributed energy resource terminal device receives the resource scheduling instruction, it executes the adjustment parameters according to the control logic corresponding to the resource type identifier, and feeds back the execution results to the virtual power plant aggregation control platform through the shared communication network.

8. A data sharing system based on a virtual power plant, used to implement a data sharing method based on a virtual power plant according to any one of claims 1 to 7, characterized in that: include: Real-time acquisition module: obtains real-time operation data of distributed energy resources of virtual power plants through a shared communication network, including resource type identification and adjustment cost parameters; Delay marking module: determines the transmission delay status of real-time operation data in the shared communication network and generates a set of delay marked and non-delay marked data streams; Dynamic compensation module: For non-delayed marked data, the preset initial priority rules are matched based on the resource type identifier; for delayed marked data, the attenuation factor is determined in coordination with the resource type identifier and the grid frequency regulation demand level, and a dynamic priority compensation coefficient is generated based on the adjustment cost parameter and the attenuation factor; Sequence generation module: integrates the initial priority rules and dynamic priority compensation coefficients to generate real-time dynamic priority sequences; Safety assessment module: Based on the real-time dynamic priority sequence, it simulates the impact of different dispatch instruction combinations on the physical safety constraint indicators of the power grid and generates safety margin assessment results; Instruction distribution module: adjusts the real-time dynamic priority sequence according to the safety margin assessment results, generates resource scheduling instructions and distributes them to the corresponding resources for execution through a shared communication network.

9. A data sharing device based on a virtual power plant, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, a data sharing method based on a virtual power plant as described in any one of claims 1 to 7 is implemented.

10. A data sharing medium based on a virtual power plant, characterized in that: The medium stores programs or instructions, and when the programs or instructions are executed by the processor, a data sharing method based on a virtual power plant as described in any one of claims 1-7 is implemented.

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