Virtual power plant multi-scene cooperative regulation and control method and system for power insurance supply

Abstract

The invention relates to the technical field of virtual power plant regulation and control, and provides a virtual power plant multi-scene cooperative regulation and control method and system for power supply insurance, and the method comprises the steps: periodically obtaining the supply and demand time series data of a virtual power plant, and carrying out the supply and demand fluctuation characteristic analysis of the supply and demand time series data of the virtual power plant, and generating supply and demand fluctuation time series data; according to the supply and demand fluctuation time sequence data, scene matching analysis is carried out based on a pre-constructed multi-scene regulation and control strategy library to obtain an initial scene regulation and control strategy; performing scene adaptability simulation analysis on the initial scene regulation and control strategy, and optimizing the initial scene regulation and control strategy according to a simulation analysis result to obtain a target scene regulation and control strategy; and performing layered and partitioned disassembly on a regulation target in the target scene regulation strategy to obtain a layered and partitioned regulation target, and executing virtual power plant regulation according to the layered and partitioned regulation target. According to the method, the collaborative optimization capability and the operation toughness of the virtual power plant in a complex and uncertain environment can be remarkably improved, and reliable technical support is provided for power supply insurance.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant control technology, and in particular to a multi-scenario collaborative control method and system for a virtual power plant for ensuring power supply. Background Art

[0002] As an important means for power systems to address energy transition and ensure power supply, virtual power plants (VPPs) play a key role in achieving efficient utilization of renewable energy and ensuring stable grid operation. By aggregating distributed energy resources, energy storage devices, and flexible loads to form a coordinated and controlled energy network, they provide flexibility and stability to the power system.

[0003] However, most of the existing virtual power plant control methods rely on the centralized scheduling of a single flexible resource, which is difficult to adapt to the random fluctuations in new energy supply and the diverse changes in load demand. The control efficiency is low and the support for the dynamic response of the power grid is insufficient. Some multi-flexible resource collaborative scheduling designs lack efficient interaction of multiple flexible resources in a dynamic environment and quantitative evaluation of scenario adaptation, resulting in insufficient resource utilization and difficulty in balancing power grid stability and resource collaborative optimization. This in turn limits the control effect of virtual power plants in complex scenarios, making it difficult to fully tap their potential to effectively meet the needs of power supply security. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-scenario collaborative control method for virtual power plants for power supply security. Through the virtual power plant control design based on the intelligent scenario control strategy acquisition mechanism based on supply and demand fluctuations combined with the scenario suitability analysis mechanism and the hierarchical and partitioned decomposition mechanism of the control target, it can not only realize the efficient interaction of source, load and storage of the virtual power plant in a complex and uncertain environment, and improve the peak-shaving and valley-filling capacity of the power grid, but also effectively smooth out the supply and demand fluctuations, ensure the stable operation of the power grid, and thus significantly improve the collaborative optimization capability and operation resilience of the virtual power plant, providing reliable technical support for power supply security.

[0005] In order to achieve the above objectives, it is necessary to provide a multi-scenario collaborative control method and system for virtual power plants for power supply security in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a multi-scenario coordinated control method for a virtual power plant for power supply security. The virtual power plant includes multiple resource aggregation areas, each of which includes multiple flexible resources. The method includes: Periodically acquiring virtual power plant supply and demand time series data, and performing supply and demand fluctuation characteristic analysis on the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data; the virtual power plant supply and demand time series data includes new energy supply time series data and load demand time series data; Based on the supply and demand fluctuation time series data, a scenario matching analysis is performed based on a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy; the multi-scenario control strategy library includes supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under various fluctuation scenarios; the scenario benchmark control strategy includes control parameters, control constraints, and control objectives; Performing a scene adaptability simulation analysis on the initial scene control strategy, and optimizing the initial scene control strategy according to the corresponding simulation analysis results to obtain a target scene control strategy; The control targets in the target scenario control strategy are decomposed into layers and partitions to obtain layered and partitioned control targets, and virtual power plant control of the corresponding period is performed according to the layered and partitioned control targets.

[0007] Furthermore, the steps of constructing the multi-scenario control strategy library include: Extract source, load and storage information from the historical operating data of the virtual power plant to obtain the historical energy production sequence of the power plant, the historical load demand sequence of the power plant, and the charging and discharging power sequence of the power plant energy storage; According to the preset time period splitting duration, the power plant's historical energy production sequence, the power plant's historical load demand sequence, and the power plant's energy storage charging and discharging power sequence are respectively split into time periods to obtain corresponding energy production subsequence sets, load demand subsequence sets, and energy storage charging and discharging power subsequence sets; Extracting fluctuation characteristics of each subsequence in the energy production subsequence set, the load demand subsequence set, and the energy storage charge and discharge power subsequence set, respectively, to obtain corresponding energy fluctuation characteristic data sets, load fluctuation characteristic data sets, and energy storage charge and discharge power fluctuation characteristic data sets; Performing cluster analysis on the energy fluctuation characteristic dataset, the load fluctuation characteristic dataset, and the energy storage charging and discharging power fluctuation characteristic dataset to obtain multiple source-load-storage interaction scenarios; The energy fluctuation characteristic data and load fluctuation characteristic data of the cluster center corresponding to each source-load-storage interaction scenario are spliced ​​respectively to obtain the corresponding supply and demand fluctuation benchmark characteristic vector, and the corresponding scenario benchmark control strategy is obtained according to the historical control strategy of the cluster center corresponding to each source-load-storage interaction scenario; The supply and demand fluctuation benchmark feature vectors and the scenario benchmark control strategies of each source-load-storage interaction scenario are aggregated to obtain the multi-scenario control strategy library.

[0008] Furthermore, the virtual power plant historical operation data includes the regional energy production history sequence, the regional load demand history sequence and the regional energy storage charging and discharging power history sequence of each resource aggregation area; The step of extracting source-load-storage information from the historical operation data of the virtual power plant to obtain the historical energy production sequence of the power plant, the historical load demand sequence of the power plant, and the charging and discharging power sequence of the power plant energy storage includes: The regional energy production history sequence, the regional load demand history sequence, and the regional energy storage charge and discharge power history sequence of each resource aggregation area are respectively cleaned and time-aligned to obtain the corresponding regional energy production sequence to be analyzed, the regional load demand sequence to be analyzed, and the regional energy storage charge and discharge power sequence to be analyzed; Aggregating the energy production sequences of the regions to be analyzed in all resource aggregation regions at the sampling time to obtain the corresponding historical energy production sequences of the power plants, and aggregating the load demand sequences of the regions to be analyzed in all resource aggregation regions at the sampling time to obtain the corresponding historical load demand sequences of the power plants; The energy storage charge and discharge power sequences of the to-be-analyzed regions in each resource aggregation region are weighted and summarized using the corresponding regional energy storage capacity ratio as a weight to obtain the corresponding power plant energy storage charge and discharge power sequence.

[0009] Furthermore, the supply and demand fluctuation time series data includes energy supply fluctuation time series data and load demand fluctuation time series data; The step of performing scenario matching analysis based on the supply and demand fluctuation time series data and obtaining the corresponding initial scenario control strategy based on a pre-built multi-scenario control strategy library includes: Extracting corresponding fluctuation characteristics from the energy supply fluctuation time series data and the load demand fluctuation time series data respectively to generate corresponding energy supply fluctuation characteristics and load demand fluctuation characteristics; generating a corresponding energy supply fluctuation characteristic vector and a load demand fluctuation characteristic vector according to the energy supply fluctuation characteristic and the load demand fluctuation characteristic respectively; Concatenating the energy supply fluctuation characteristic vector and the load demand fluctuation characteristic vector to obtain a supply and demand fluctuation characteristic vector; Performing a similarity analysis on the supply and demand fluctuation feature vector and the supply and demand fluctuation benchmark feature vector corresponding to each scenario control strategy in the multi-scenario regulation strategy library to obtain a corresponding scenario similarity value; According to all the scene similarity values, obtaining a number of relevant scene control strategies based on a preset similarity threshold; Normalizing the scene similarity values ​​corresponding to the relevant scene control strategies to obtain corresponding similarity weights; Based on the similarity weights of the respective related scene control strategies, all the related scene control strategies are weightedly fused to obtain the initial scene control strategy.

