Virtual power plant multivariate main body aggregation adjustment capability quantitative evaluation method and system

Abstract

The invention discloses a virtual power plant multivariate main body aggregation adjustment capability quantitative evaluation method and system, and the method comprises the steps: extracting common parameters based on the operation characteristics and adjustment characteristics of various adjustable loads of a virtual power plant, and building a virtual power plant multivariate main body adjustment capability index system; constructing a virtual power plant equipment-aggregation unit-virtual power plant three-level adjustment capability data model based on the adjustment capability index system; collecting adjustable load resource data under the jurisdiction of the virtual power plant, and inputting the adjustable load resource data into the adjustment capability data model; calculating a virtual power plant multi-element main body adjustment capability index and a virtual power plant aggregation adjustment capability index; establishing a virtual power plant regulation capability evaluation model, performing quantitative scoring on the regulation capability of the virtual power plant, and generating an evaluation report; according to the method, the differential adjustment characteristics of the multi-element main body in the virtual power plant are considered, the precision of the evaluation result can be improved, and the actual adjustment capability of the virtual power plant is accurately reflected.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the regulation ability of a virtual power plant, and in particular to a method and system for quantitatively evaluating the aggregated regulation ability of multiple entities in a virtual power plant. Background Art

[0002] Driven by the "dual carbon" goal, the grid-connected scale of new energy has grown rapidly, but its volatility and uncertainty pose severe challenges to the stable operation of the power grid. As an important participant in grid interaction, demand-side resources can effectively promote the consumption of new energy and ensure the safe and stable operation of the power grid. As a new organizational form, a virtual power plant (VPP) participates in grid interaction scenarios such as peak shaving, frequency modulation, and emergency support by aggregating multiple demand-side entities (such as distributed energy, energy storage systems, adjustable loads, etc.), fully tapping the potential of flexible demand-side resources, and has become an indispensable part of the new power system. With the continuous development of the power market, the role of virtual power plants in the safe operation of the power grid and market-based transactions has become increasingly prominent.

[0003] Currently, the evaluation of the regulation ability of virtual power plants mainly adopts the ability detection method of the overall packaging method, that is, regarding the virtual power plant as a whole and testing its response speed and regulation accuracy by simulating grid demand scenarios. This method is mainly based on the external performance of the virtual power plant, and by simulating grid demand scenarios, its response speed and regulation accuracy are tested to obtain its overall regulation ability.

[0004] However, the evaluation method of the overall packaging method ignores the differential regulation characteristics of multiple entities inside the virtual power plant, resulting in insufficient accuracy of the evaluation results and making it difficult to accurately reflect the actual regulation ability of the virtual power plant. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide a method for quantitatively evaluating the aggregated regulation ability of multiple entities in a virtual power plant considering the differential regulation characteristics of multiple entities inside the virtual power plant. On the other hand, a system for quantitatively evaluating the aggregated regulation ability of multiple entities in a virtual power plant is provided.

[0006] Technical Solution: A method for quantitatively evaluating the aggregated regulation ability of multiple entities in a virtual power plant according to the present invention includes:

[0007] Based on the operation characteristics and regulation characteristics of various adjustable loads in the virtual power plant, common parameters are extracted to establish an index system for the regulation ability of multiple entities in the virtual power plant;

[0008] Based on the regulation ability index system, a three-level regulation ability data model of virtual power plant equipment - aggregation unit - virtual power plant is constructed to describe the aggregation relationship of adjustable load resources among the equipment, aggregation unit, and virtual power plant levels;

[0009] Collect the data of adjustable load resources under the virtual power plant, and enter the adjustable load resource data into the information table corresponding to the regulation capacity data model;

[0010] Calculate the regulation capacity indexes of multiple entities in the virtual power plant and the aggregated regulation capacity index of the virtual power plant;

[0011] Based on the regulation capacity data model, the regulation capacity indexes of multiple entities, and the aggregated regulation capacity index, establish a virtual power plant regulation capacity evaluation model, quantitatively score the regulation capacity of the virtual power plant, and generate an evaluation report.

[0012] Through the above technical solutions, based on the operation characteristics and regulation characteristics of various adjustable loads within the virtual power plant, common parameters are extracted and a regulation capacity index system for multiple entities is established, providing a scientific basis for quantitative evaluation; the constructed three-level regulation capacity data model of "equipment - aggregation unit - virtual power plant" clearly describes the aggregation relationship of adjustable load resources at different levels, realizing multi-level management of resources; by collecting and entering the data of adjustable load resources under the virtual power plant, the actual operation data is closely combined with the model, ensuring the accuracy and real-time nature of the evaluation; by calculating the regulation capacity indexes of multiple entities in the virtual power plant and the aggregated regulation capacity index and entering the results into the database, a comprehensive quantitative evaluation and dynamic monitoring of the regulation capacity of the virtual power plant are realized.

[0013] Preferably, the regulation capacity index system of multiple entities in the virtual power plant includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate.

[0014] Through the above technical solutions, the regulation capacity provides the upper limit data of the resource regulation capacity, laying a foundation for planning and scheduling; the regulation duration evaluates the continuous regulation capacity of the resources, optimizing the resource allocation; the regulation deviation rate quantifies the regulation accuracy, improving the accuracy and reliability of regulation; the regulation rate measures the dynamic response ability of the resources, enhancing the flexibility and stability of the system; this index system not only scientifically depicts the differentiated regulation characteristics of multiple entities, but also provides a key basis for subsequent calculation of the aggregated regulation capacity and optimized scheduling, significantly improving the overall regulation performance, operation efficiency of the virtual power plant, and its supporting ability for the power system.

[0015] Preferably, the regulation capacity data model includes an equipment model, an aggregation unit model, and a virtual power plant model. The aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to the resource type. The equipment model includes an equipment file information table and an equipment operation information table. The aggregation unit model includes an aggregation unit file information table. The virtual power plant model includes a virtual power plant file information table.

[0016] Through the above technical solution, the device model details the static attributes and dynamic operating status of the device through the device file information table and the device operation information table, providing comprehensive support for the underlying data; the aggregation unit model classifies and aggregates adjustable load resources according to resource types, forming a structured aggregation unit file information table, realizing the classified management and efficient integration of resources; the virtual power plant model summarizes the overall information of the virtual power plant through the virtual power plant file information table, providing a data basis for upper-level decision-making; this hierarchical modeling method not only clearly describes the aggregation relationship of adjustable load resources between different levels, but also realizes the refined management and dynamic monitoring of resources, providing reliable data support and a scientific basis for the quantitative evaluation and optimal scheduling of the regulation capacity of the virtual power plant.

[0017] Preferably, collecting the data of adjustable load resources under the jurisdiction of the virtual power plant and entering the adjustable load resource data into the information table corresponding to the regulation capacity data model includes:

[0018] Obtain the basic file data of the adjustable load resource and enter it into the device file information table. The basic file data of the adjustable load resource includes the first device primary key ID, the virtual power plant ID to which it belongs, and the device type.

[0019] Obtain the basic file data of the virtual power plant and enter it into the virtual power plant file information table. The basic file data of the virtual power plant includes the virtual power plant primary key ID, the virtual power plant name, and the installed capacity.

[0020] Based on the device type, create corresponding aggregation unit basic file data in the aggregation unit file information table. The aggregation unit basic file data includes the aggregation unit primary key ID, the aggregation unit name, and the virtual power plant ID to which it belongs.

[0021] Add the aggregation unit primary key ID to the aggregation unit ID field of the device file information table to form an "equipment - aggregation unit - virtual power plant" adjustable load resource aggregation hierarchy.

[0022] Obtain the adjustable load operation data and enter it into the device operation information table. The adjustable load operation data includes the second device primary key ID, the operation load data at each moment, and the baseline load data. The first device primary key ID is the same as the second device primary key ID.