[0010] Furthermore, the control parameters include new energy control parameters, energy storage control parameters and load control parameters; The step of performing scene adaptability simulation analysis on the initial scene control strategy and optimizing the initial scene control strategy according to the corresponding simulation analysis results to obtain the target scene control strategy includes: Performing a virtual power plant strategy scheduling simulation based on the initial scenario control strategy and the virtual power plant supply and demand time series data to obtain corresponding simulation operation data; According to the simulation operation data, corresponding scenario adaptability evaluation indicators are obtained; the scenario adaptability evaluation indicators include supply and demand balance error rate, response delay time, regulation resource utilization rate and over-limit frequency; the regulation resource utilization rate includes new energy utilization rate, load utilization rate and energy storage utilization rate; Comparing and analyzing each of the scene adaptability evaluation indicators with the corresponding scene adaptability evaluation indicator thresholds to obtain corresponding scene adaptability evaluation results; Obtaining the scene adaptability evaluation index items that do not meet the standards in the scene adaptability evaluation results, and obtaining the control parameters to be optimized from the control parameters based on a preset association analysis algorithm; Based on preset adjustment rules, each of the control parameters to be optimized in the control parameters is adjusted to obtain the target scene control strategy.

[0011] Furthermore, the control targets include new energy output control value, load demand control value and energy storage charging and discharging power control value; The hierarchical and regional control targets include the regional control targets of each resource aggregation area and the resource control targets of each flexible resource in each resource aggregation area; The step of performing hierarchical and partitioned decomposition of the control target in the target scene control strategy to obtain the hierarchical and partitioned control target comprises: The control targets in the target scenario control strategy are split into hierarchical targets based on a preset regional target allocation principle to obtain regional control targets for each resource aggregation area; According to the regional control targets of each of the resource aggregation areas, the regional targets are decomposed based on the resource scheduling optimization model in the preset area to obtain the resource control targets of each flexible resource in the corresponding resource aggregation area; the resource scheduling optimization model in the preset area is constructed with the optimization target of balancing new energy consumption, control costs and power grid operation risks.

[0012] Furthermore, the regional control targets include regional new energy output control targets, regional load demand control targets, and regional energy storage charging and discharging power control targets; The step of performing hierarchical target splitting on the control target in the target scenario control strategy based on a preset regional target allocation principle to obtain the regional control target of each resource aggregation area includes: According to the proportion of new energy installed capacity and the proportion of current available output in each resource aggregation area, the new energy output control allocation ratio of the corresponding resource aggregation area is obtained, and according to the new energy output control allocation ratio and the new energy output control value, the regional new energy output control target of the corresponding resource aggregation area is obtained; performing a summary analysis of regional load regulation potential based on the load capacity of different regulation levels and the preset regulation potential coefficient within each of the resource aggregation areas to obtain a corresponding regional load regulation potential ratio, and obtaining a corresponding regional load demand regulation target based on the regional load regulation potential ratio and the load demand regulation value; According to the energy storage power control type of the energy storage charge and discharge power control value, the energy storage charge and discharge power allocation ratio of each resource aggregation area is obtained, and according to the energy storage charge and discharge power allocation ratio and the energy storage charge and discharge power control value, the corresponding regional energy storage charge and discharge power control target is obtained.

[0013] Furthermore, the step of obtaining the energy storage charging and discharging power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charging and discharging power control value includes: When the energy storage power control type is energy storage charging power control, the energy storage charging efficiency, unit charging cost and allowed charging power ratio of each resource aggregation area are obtained respectively; Based on the charging allocation ratio setting principle, the energy storage charging efficiency, unit charging cost and allowable charging power ratio of each resource aggregation area are weighted and integrated for analysis to obtain the corresponding regional energy storage charging power allocation ratio; the charging allocation ratio setting principle includes that the regional energy storage charging power allocation ratio is directly proportional to the energy storage charging efficiency and the allowable charging power ratio, and the regional energy storage charging power allocation ratio is inversely proportional to the unit charging cost.

[0014] Furthermore, the step of obtaining the energy storage charge and discharge power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charge and discharge power control value further includes: When the energy storage charging and discharging power control value is energy storage discharging power control, the energy storage discharging efficiency, unit discharge cost and allowable discharge power ratio of each resource aggregation area are obtained respectively; Based on the discharge allocation ratio setting principle, a weighted fusion analysis is performed on the energy storage discharge efficiency, unit discharge cost, and allowable discharge power ratio of each resource aggregation area to obtain the corresponding regional energy storage discharge power allocation ratio; the discharge allocation ratio setting principle includes that the regional energy storage discharge power allocation ratio is directly proportional to the energy storage discharge efficiency and the allowable discharge power ratio, and the regional energy storage discharge power allocation ratio is inversely proportional to the unit discharge cost.

[0015] In a second aspect, an embodiment of the present invention provides a multi-scenario coordinated control system for a virtual power plant for ensuring power supply. The virtual power plant includes multiple resource aggregation areas, each of which includes multiple flexible resources. The system includes: a fluctuation analysis module for periodically acquiring supply and demand time series data of a virtual power plant, and performing supply and demand fluctuation characteristic analysis on the supply and demand time series data of the virtual power plant, to generate corresponding supply and demand fluctuation time series data; the supply and demand time series data of the virtual power plant includes new energy supply time series data and load demand time series data; A scenario matching module is configured to perform scenario matching analysis based on the supply and demand fluctuation time series data and a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy; the multi-scenario control strategy library includes supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under various fluctuation scenarios; the scenario benchmark control strategy includes control parameters, control constraints, and control objectives; A strategy acquisition module is used to perform a scenario adaptability simulation analysis on the initial scenario control strategy, and optimize the initial scenario control strategy according to the corresponding simulation analysis results to obtain a target scenario control strategy; The target decomposition module is used to decompose the control targets in the target scenario control strategy into layers and partitions to obtain layered and partitioned control targets, and execute virtual power plant control of the corresponding period according to the layered and partitioned control targets.

[0016] The present invention provides a multi-scenario collaborative control method and system for a virtual power plant for ensuring power supply. The method realizes periodic acquisition of virtual power plant supply and demand time series data including new energy supply time series data and load demand time series data, performs supply and demand fluctuation characteristic analysis on the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data, performs scenario matching analysis on the supply and demand fluctuation time series data based on a pre-constructed multi-scenario control strategy library including supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under multiple fluctuation scenarios to obtain a corresponding initial scenario control strategy, performs scenario adaptability simulation analysis on the initial scenario control strategy, optimizes the initial scenario control strategy according to the corresponding simulation analysis results to obtain a target scenario control strategy, then decomposes the control targets in the target scenario control strategy into layers and partitions to obtain layered and partitioned control targets, and executes a technical solution for executing virtual power plant control of the corresponding period according to the layered and partitioned control targets. Compared with existing technologies, this multi-scenario collaborative control method for virtual power plants for power supply security is a virtual power plant control design that combines an intelligent scenario control strategy acquisition mechanism based on supply and demand fluctuations with a scenario suitability analysis mechanism and a hierarchical and partitioned decomposition mechanism for control targets. It can not only achieve efficient interaction between sources, loads, and storage in virtual power plants under complex and uncertain environments, and improve the peak-shaving and valley-filling capabilities of power grids, but also effectively smooth out supply and demand fluctuations, ensure stable power grid operation, and significantly improve the collaborative optimization capabilities and operational resilience of virtual power plants, providing reliable technical support for power supply security, and has good engineering application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a multi-scenario collaborative control method for a virtual power plant for power supply assurance according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of a multi-scenario collaborative control system for a virtual power plant for power supply assurance according to an embodiment of the present invention; Wherein, the accompanying drawings are marked as follows: 1. Fluctuation analysis module; 2. Scenario matching module; 3. Strategy acquisition module; 4. Target decomposition module. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In one embodiment, Figure 1As shown, a multi-scenario collaborative control method for a virtual power plant for power supply security is provided. The virtual power plant includes multiple resource aggregation areas, and the resource aggregation areas include multiple flexible resources, and the flexible resources include distributed new energy (photovoltaic, hydropower and wind power, etc.), and adjustable loads (industrial loads, building loads and traffic loads, etc.). It can be understood that the architecture of the virtual power plant is divided from top to bottom into a virtual power plant management layer, a resource aggregation layer (including multiple resource aggregation areas) and a resource layer (including multiple flexible resources). The corresponding virtual power plant architecture description can refer to the existing hierarchical partitioning architecture, which will not be described in detail here. The multi-scenario collaborative control method for a virtual power plant provided by the present invention is suitable for the control of a virtual power plant with a hierarchical partitioning architecture. The specific method includes: S11. Periodically obtain the supply and demand time series data of the virtual power plant, and analyze the supply and demand fluctuation characteristics of the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data; wherein, the supply and demand time series data of the virtual power plant can be understood as multi-dimensional time series data obtained based on the relevant virtual power plant operation information monitoring system, such as the SCADA (Supervisory Control And Data Acquisition) system, according to the preset virtual power plant control cycle, for analyzing the new energy output supply fluctuation and load demand fluctuation of the virtual power plant, including new energy supply time series data and load demand time series data. It should be noted that the preset virtual power plant control cycle can be set based on the actual application control demand to determine the sequence length of the virtual power plant supply and demand time series data, and the actual collection time interval of the virtual power plant supply and demand time series data will be less than the preset virtual power plant control cycle. For example, the preset virtual power plant control cycle is one day or one hour, while the actual collection time interval may be minutes or seconds, which is not specifically limited here.