[0023] Through the above technical solution, the basic archive data of adjustable load resources and the basic archive data of virtual power plants are obtained, and they are entered into the equipment archive information table and the virtual power plant archive information table respectively, and the basic information framework of equipment and virtual power plants is established; based on the equipment type, the basic archive data of aggregation units is created and entered into the aggregation unit archive information table, and the classified aggregation management of adjustable load resources is realized; by adding the aggregation unit primary key ID to the equipment archive information table, a clear three-level aggregation relationship of "equipment-aggregation unit-virtual power plant" is formed, which provides a data basis for multi-level regulation capability analysis; finally, by obtaining and entering the adjustable load operation data, the dynamic operation status of the equipment is associated with the static archive information, ensuring the integrity and consistency of the data.

[0024] Preferably, the calculation of the virtual power plant multi-subject regulation capability index includes:

[0025] Get the adjustable load adjustment target value P 目标 , set the coefficient k, and convert k*P 目标 The effective threshold value P of the adjustable load is defined as 门槛 ;

[0026] Obtain and record the load regulation response value at each moment and the corresponding baseline load and time;

[0027] Filter out those greater than P 门槛 The load regulation response value {P1P2…P n}} and the corresponding time {T1T2…T n}}, calculate the adjustment duration T 调节 =T n -T1 and enter the equipment file information table;

[0028] Calculate the load regulation response value {P1P2…P n The mean value of the adjusted response P 响应 , obtain each moment {T1T2…T n} and calculate its mean P 基线 , calculate the regulating capacity P 调节}=|P 响应 -P 基线}|And enter the equipment file information table;

[0029] Based on the regulation target value and regulation capacity, the regulation deviation rate d is calculated. 调节 And enter the equipment file information table, the calculation is shown as follows:

[0030]

[0031] Get the first adjustment to P 门槛 Time T 门槛, the adjustment instruction issuance time T 指令 and its corresponding instruction load value P 指令 , calculate the adjustment rate v 调节 and record it in the equipment file information table. The calculation formula is as follows:

[0032]

[0033] Through the above technical solution, setting the adjustment effective threshold value and screening out the effective load adjustment response value ensure the accuracy and practicability of the adjustment ability calculation; by calculating key indicators such as the adjustment duration, adjustment capacity, adjustment deviation rate, and adjustment rate, it comprehensively reflects the adjustment performance of the adjustable load resources, including its response speed, adjustment accuracy, and stability; recording these indicators in the equipment file information table realizes the structured storage and dynamic update of the adjustment ability data, providing data support for the real-time monitoring and optimal dispatching of the virtual power plant.

[0034] Preferably, the calculation of the virtual power plant aggregation adjustment ability index includes:

[0035] Obtain the primary key ID of each aggregation unit from the aggregation unit file information table, and obtain the corresponding adjustable load information from the equipment file information table according to the primary key ID of each aggregation unit, including the adjustable load adjustment capacity P1, adjustable load adjustment duration T1, adjustable load adjustment deviation rate d1, and adjustable load adjustment rate v1;

[0036] Obtain the adjustment sustainable time required for the virtual power plant to participate in the power grid regulation scenario as the aggregation unit adjustment duration T u , because the adjustment rates of the same type of equipment are similar, take the average of the adjustable load adjustment rates v1 of each to calculate the aggregation unit adjustment rate v u and record it in the aggregation unit file information table;

[0037] Compare the adjustable load adjustment duration T1 with the aggregation unit adjustment duration T u , and if T1 is greater than T u , all the adjustable loads are fully involved in the adjustment, and the adjustment capacity is recorded as Substitute the aggregation unit adjustment duration T u into Q = P * T to calculate the adjustment power If T1 is less than T u , the adjustable loads are adjusted according to the power sharing, and the adjustment capacity is recorded as Substitute it into Q = P * T to calculate the adjustment power Calculate the aggregation unit adjustment capacity P u and the aggregation unit adjustment deviation rate d u respectively according to the following formula and record them in the aggregation unit file information table:

[0038]