[0020] The new energy supply time series data in this embodiment can be understood as the time series data of the total new energy production supply of all resource aggregation areas in the entire virtual power plant, which can be obtained by adding up the new energy production values ​​at the same sampling time in the new energy production sequences of all resource aggregation areas, and the new energy production value at each sampling time in the new energy production sequence of each resource aggregation area is the cumulative value of all new energy production in the resource aggregation area at the corresponding time; correspondingly, the load demand time series data can be understood as the time series data of the total load demand of all resource aggregation areas in the entire virtual power plant, which can be obtained by adding up the load demand values ​​at the same sampling time in the load demand sequence of all resource aggregation areas, and the load demand value at each sampling time in the load demand sequence of each resource aggregation area is the cumulative value of all load demands in the resource aggregation area at the corresponding time.

[0021] In actual application, after data cleaning (abnormal value removal, missing value filling, etc.) is performed on the new energy supply time series data and the load demand time series data, time alignment processing is performed on both according to a preset time window (set according to demand) to obtain time-aligned new energy supply time series data and time-aligned load demand time series data. Then, first-order difference processing is performed on the time-aligned new energy supply time series data and the time-aligned load demand time series data respectively, and corresponding energy supply fluctuation time series data and load demand fluctuation time series data can be obtained, that is, supply and demand fluctuation time series data for subsequent intelligent scene matching is obtained. It should be noted that the data cleaning, time alignment and first-order difference processing in this embodiment can be implemented by referring to related prior art, which will not be described in detail here.

[0022] S12, scene matching analysis is performed based on the supply and demand fluctuation time series data and a pre-constructed multi-scene regulation strategy library to obtain a corresponding initial scene regulation strategy; the multi-scene regulation strategy library includes supply and demand fluctuation reference feature vectors and scene reference regulation strategies in multiple fluctuation scenes; wherein the supply and demand fluctuation reference feature vector can be understood as a feature vector obtained by splicing new energy output supply fluctuation features and load demand fluctuation features; the scene reference regulation strategy includes regulation parameters, regulation constraints and regulation targets.

[0023] The regulation parameters in this embodiment include new energy regulation parameters, energy storage regulation parameters and load regulation parameters, and the new energy regulation parameters can include new energy unit output range, ramp rate limit, economic cost and physical time consumption of unit start-stop, etc., the energy storage regulation parameters can include time-division energy storage charge / discharge power threshold, energy storage state of charge safety range and energy storage charge / discharge response priority (such as priority response power shortage), etc., and the load regulation parameters can include interruptible load reduction amount and time period, shiftable load transfer amount and time shift window, and temperature control load regulation range, etc.; the regulation constraint can be understood as a strategy operation boundary, which can include allowed power deviation threshold, reserve capacity requirement, power grid safety constraint (voltage allowed fluctuation range) and upper limit of unit regulation cost, etc.; the corresponding regulation target can be understood as a comprehensive optimization target (such as minimum comprehensive regulation cost) of a virtual power plant under the regulation parameters and regulation constraints, which is used to determine the comprehensive resource regulation demand, including new energy output regulation value, load demand regulation value and energy storage charge / discharge power regulation value, and the new energy output regulation value can be positive or negative (positive value for increasing new energy output, negative value for decreasing new energy output), the load demand regulation value can be positive or negative (positive value for decreasing load demand, negative value for stimulating load demand), and the energy storage charge / discharge power regulation value can be positive, negative or zero (positive value for energy storage to release energy to the virtual power plant, negative value for energy storage to absorb energy from the virtual power plant, and zero value for energy storage in idle state).

[0024] In order to ensure the efficiency and reliability of the control strategy acquisition for the supply and demand fluctuation scenarios of the virtual power plant in actual applications, this embodiment preferably analyzes and organizes the control strategy of typical supply and demand fluctuation scenarios based on the historical operation data of the virtual power plant to build a multi-scenario control strategy library. The number and type of fluctuation scenarios in the strategy library vary depending on the actual historical operation data of the virtual power plant, and are not specifically limited here. Specifically, the steps of building the multi-scenario control strategy library include: Source, load and storage information is extracted from the historical operating data of the virtual power plant to obtain the historical energy production sequence of the power plant, the historical load demand sequence of the power plant and the energy storage charging and discharging power sequence of the power plant; among them, the acquisition time of the historical operating data of the virtual power plant can be as long as possible while ensuring the efficiency of calculation and analysis, so as to ensure the accuracy of the subsequent extraction of source, load and storage interaction scenarios. Taking into account the fact that fluctuations in new energy production and load demand will have certain seasonal effects, in principle, it is necessary to ensure that the total data time of the historical operating data of the virtual power plant is greater than one year.

[0025] Specifically, the historical operation data of the virtual power plant includes a historical sequence of regional energy production, a historical sequence of regional load demand, and a historical sequence of regional energy storage charge and discharge power of each resource aggregation area; the step of extracting source, load, and storage information from the historical operation data of the virtual power plant to obtain a historical sequence of power plant energy production, a historical sequence of power plant load demand, and a historical sequence of power plant energy storage charge and discharge power includes: The regional energy production history sequence, the regional load demand history sequence, and the regional energy storage charge and discharge power history sequence of each resource aggregation area are respectively subjected to data cleaning and time alignment processing to obtain the corresponding regional energy production sequence to be analyzed, the regional load demand sequence to be analyzed, and the regional energy storage charge and discharge power sequence to be analyzed; wherein, the data cleaning and time alignment processing are as described above and will not be repeated here.

[0026] The energy production sequences of the to-be-analyzed areas of all the resource aggregation areas are aggregated at sampling times to obtain the corresponding historical energy production sequences of the power plants, and the load demand sequences of the to-be-analyzed areas of all the resource aggregation areas are aggregated at sampling times to obtain the corresponding historical load demand sequences of the power plants; that is, the historical energy production of the power plants at each sampling moment in the historical energy production sequences of the power plants is the sum of the energy productions at the corresponding sampling moments in the energy production sequences of the to-be-analyzed areas of all the resource aggregation areas; the historical load demand of the power plants at each sampling moment in the historical load demand sequences of the power plants is the sum of the load demands at the corresponding sampling moments in the load demand sequences of the to-be-analyzed areas of all the resource aggregation areas.

[0027] The energy storage charge and discharge power sequences of the to-be-analyzed regions of each resource aggregation region are weighted and summarized using the corresponding regional energy storage capacity ratio as a weight to obtain the corresponding power plant energy storage charge and discharge power sequence; wherein, the regional energy storage capacity ratio can be understood as the ratio of the regional energy storage capacity of the resource aggregation region to the total energy storage capacity of the entire virtual power plant; that is, the power plant energy storage charge and discharge power at each sampling moment in the power plant energy storage charge and discharge power sequence is the cumulative value of the product of the energy storage charge and discharge power at the corresponding sampling moment in the energy storage charge and discharge power sequence of the to-be-analyzed regions of all resource aggregation regions and the regional energy storage capacity ratio.

[0028] This embodiment summarizes the energy production, load demand and regional energy storage charging and discharging power of each resource aggregation area to obtain the comprehensive energy production, comprehensive load demand and comprehensive energy storage charging and discharging power of the entire virtual power plant, which can effectively ensure the reliability of subsequent supply and demand fluctuation scenario analysis based on the entire virtual power plant as the research object.