[0039] Through the above technical solution, key parameters such as the regulation capacity, regulation duration, regulation deviation rate, and regulation rate of the adjustable load are obtained from the aggregated unit file information table and the equipment file information table, providing a data basis for the calculation of the regulation capacity of the aggregated unit; by comparing the regulation duration of the adjustable load with the required regulation sustainable time in the virtual power plant regulation scenario, the equipment participating in the regulation in full and the equipment sharing the regulation according to the electricity quantity are distinguished, ensuring the reasonable distribution of the regulation capacity and the regulated electricity quantity; by calculating the weighted regulation capacity and regulation deviation rate of the aggregated unit, the regulation capacity and regulation accuracy of different types of equipment are comprehensively considered, realizing the scientific evaluation of the regulation capacity of the aggregated unit; finally, the calculation results are entered into the aggregated unit file information table, providing reliable data support for the overall regulation capacity evaluation and optimal dispatching of the virtual power plant.

[0040] Preferably, the calculation of the virtual power plant aggregated regulation capacity index further includes:

[0041] Obtain the primary key ID of the virtual power plant from the virtual power plant file information table, and obtain the corresponding aggregated unit information from the aggregated unit file information table according to the primary key ID of the virtual power plant, including the aggregated unit regulation capacity P u 、the aggregated unit regulation duration T u 、the aggregated unit regulation deviation rate d u and the aggregated unit regulation rate v u ;

[0042] Since the regulation durations T u of each aggregated unit within the virtual power plant are the same, use it as the virtual power plant regulation duration T vpp , accumulate and calculate the virtual power plant regulation capacity P u by adding up the regulation capacities P vpp of each virtual power plant, select the minimum value of the aggregated unit regulation rate v u as the virtual power plant regulation rate v vpp , and enter them into the virtual power plant file information table respectively;

[0043] Substitute the aggregated unit regulation capacity P u and the aggregated unit regulation duration T u into the formula Q = P * T to calculate the aggregated unit regulation electricity quantity Q u , calculate the virtual power plant regulation deviation rate d vpp according to the following formula and enter it into the virtual power plant file information table:

[0044]

[0045] Through the above technical solution, key parameters such as the regulation capacity, regulation duration, regulation deviation rate, and regulation rate of each aggregation unit are obtained from the virtual power plant file information table and the aggregation unit file information table, providing a data basis for the calculation of the overall regulation capacity of the virtual power plant; by accumulating the regulation capacities of each aggregation unit and selecting the minimum regulation rate, the scientificity and reliability of the virtual power plant's regulation capacity and regulation rate are ensured, reflecting the upper limit and response speed of the virtual power plant's overall regulation ability; by calculating the regulation deviation rate of the virtual power plant through weighting, the regulation accuracy and regulated power of each aggregation unit are comprehensively considered, realizing an accurate assessment of the overall regulation accuracy of the virtual power plant; finally, the calculation results are entered into the virtual power plant file information table, providing comprehensive data support for the virtual power plant to participate in scenarios such as power grid regulation and demand response.

[0046] A virtual power plant multi-agent aggregation regulation ability quantitative evaluation system according to the present invention includes:

[0047] An index system establishment module for extracting common parameters and establishing a virtual power plant multi-agent regulation ability index system, where the virtual power plant multi-agent regulation ability index system includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate;

[0048] A data model construction module for constructing a three-level regulation ability data model of virtual power plant equipment-aggregation unit-virtual power plant based on the regulation ability index system. The regulation ability data model includes an equipment model, an aggregation unit model, and a virtual power plant model. The aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to the resource type. The equipment model includes an equipment file information table and an equipment operation information table. The aggregation unit model includes an aggregation unit file information table. The virtual power plant model includes a virtual power plant file information table;

[0049] A data collection and entry module for collecting the adjustable load resource data under the jurisdiction of the virtual power plant and entering the adjustable load resource data into the regulation ability data model;

[0050] A regulation ability calculation module for calculating the virtual power plant multi-agent regulation ability index and the virtual power plant aggregation regulation ability index;

[0051] A regulation ability evaluation module for establishing a virtual power plant regulation ability evaluation model, quantitatively scoring the regulation ability of the virtual power plant, and generating an evaluation report.