[0029] According to the preset time period splitting duration, the power plant energy historical production sequence, the power plant load historical demand sequence and the power plant energy storage charging and discharging power sequence are respectively split into time periods to obtain corresponding energy production subsequence sets, load demand subsequence sets and energy storage charging and discharging power subsequence sets; wherein, the preset time period splitting duration can be consistent with the virtual power plant control cycle to ensure the rationality of intelligent scenario matching based on the multi-scenario control strategy library. For example, when the virtual power plant control cycle is one day, the preset time period splitting duration can also be set to one day, and the power plant energy historical production sequence, the power plant load historical demand sequence and the power plant energy storage charging and discharging power sequence are respectively split into multiple subsequences with corresponding sequence durations of one day to obtain corresponding subsequence sets.

[0030] Fluctuation characteristics are extracted from each subsequence in the energy production subsequence set, the load demand subsequence set, and the energy storage charge and discharge power subsequence set, respectively, to obtain corresponding energy fluctuation characteristic data sets, load fluctuation characteristic data sets, and energy storage charge and discharge power fluctuation characteristic data sets; wherein, the energy fluctuation characteristic data sets include the production mean, production fluctuation variance, and production peak-to-valley difference (the difference between the maximum energy production and the minimum energy production) of each time period; the load fluctuation characteristic data sets include the load mean, load fluctuation variance, and load peak-to-valley difference (the difference between the maximum load demand and the minimum load demand) of each time period; the energy storage charge and discharge power fluctuation characteristic data sets include the energy storage charge and discharge power mean and the energy storage charge and discharge power change rate mean; it should be noted that the acquisition process of the energy fluctuation characteristic data sets, the load fluctuation characteristic data sets, and the energy storage charge and discharge power fluctuation characteristic data sets can be obtained by referring to the existing calculation method of relevant fluctuation characteristics, and will not be described in detail here.

[0031] Cluster analysis is performed on the energy fluctuation characteristic dataset, the load fluctuation characteristic dataset, and the energy storage charging and discharging power fluctuation characteristic dataset to obtain multiple source-load-storage interaction scenarios; wherein the cluster analysis process may include concatenating the characteristic data of the same time period in the energy fluctuation characteristic dataset, the load fluctuation characteristic dataset, and the energy storage charging and discharging power fluctuation characteristic dataset to obtain corresponding feature vectors, and then performing cluster analysis based on the cosine similarity of the feature vectors as a distance metric to obtain multiple source-load-storage interaction scenarios. This embodiment uses energy fluctuation characteristics, load fluctuation characteristics, and energy storage charging and discharging power fluctuation characteristics to construct source-load-storage interaction scenarios, which can effectively capture the interactive relationship between source, load, and storage under supply and demand fluctuations in virtual power plants, and provide reliable guarantees for the effectiveness of extracting typical operating scenarios of virtual power plants.

[0032] The energy fluctuation characteristic data and load fluctuation characteristic data of the cluster center corresponding to each source-load-storage interaction scenario are spliced ​​together respectively to obtain the corresponding supply and demand fluctuation benchmark characteristic vector, and the corresponding scenario benchmark control strategy is obtained according to the historical control strategy of the cluster center corresponding to each source-load-storage interaction scenario; it should be noted that the supply and demand fluctuation benchmark characteristic vector is only obtained by splicing the energy fluctuation characteristic data and the load fluctuation characteristic data, so as to facilitate subsequent efficient matching with the supply and demand fluctuation data of the virtual power plant.

[0033] The supply and demand fluctuation benchmark feature vectors and the scenario benchmark control strategies of each source-load-storage interaction scenario are aggregated to obtain the multi-scenario control strategy library.

[0034] This embodiment constructs a multi-scenario control strategy library based on the source-load-storage interaction relationship and supply and demand fluctuation analysis during the operation of the virtual power plant. It can not only ensure the scientific nature and practicality of the extraction of each scenario in the multi-scenario control strategy library, but also ensure the subsequent efficient and reliable intelligent scenario matching based on the supply and demand fluctuation data during the actual operation of the virtual power plant, so as to accurately obtain the required scenario control strategy to respond to scheduling needs in a timely manner. It should be noted that in order to ensure the continued reliability of the multi-scenario control strategy library in actual applications, the multi-scenario control strategy library can also be updated and optimized based on the online operation data of the virtual power plant according to the aforementioned control strategy library construction steps to ensure the adaptability of the scenarios in the multi-scenario control strategy library to the actual virtual power plant supply and demand fluctuation scenarios.

[0035] Specifically, the step of performing scenario matching analysis based on the supply and demand fluctuation time series data and a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy includes: The corresponding fluctuation characteristics of the energy supply fluctuation time series data and the load demand fluctuation time series data are extracted respectively to generate corresponding energy supply fluctuation characteristics and load demand fluctuation characteristics; wherein, the energy supply fluctuation characteristics include the output mean, the output fluctuation variance and the output peak-to-valley difference, and the load demand fluctuation characteristics include the load mean, the load fluctuation variance and the load peak-to-valley difference. The specific extraction process will not be described in detail here.

[0036] According to the energy supply fluctuation characteristics and the load demand fluctuation characteristics, the corresponding energy supply fluctuation characteristic vector and load demand fluctuation characteristic vector are generated respectively; wherein, the energy supply fluctuation characteristic vector can be understood as a characteristic vector obtained by sequentially splicing data such as the output mean, output fluctuation variance and output peak-to-valley difference in the energy supply fluctuation characteristics; the corresponding load demand fluctuation characteristic vector can be understood as a characteristic vector obtained by sequentially splicing data such as the load mean, load fluctuation variance and load peak-to-valley difference in the load demand fluctuation characteristics.

[0037] The energy supply fluctuation characteristic vector and the load demand fluctuation characteristic vector are spliced ​​together to obtain a supply and demand fluctuation characteristic vector; that is, the supply and demand fluctuation characteristic vector is a higher-dimensional characteristic vector obtained by splicing the energy supply fluctuation characteristic vector and the load demand fluctuation characteristic vector.

[0038] A similarity analysis is performed on the supply and demand fluctuation feature vector and the supply and demand fluctuation benchmark feature vector corresponding to each scenario control strategy in the multi-scenario regulation strategy library to obtain a corresponding scenario similarity value; wherein the similarity analysis can be implemented using existing related similarity analysis technology.

[0039] According to all the scene similarity values, several related scene control strategies are obtained based on a preset similarity threshold; wherein, the preset similarity threshold can be set according to actual application requirements and is not specifically limited here. The corresponding related scene control strategies can be understood as one or more scene control strategies in the multi-scene control strategy library whose scene similarity values ​​with the current supply and demand fluctuation feature vector are higher than the preset similarity threshold.

[0040] The scene similarity values ​​corresponding to the respective relevant scene control strategies are normalized to obtain corresponding similarity weights; wherein, the normalization process can be implemented by using any existing normalization technology, such as maximum-minimum normalization.

[0041] Based on the similarity weights of each of the related scene control strategies, all the related scene control strategies are weightedly integrated to obtain the initial scene control strategy; wherein, the initial scene control strategy can be understood as a control strategy obtained by weighted averaging all parameters in the related scene control strategies according to the corresponding similarity weights.

[0042] This embodiment can effectively ensure the reliability and real-time performance of the actual scenario control strategy acquisition by matching the supply and demand fluctuation data of the actual virtual power plant with the known classic fluctuation scenarios in the multi-scenario control strategy library to obtain the required initial scenario control strategy, thereby ensuring the efficiency of the virtual power plant scheduling optimization response.

[0043] S13. Performing a scene adaptability simulation analysis on the initial scene control strategy, and optimizing the initial scene control strategy according to the corresponding simulation analysis results to obtain a target scene control strategy.

[0044] Scenario adaptability simulation analysis can be understood as building a virtual simulation environment based on existing virtual power plant-specific simulation tools (such as GridLAB-D) that includes new energy, loads, energy storage, and grid operation constraints. The simulation verifies the fluctuation scenario control effect of the initial scenario control strategy to ensure that a target scenario control strategy is obtained that is more in line with the actual supply and demand fluctuation scenario. Specifically, the steps of performing scenario adaptability simulation analysis on the initial scenario control strategy and optimizing the initial scenario control strategy based on the corresponding simulation analysis results to obtain the target scenario control strategy include: A virtual power plant strategy scheduling simulation is performed based on the initial scenario control strategy and the virtual power plant supply and demand timing data to obtain corresponding simulation operation data; wherein, the simulation operation data may include the total energy supply value, the total load demand value, the strategy effectiveness delay (the time interval from the issuance of the strategy to its effectiveness), the actual call power of new energy, the actual call power of the load, the actual call power of energy storage, the allowed call power of new energy, the allowed call power of the load, the allowed call power of energy storage and the number of grid over-limits (such as the number of voltage over-limits, current over-limits and frequency over-limits), etc.

[0045] According to the simulation operation data, corresponding scenario adaptability evaluation indicators are obtained; the scenario adaptability evaluation indicators include supply and demand balance error rate, response delay time, regulation resource utilization rate and over-limit frequency; the regulation resource utilization rate includes new energy utilization rate, load utilization rate and energy storage utilization rate; among them, the supply and demand balance error rate is the absolute difference between the total energy supply value and the total load demand value, which is equivalent to the proportion of the total load demand value. The lower the supply and demand balance error rate, the stronger the strategy's ability to smooth fluctuations; the response delay time is the strategy effectiveness delay, which can reflect the fluctuation response efficiency; the new energy utilization rate can be understood as the ratio of the actual new energy call power to the allowed new energy call power, the load utilization rate can be understood as the ratio of the actual load call power to the allowed load call power, and the energy storage utilization rate can be understood as the ratio of the actual energy storage call power to the allowed energy storage call power, which can effectively reflect the activation efficiency of various resources; the over-limit frequency can be understood as the ratio of the number of grid over-limit times to the number of regulation times, so as to reflect the grid security during the strategy regulation process; it should be noted that in actual applications, indicators such as unit regulation cost, virtual power plant revenue fluctuation and equipment loss rate can also be added according to application requirements.

[0046] Each of the scenario adaptability evaluation indicators is compared and analyzed with the corresponding scenario adaptability evaluation indicator threshold to obtain the corresponding scenario adaptability evaluation result; wherein, the scenario adaptability evaluation indicator threshold can be understood as the indicator validity evaluation threshold set for each scenario adaptability evaluation indicator, which corresponds one-to-one with each scenario adaptability evaluation indicator, and the numerical value of each scenario adaptability evaluation indicator threshold can be selected based on actual application requirements, and is not specifically limited here. Correspondingly, the scenario adaptability evaluation result includes an evaluation result of whether each of the above-mentioned scenario adaptability evaluation indicators meets the scenario adaptability evaluation indicator threshold requirement. If not, it is considered that the indicator does not meet the standard; if so, it is considered that the indicator has met the standard.

[0047] Obtain the scene adaptability evaluation index items that do not meet the standards in the scene adaptability evaluation results, and obtain the control parameters to be optimized from the control parameters based on a preset association analysis algorithm; wherein, the preset association analysis algorithm can adopt the Apriori algorithm, and perform association reasoning analysis based on the pre-set association rules between each scene adaptability evaluation index and each control parameter, so as to find the control parameters corresponding to each non-standard indicator as the control parameters to be optimized; the specific implementation process of the association reasoning analysis based on the Apriori algorithm can refer to the relevant existing technology implementation, and will not be described in detail here.

[0048] Based on the preset adjustment rules, each of the control parameters to be optimized in the control parameters is adjusted to obtain the target scenario control strategy; wherein, the preset adjustment rules can be set according to actual application requirements, such as setting the increase ratio or decrease ratio of the control parameter based on the deviation between the unqualified scenario adaptability evaluation index and the corresponding scenario adaptability evaluation index threshold, which is used to fine-tune the control parameters to be optimized, etc., and then based on the adjusted control parameters and the corresponding control constraints, the corresponding control targets are fine-tuned based on the comprehensive optimization goals of the virtual power plant, which is not specifically limited here.

[0049] This embodiment obtains the target scenario control strategy by simulating and verifying the control effect of the initial scenario control strategy obtained based on intelligent scenario matching and fine-tuning the control strategy. This can effectively ensure the adaptability of the target scenario control strategy to the actual virtual power plant supply and demand fluctuation scenario, thereby improving the application effect of the control strategy.

[0050] S14. Decompose the control targets in the target scenario control strategy into layers and partitions to obtain layered and partitioned control targets, and execute virtual power plant control of the corresponding period according to the layered and partitioned control targets; wherein, the control targets can be understood as the control requirements for the entire virtual power plant, including the new energy output control value, the load demand control value and the energy storage charging and discharging power control value; the layered and partitioned decomposition can be understood as first decomposing the control targets into various resource aggregation areas, and then each resource aggregation area decomposes the corresponding regional control targets into the control target splitting and execution process of each flexible resource in the area, and the corresponding layered and partitioned control targets include the regional control targets of each resource aggregation area and the resource control targets of each flexible resource in each of the resource aggregation areas.

[0051] Specifically, the step of decomposing the control target in the target scene control strategy into layers and partitions to obtain the layered and partitioned control targets includes: The control targets in the target scenario control strategy are split into hierarchical targets based on the preset regional target allocation principle to obtain regional control targets for each of the resource aggregation areas; wherein, the preset regional target allocation principle can be understood as a rule for decomposing the control targets into regional control targets for each resource aggregation area. This embodiment preferably includes splitting the new energy output control value in the control target based on the proportion of new energy installed capacity in each resource aggregation area, splitting the load demand control value in the control target based on the load control potential of each resource aggregation area, and splitting the energy storage charge and discharge power control value in the control target based on the charge and discharge efficiency and charge and discharge cost of each resource aggregation area. The corresponding regional control targets include regional new energy output control targets, regional load demand control targets, and regional energy storage charge and discharge power control targets. Specifically, the step of splitting the control targets in the target scenario control strategy into hierarchical targets based on the preset regional target allocation principle to obtain regional control targets for each of the resource aggregation areas includes: Based on the proportion of installed new energy capacity and the current available output proportion of each resource aggregation region, a new energy production control allocation ratio for the corresponding resource aggregation region is obtained, and based on the new energy production control allocation ratio and the new energy production control value, a regional new energy production control target for the corresponding resource aggregation region is obtained. The proportion of installed new energy capacity can be understood as the ratio of the installed new energy capacity of the resource aggregation region to the installed new energy capacity of the entire virtual power plant. The current available output proportion can be understood as the ratio of the actual available output limit of the resource aggregation region to the installed new energy capacity of the region. This is a correction factor added to take into account that the new energy in the actual resource aggregation region may be affected by the environment (for example, if the wind speed in resource aggregation region A is lower than the predicted value, the corresponding wind turbine output limit is reduced to 50% of the corresponding installed capacity, while the wind turbine output limit of other regions can reach 80%, and the corresponding allocation ratio needs to be reduced to ensure resource utilization). In actual applications, the product of the proportion of installed new energy capacity and the current available output proportion of each resource aggregation region is used as the required new energy production control allocation ratio, and the required regional new energy production control target is then obtained based on the product of the new energy production control allocation ratio and the new energy production control value. This embodiment adopts the proportion of new energy installed capacity as the basic allocation ratio, and then uses the current output capacity proportion affected by the regional environment as a correction coefficient to calculate the final new energy output control allocation ratio, which can simultaneously ensure the fairness and rationality of the allocation of control targets in each region.

[0052] A regional load regulation potential summary analysis is performed based on the load capacity and preset regulation potential coefficient of different regulation levels in each of the resource aggregation areas to obtain a corresponding regional load regulation potential ratio, and a corresponding regional load demand regulation target is obtained based on the regional load regulation potential ratio and the load demand regulation value; wherein, the regional load regulation potential summary analysis process may include: classifying the adjustable loads in each resource aggregation area according to the corresponding regulation level, and setting corresponding preset regulation potential coefficients for the adjustable loads of different regulation levels (the higher the regulation level, the larger the corresponding preset regulation potential coefficient can be selected in the range of 0~1); multiplying the sum of the adjustable load capacities of the same regulation level in the same resource aggregation area by the preset regulation potential coefficient of the corresponding regulation level to obtain the load regulation potential of the corresponding regulation level; accumulating the load regulation potentials of all regulation levels in the same resource aggregation area to obtain the regional load regulation potential; and then comparing the regional load regulation potential of each resource aggregation area with the sum of all regional load regulation potentials to obtain the corresponding regional load regulation potential ratio. The corresponding regional load demand control target can be understood as the product of the regional load control potential ratio and the load demand control value. If the obtained regional load demand control target exceeds the maximum controllable load of the corresponding resource aggregation area, the part of the regional load demand control target that exceeds the maximum controllable load needs to be allocated to other resource aggregation areas that still have control potential according to the regional load control potential ratio of other resource aggregation areas, to ensure that the regional load demand control targets of each resource aggregation area are within the corresponding allowable control range.

[0053] According to the energy storage power control type of the energy storage charge and discharge power control value, the energy storage charge and discharge power allocation ratio of each resource aggregation area is obtained, and according to the energy storage charge and discharge power allocation ratio and the energy storage charge and discharge power control value, the corresponding regional energy storage charge and discharge power control target is obtained; wherein, the energy storage power control type may include energy storage charging power control and energy storage discharging power control, and the corresponding energy storage charge and discharge power control values ​​include energy storage charging power control value and energy storage discharging power control value; specifically, the step of obtaining the energy storage charge and discharge power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charge and discharge power control value includes: When the energy storage power control type is energy storage charging power control, the energy storage charging efficiency, unit charging cost and allowed charging power ratio of each resource aggregation area are obtained respectively; among which, the allowed charging power ratio can be understood as the ratio of the allowed charging power of the resource aggregation area to the sum of the allowed charging powers of all resource aggregation areas; it should be noted that the energy storage charging efficiency, unit charging cost and allowed charging power of each resource aggregation area can all be obtained based on the relevant virtual power plant operation information monitoring system, which will not be described in detail here.

[0054] Based on the charging allocation ratio setting principle, a weighted fusion analysis is performed on the energy storage charging efficiency, unit charging cost, and allowable charging power ratio of each resource aggregation area to obtain the corresponding regional energy storage charging power allocation ratio. The charging allocation ratio setting principle includes that the regional energy storage charging power allocation ratio is proportional to the energy storage charging efficiency and the allowable charging power ratio, and inversely proportional to the unit charging cost. That is, the regional energy storage charging power allocation ratio can be expressed as: Where, represents the regional energy storage charging power allocation ratio of resource aggregation area q; and They represent the unit charging cost of resource aggregation area q and the corresponding unit charging cost reference value respectively; and They represent the energy storage charging efficiency and the proportion of allowed charging power in the resource aggregation area q respectively; 、 and Represents the weight coefficient. The size of each weight coefficient can be adjusted according to the actual application requirements. , which can ensure the reliability and fairness of the energy storage charging power distribution ratio.

[0055] In practical applications, the corresponding regional energy storage charging power control target can be obtained based on the product of the energy storage charging power allocation ratio and the corresponding energy storage charging and discharging power control value (actually the energy storage charging power control value). However, considering that the obtained regional energy storage charging power control target may exceed the corresponding allowable charging power upper limit, in order to ensure the rationality of setting the regional energy storage charging power control target, this embodiment preferably determines whether each regional energy storage charging power control target is greater than the corresponding allowable charging power upper limit after obtaining the regional energy storage charging power control target for each resource aggregation area. If so, the portion of the charging power that exceeds the allowable charging power upper limit by the regional energy storage charging power control target is allocated to other resource aggregation areas with control potential, ensuring that the regional energy storage charging power control target of each resource aggregation area is within the corresponding allowable control range.

[0056] Correspondingly, the step of obtaining the energy storage charging and discharging power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charging and discharging power control value further includes: When the energy storage charging and discharging power control value is energy storage discharge power control, the energy storage discharge efficiency, unit discharge cost and allowable discharge power ratio of each resource aggregation area are obtained respectively; among which, the allowable discharge power ratio can be understood as the ratio of the allowable discharge power of the resource aggregation area to the sum of the allowable discharge powers of all resource aggregation areas; it should be noted that the energy storage discharge efficiency, unit discharge cost and allowable discharge power of each resource aggregation area can all be obtained based on the relevant virtual power plant operation information monitoring system, which will not be described in detail here.

[0057] Based on the discharge allocation ratio setting principle, a weighted fusion analysis is performed on the energy storage discharge efficiency, unit discharge cost, and allowable discharge power ratio of each resource aggregation area to obtain the corresponding regional energy storage discharge power allocation ratio; the discharge allocation ratio setting principle includes that the regional energy storage discharge power allocation ratio is proportional to the energy storage discharge efficiency and the allowable discharge power ratio, and the regional energy storage discharge power allocation ratio is inversely proportional to the unit discharge cost; Where, represents the regional energy storage discharge power allocation ratio of resource aggregation area q; and They represent the unit discharge cost of resource aggregation area q and the corresponding unit discharge cost reference value respectively; and They represent the energy storage discharge efficiency and the allowable discharge power ratio of the resource aggregation area q respectively; 、 and Represents the weight coefficient. The size of each weight coefficient can be adjusted according to the actual application requirements. , which can ensure the reliability and fairness of the energy storage discharge power distribution ratio.

[0058] Similarly, in actual applications, the corresponding regional energy storage discharge power control target can be obtained based on the product of the energy storage discharge power allocation ratio and the corresponding energy storage charge and discharge power control value (actually the energy storage discharge power control value). However, considering that the obtained regional energy storage discharge power control target may exceed the corresponding allowable discharge power upper limit, in order to ensure the rationality of setting the regional energy storage discharge power control target, this embodiment preferably determines whether each regional energy storage discharge power control target is greater than the corresponding allowable discharge power upper limit after obtaining the regional energy storage discharge power control target of each resource aggregation area. If so, the portion of the discharge power of the regional energy storage discharge power control target that exceeds the allowable discharge power upper limit is allocated to other resource aggregation areas with control potential, ensuring that the regional energy storage discharge power control target of each resource aggregation area is within the corresponding allowable control range.

[0059] According to the regional control targets of each resource aggregation area, the regional targets are decomposed based on the resource scheduling optimization model in the preset area to obtain the resource control targets of each flexible resource in the corresponding resource aggregation area; wherein, the resource scheduling optimization model in the preset area can be understood as an optimization model for reasonably allocating the regional control targets of each resource aggregation area to each flexible resource in the resource aggregation area according to a certain optimization target. In order to ensure the collaborative optimization of virtual power plant resources with low cost and high resource utilization, improve the peak shaving and valley filling benefits, and ensure the stability of power grid operation, this embodiment preferably constructs the resource scheduling optimization model in the preset area with the optimization target of balancing the control cost and the power grid operation risk. The specific objective function is expressed as: Where, in, and They represent the regional regulation cost and regional voltage over-limit risk cost respectively; and represents the weight coefficient, and , which can be set based on actual application requirements; represents the amount of wasted energy from new energy i; Indicates the reduction of the adjustable load k; and represents the charging power and discharging power of the energy storage device l; represents the unit cost of curtailed electricity from new energy source i; represents the unit compensation cost of reducing the adjustable load k; and represents the unit cost of charging and the unit cost of discharging of the energy storage device l; and They represent the positive voltage offset and negative voltage offset of node m, respectively, both are absolute deviation values; represents the unit risk cost of voltage deviation at node m; 、 、 and They represent the number of new energy sources, adjustable loads, energy storage devices and nodes in the aggregation area respectively.

[0060] To ensure the efficiency and reliability of obtaining the resource control targets of each flexible resource in the resource aggregation area based on the resource scheduling optimization model in the preset area, the preferred constraints in this embodiment include regional total power balance constraints, new energy output constraints, adjustable load operation constraints, energy storage equipment operation constraints, and grid voltage risk constraints, which are specifically expressed as follows: 1) Regional total power balance constraint, expressed as: Where, represents the actual dispatch output of new energy source i; 、 and They respectively represent the regional new energy output control target, the regional load demand control target, and the regional energy storage charging and discharging power control target (positive value means discharging, negative value means charging, and zero value means not in use).

[0061] 2) New energy output constraints, expressed as: in, It represents the predicted output of renewable energy i, which can be obtained based on the renewable energy output prediction mechanism of the existing virtual power plant and will not be described in detail here; Indicates the maximum amount of power wasted by the new energy i, which can be set according to the actual application scenario.

[0062] 3) Adjustable load operation constraints, expressed as: in, Indicates the maximum reduction of the adjustable load k, which can be set according to the actual application scenario.

[0063] 4) Energy storage equipment operation constraints, expressed as: Where, Indicates the charge and discharge status of the energy storage device l, 0 indicates charging, and 1 indicates discharging; and Respectively represent the maximum charging power and maximum discharging power of energy storage device 1, which can be set according to the actual application scenario; 、 、 and Respectively represent the state of charge, initial state of charge, minimum state of charge and maximum state of charge of the energy storage device 1; Indicates the duration of the control cycle; and They represent the charging efficiency and discharging efficiency of the energy storage device l respectively; Indicates the rated capacity of the energy storage device l.

[0064] 5) The grid voltage risk constraint can be set based on the existing grid operation safety conditions and is expressed as: Where, represents the voltage change at node m; represents the initial voltage of node m; and They represent the minimum and maximum allowable voltages of node m, respectively, and can be set according to the actual application scenario; represents the sensitivity of node n’s injected power change to node m, represents the injection power variation of node n. Both the sensitivity and the injection power variation can be calculated using relevant existing technologies and will not be described in detail here. and Respectively indicate that the voltage of node m exceeds the upper limit and the voltage exceeds the lower limit. If the flag value is 1, it means that the limit is exceeded; Represents a large constant, a large and fixed constant used for out-of-limit identification linearization.

[0065] It should be noted that the resource control targets of each flexible resource in the resource aggregation area vary depending on the type of flexible resource. The resource control target of new energy resources is the new energy production control target (output control target), the resource control target of adjustable load resources is the load demand control target, and the resource control target of energy storage resources is the energy storage charging and discharging power control target. The specific control targets of each flexible resource can be obtained based on the resource operation data of the actual resource aggregation area by using existing commercial solvers (such as CPLEX, Gurobi, etc.) to efficiently solve the resource scheduling optimization model in the above-mentioned preset area. The specific solution process will not be described in detail here. This embodiment allocates control targets to each resource aggregation area by designing a resource scheduling optimization model in the preset area with the optimization target of balancing new energy consumption, control costs and grid operation risks. This not only achieves efficient collaborative optimization of multiple flexible resources with low cost and high resource utilization, improves the peak shaving and valley filling benefits of the virtual power plant, but also ensures the safety and stability of the grid operation during the control process.

[0066] The embodiment of the present invention provides a method for periodically acquiring virtual power plant supply and demand time series data including new energy supply time series data and load demand time series data, performs supply and demand fluctuation characteristic analysis on the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data, performs scenario matching analysis on the supply and demand fluctuation time series data based on a pre-built multi-scenario control strategy library including supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under multiple fluctuation scenarios to obtain the corresponding initial scenario control strategy, performs scenario adaptability simulation analysis on the initial scenario control strategy, optimizes the initial scenario control strategy according to the corresponding simulation analysis results to obtain the target scenario control strategy, and adjusts the target scenario control strategy accordingly. The control objectives in the control strategy are decomposed into layers and partitions to obtain layered and partitioned control objectives, and the technical solution for executing virtual power plant control in the corresponding period is implemented according to the layered and partitioned control objectives. The virtual power plant control design based on the intelligent scenario control strategy acquisition mechanism of supply and demand fluctuations is combined with the scenario adaptability analysis mechanism and the layered and partitioned decomposition mechanism of control objectives. It can not only realize the efficient interaction of source, load and storage of virtual power plants in complex and uncertain environments, and improve the peak-shaving and valley-filling capacity of the power grid, but also effectively smooth out supply and demand fluctuations, ensure the stable operation of the power grid, and thus significantly improve the collaborative optimization capability and operation resilience of the virtual power plant, provide reliable technical support for the power supply needs of the virtual power plant, and have good engineering application prospects.

[0067] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0068] In one embodiment, Figure 2 As shown, a multi-scenario coordinated control system for a virtual power plant for power supply security is provided. The virtual power plant includes multiple resource aggregation areas, each of which includes multiple flexible resources. The system includes: Fluctuation analysis module 1 is used to periodically obtain virtual power plant supply and demand time series data, and perform supply and demand fluctuation characteristics analysis on the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data; the virtual power plant supply and demand time series data includes new energy supply time series data and load demand time series data; Scenario matching module 2 is configured to perform scenario matching analysis based on the supply and demand fluctuation time series data and a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy; the multi-scenario control strategy library includes supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under various fluctuation scenarios; the scenario benchmark control strategy includes control parameters, control constraints, and control objectives; A strategy acquisition module 3 is used to perform a scenario adaptability simulation analysis on the initial scenario control strategy, and optimize the initial scenario control strategy according to the corresponding simulation analysis results to obtain a target scenario control strategy; The target decomposition module 4 is used to decompose the control target in the target scenario control strategy into layers and partitions to obtain layered and partitioned control targets, and perform virtual power plant control of the corresponding period according to the layered and partitioned control targets.

[0069] Regarding the specific limitations of the multi-scenario collaborative control system of a virtual power plant for ensuring power supply, please refer to the limitations of the multi-scenario collaborative control method of a virtual power plant for ensuring power supply above. The corresponding technical effects can also be obtained equivalently, so we will not go into details here. Each module in the above-mentioned multi-scenario collaborative control system of a virtual power plant for ensuring power supply can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0070] In summary, the embodiments of the present invention provide a multi-scenario collaborative control method and system for virtual power plants for power supply security. The virtual power plant control design is based on an intelligent scenario control strategy acquisition mechanism for supply and demand fluctuations combined with a scenario suitability analysis mechanism and a hierarchical and partitioned decomposition mechanism for control targets. It can not only achieve efficient interaction between sources, loads and storage in virtual power plants under complex and uncertain environments, and improve the peak-shaving and valley-filling capabilities of power grids, but also effectively smooth out supply and demand fluctuations, ensure stable power grid operation, and significantly improve the collaborative optimization capabilities and operational resilience of virtual power plants, providing reliable technical support for the power supply security needs of virtual power plants, and has good engineering application prospects.

[0071] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] The above embodiments only express several preferred embodiments of the present application, which are described in more detail and in more detail, but cannot be understood as limiting the scope of the patent. It should be noted that for ordinary skilled in the art, several improvements and replacements can be made without departing from the technical principles of the present application, and these improvements and replacements should also be considered as the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the protection scope of the claims.

Claims

1. A multi-scenario collaborative control method for a virtual power plant for power supply security, characterized in that: The virtual power plant includes multiple resource aggregation areas, each of which includes multiple flexible resources. The method includes: Periodically acquiring virtual power plant supply and demand time series data, and performing supply and demand fluctuation characteristic analysis on the virtual power plant supply and demand time series data to generate corresponding supply and demand fluctuation time series data; the virtual power plant supply and demand time series data includes new energy supply time series data and load demand time series data; Based on the supply and demand fluctuation time series data, a scenario matching analysis is performed based on a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy; the multi-scenario control strategy library includes supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under various fluctuation scenarios; the scenario benchmark control strategy includes control parameters, control constraints, and control objectives; Performing a scene adaptability simulation analysis on the initial scene control strategy, and optimizing the initial scene control strategy according to the corresponding simulation analysis results to obtain a target scene control strategy; The control targets in the target scenario control strategy are decomposed into layers and partitions to obtain layered and partitioned control targets, and virtual power plant control of the corresponding period is performed according to the layered and partitioned control targets.

2. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 1 is characterized in that: The steps of constructing the multi-scenario control strategy library include: Extract source, load and storage information from the historical operating data of the virtual power plant to obtain the historical energy production sequence of the power plant, the historical load demand sequence of the power plant, and the charging and discharging power sequence of the power plant energy storage; According to the preset time period splitting duration, the power plant's historical energy production sequence, the power plant's historical load demand sequence, and the power plant's energy storage charging and discharging power sequence are respectively split into time periods to obtain corresponding energy production subsequence sets, load demand subsequence sets, and energy storage charging and discharging power subsequence sets; Extracting fluctuation characteristics of each subsequence in the energy production subsequence set, the load demand subsequence set, and the energy storage charge and discharge power subsequence set, respectively, to obtain corresponding energy fluctuation characteristic data sets, load fluctuation characteristic data sets, and energy storage charge and discharge power fluctuation characteristic data sets; Performing cluster analysis on the energy fluctuation characteristic dataset, the load fluctuation characteristic dataset, and the energy storage charging and discharging power fluctuation characteristic dataset to obtain multiple source-load-storage interaction scenarios; The energy fluctuation characteristic data and load fluctuation characteristic data of the cluster center corresponding to each source-load-storage interaction scenario are spliced ​​respectively to obtain the corresponding supply and demand fluctuation benchmark characteristic vector, and the corresponding scenario benchmark control strategy is obtained according to the historical control strategy of the cluster center corresponding to each source-load-storage interaction scenario; The supply and demand fluctuation benchmark feature vectors and the scenario benchmark control strategies of each source-load-storage interaction scenario are aggregated to obtain the multi-scenario control strategy library.

3. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 2 is characterized in that: The virtual power plant historical operation data includes the regional energy production history sequence, the regional load demand history sequence and the regional energy storage charging and discharging power history sequence of each resource aggregation area; The step of extracting source, load and storage information from the historical operation data of the virtual power plant to obtain the historical energy production sequence of the power plant, the historical load demand sequence of the power plant and the charging and discharging power sequence of the power plant includes: The regional energy production history sequence, the regional load demand history sequence, and the regional energy storage charge and discharge power history sequence of each resource aggregation area are respectively cleaned and time-aligned to obtain the corresponding regional energy production sequence to be analyzed, the regional load demand sequence to be analyzed, and the regional energy storage charge and discharge power sequence to be analyzed; Aggregating the energy production sequences of the regions to be analyzed in all resource aggregation regions at the sampling time to obtain the corresponding historical energy production sequences of the power plants, and aggregating the load demand sequences of the regions to be analyzed in all resource aggregation regions at the sampling time to obtain the corresponding historical load demand sequences of the power plants; The energy storage charge and discharge power sequences of the to-be-analyzed regions in each resource aggregation region are weighted and summarized using the corresponding regional energy storage capacity ratio as a weight to obtain the corresponding power plant energy storage charge and discharge power sequence.

4. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 1 is characterized in that: The supply and demand fluctuation time series data includes energy supply fluctuation time series data and load demand fluctuation time series data; The step of performing scenario matching analysis based on the supply and demand fluctuation time series data and obtaining the corresponding initial scenario control strategy based on a pre-built multi-scenario control strategy library includes: Extracting corresponding fluctuation characteristics from the energy supply fluctuation time series data and the load demand fluctuation time series data respectively to generate corresponding energy supply fluctuation characteristics and load demand fluctuation characteristics; generating a corresponding energy supply fluctuation characteristic vector and a load demand fluctuation characteristic vector according to the energy supply fluctuation characteristic and the load demand fluctuation characteristic respectively; Concatenating the energy supply fluctuation characteristic vector and the load demand fluctuation characteristic vector to obtain a supply and demand fluctuation characteristic vector; Performing a similarity analysis on the supply and demand fluctuation feature vector and the supply and demand fluctuation benchmark feature vector corresponding to each scenario control strategy in the multi-scenario regulation strategy library to obtain a corresponding scenario similarity value; According to all the scene similarity values, obtaining a number of relevant scene control strategies based on a preset similarity threshold; Normalizing the scene similarity values ​​corresponding to the relevant scene control strategies to obtain corresponding similarity weights; Based on the similarity weights of the respective related scene control strategies, all the related scene control strategies are weightedly fused to obtain the initial scene control strategy.

5. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 1 is characterized in that: The control parameters include new energy control parameters, energy storage control parameters and load control parameters; The step of performing scene adaptability simulation analysis on the initial scene control strategy and optimizing the initial scene control strategy according to the corresponding simulation analysis results to obtain the target scene control strategy includes: Performing a virtual power plant strategy scheduling simulation based on the initial scenario control strategy and the virtual power plant supply and demand time series data to obtain corresponding simulation operation data; According to the simulation operation data, corresponding scenario adaptability evaluation indicators are obtained; the scenario adaptability evaluation indicators include supply and demand balance error rate, response delay time, regulation resource utilization rate and over-limit frequency; the regulation resource utilization rate includes new energy utilization rate, load utilization rate and energy storage utilization rate; Comparing and analyzing each of the scene adaptability evaluation indicators with the corresponding scene adaptability evaluation indicator thresholds to obtain corresponding scene adaptability evaluation results; Obtaining the scene adaptability evaluation index items that do not meet the standards in the scene adaptability evaluation results, and obtaining the control parameters to be optimized from the control parameters based on a preset association analysis algorithm; Based on preset adjustment rules, each of the control parameters to be optimized in the control parameters is adjusted to obtain the target scene control strategy.

6. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 1 is characterized in that: The control targets include new energy output control value, load demand control value and energy storage charging and discharging power control value; The hierarchical and regional control targets include the regional control targets of each resource aggregation area and the resource control targets of each flexible resource in each resource aggregation area; The step of performing hierarchical and partitioned decomposition of the control target in the target scene control strategy to obtain the hierarchical and partitioned control target comprises: The control targets in the target scenario control strategy are split into hierarchical targets based on a preset regional target allocation principle to obtain regional control targets for each resource aggregation area; According to the regional control targets of each of the resource aggregation areas, the regional targets are decomposed based on the resource scheduling optimization model in the preset area to obtain the resource control targets of each flexible resource in the corresponding resource aggregation area; the resource scheduling optimization model in the preset area is constructed with the optimization target of balancing new energy consumption, control costs and power grid operation risks.

7. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 6 is characterized in that: The regional control targets include regional new energy output control targets, regional load demand control targets, and regional energy storage charging and discharging power control targets; The step of performing hierarchical target splitting on the control target in the target scenario control strategy based on a preset regional target allocation principle to obtain the regional control target of each resource aggregation area includes: According to the proportion of new energy installed capacity and the proportion of current available output in each resource aggregation area, the new energy output control allocation ratio of the corresponding resource aggregation area is obtained, and according to the new energy output control allocation ratio and the new energy output control value, the regional new energy output control target of the corresponding resource aggregation area is obtained; performing a summary analysis of regional load regulation potential based on the load capacity of different regulation levels and the preset regulation potential coefficient within each of the resource aggregation areas to obtain a corresponding regional load regulation potential ratio, and obtaining a corresponding regional load demand regulation target based on the regional load regulation potential ratio and the load demand regulation value; According to the energy storage power control type of the energy storage charge and discharge power control value, the energy storage charge and discharge power allocation ratio of each resource aggregation area is obtained, and according to the energy storage charge and discharge power allocation ratio and the energy storage charge and discharge power control value, the corresponding regional energy storage charge and discharge power control target is obtained.

8. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 7 is characterized in that: The step of obtaining the energy storage charging and discharging power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charging and discharging power control value includes: When the energy storage power control type is energy storage charging power control, the energy storage charging efficiency, unit charging cost and allowed charging power ratio of each resource aggregation area are obtained respectively; Based on the charging allocation ratio setting principle, the energy storage charging efficiency, unit charging cost and allowable charging power ratio of each resource aggregation area are weighted and integrated for analysis to obtain the corresponding regional energy storage charging power allocation ratio; the charging allocation ratio setting principle includes that the regional energy storage charging power allocation ratio is directly proportional to the energy storage charging efficiency and the allowable charging power ratio, and the regional energy storage charging power allocation ratio is inversely proportional to the unit charging cost.

9. The multi-scenario coordinated control method for a virtual power plant for power supply security according to claim 7 is characterized in that: The step of obtaining the energy storage charging and discharging power allocation ratio of each resource aggregation area according to the energy storage power control type of the energy storage charging and discharging power control value further includes: When the energy storage charging and discharging power control value is energy storage discharging power control, the energy storage discharging efficiency, unit discharge cost and allowable discharge power ratio of each resource aggregation area are obtained respectively; Based on the discharge allocation ratio setting principle, a weighted fusion analysis is performed on the energy storage discharge efficiency, unit discharge cost, and allowable discharge power ratio of each resource aggregation area to obtain the corresponding regional energy storage discharge power allocation ratio; the discharge allocation ratio setting principle includes that the regional energy storage discharge power allocation ratio is directly proportional to the energy storage discharge efficiency and the allowable discharge power ratio, and the regional energy storage discharge power allocation ratio is inversely proportional to the unit discharge cost.

10. A multi-scenario collaborative control system for virtual power plants for power supply security, characterized by: The virtual power plant includes multiple resource aggregation areas, each of which includes multiple flexible resources. The system includes: a fluctuation analysis module for periodically acquiring supply and demand time series data of a virtual power plant, and performing supply and demand fluctuation characteristic analysis on the supply and demand time series data of the virtual power plant, to generate corresponding supply and demand fluctuation time series data; the supply and demand time series data of the virtual power plant includes new energy supply time series data and load demand time series data; A scenario matching module is configured to perform scenario matching analysis based on the supply and demand fluctuation time series data and a pre-built multi-scenario control strategy library to obtain a corresponding initial scenario control strategy; the multi-scenario control strategy library includes supply and demand fluctuation benchmark feature vectors and scenario benchmark control strategies under various fluctuation scenarios; the scenario benchmark control strategy includes control parameters, control constraints, and control objectives; A strategy acquisition module is used to perform a scenario adaptability simulation analysis on the initial scenario control strategy, and optimize the initial scenario control strategy according to the corresponding simulation analysis results to obtain a target scenario control strategy; The target decomposition module is used to decompose the control targets in the target scenario control strategy into layers and partitions to obtain layered and partitioned control targets, and execute virtual power plant control of the corresponding period according to the layered and partitioned control targets.

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