[0052] Preferably, the data collection and entry module is specifically used for:

[0053] Obtain the basic file data of adjustable load resources, and enter it into the equipment file information table. The basic file data of adjustable load resources includes the first equipment primary key ID, the virtual power plant ID to which it belongs, and the equipment type; obtain the basic file data of the virtual power plant, and enter it into the virtual power plant file information table. The basic file data of the virtual power plant includes the virtual power plant primary key ID, the virtual power plant name, and the installed capacity; based on the equipment type, create corresponding basic file data of the aggregation unit in the aggregation unit file information table. The basic file data of the aggregation unit includes the aggregation unit primary key ID, the aggregation unit name, and the virtual power plant ID to which it belongs; add the aggregation unit primary key ID to the aggregation unit ID field of the equipment file information table to form an "equipment-aggregation unit-virtual power plant" adjustable load resource aggregation hierarchy; obtain the adjustable load operation data and enter it into the equipment operation information table. The adjustable load operation data includes the second equipment primary key ID, the operation load data at each moment, and the baseline load data, and the first equipment primary key ID is the same as the second equipment primary key ID;

[0054] The multi-agent regulation ability calculation module is specifically used for:

[0055] Obtain the adjustable load regulation target value P 目标 , set the coefficient k, and set k*P 目标 as the adjustable load regulation effective threshold value P 门槛 ; obtain and record the load regulation response value at each moment and the corresponding baseline load and moment; screen out the load regulation response values {P1 P2…P 门槛} greater than P n} and the corresponding moments {T1 T2…T n}, calculate the regulation duration T 调节 =T n -T1 and enter it into the equipment file information table; calculate the regulation response mean value P n of the load regulation response values {P1 P2…P 响应}, obtain the baseline load values at each moment {T1 T2…T n} and calculate their mean value P 基线 , calculate the regulation capacity P 调节 =|P 响应 -P 基线 | and enter it into the equipment file information table; based on the regulation target value and the regulation capacity, calculate the regulation deviation rate d 调节 and enter it into the equipment file information table. The calculation formula is as follows:

[0056]

[0057] Obtain the moment T 门槛 when the first regulation reaches P 门槛 , the moment T 指令 when the regulation instruction is issuedand its corresponding instruction load value P 指令 , calculate the adjustment rate v 调节 and record it in the equipment file information table. The calculation formula is as follows:

[0058]

[0059] The specific function of the aggregation adjustment ability calculation module is as follows:

[0060] Obtain the primary key ID of each aggregation unit from the aggregation unit file information table, and obtain the corresponding adjustable load information from the equipment file information table according to the primary key ID of each aggregation unit, including the adjustable load adjustment capacity P1, the adjustable load adjustment duration T1, the adjustable load adjustment deviation rate d1, and the adjustable load adjustment rate v1; obtain the adjustment sustainable time required for the virtual power plant to participate in the power grid regulation scenario as the aggregation unit adjustment duration T u , because the adjustment rates of the same type of equipment are similar, average the adjustable load adjustment rates v1 of each to calculate the aggregation unit adjustment rate v u and record it in the aggregation unit file information table; compare the adjustable load adjustment duration T1 with the aggregation unit adjustment duration T u , and if T1 is greater than T u , all adjustable loads will participate in the adjustment, and the adjustment capacity is recorded as Substitute the aggregation unit adjustment duration T u into Q = P * T to calculate the adjustment power , if T1 is less than T u , the adjustable loads will be adjusted according to the power sharing, and the adjustment capacity is recorded as Substitute it into Q = P * T to calculate the adjustment power Calculate the aggregation unit adjustment capacity P u and the aggregation unit adjustment deviation rate d u respectively according to the following formula and record them in the aggregation unit file information table:

[0061]

[0062] Obtain the virtual power plant primary key ID from the virtual power plant file information table, and obtain the corresponding aggregation unit information from the aggregation unit file information table according to the virtual power plant primary key ID, including the aggregation unit adjustment capacity P u , the aggregation unit adjustment duration T u , the aggregation unit adjustment deviation rate d u and the aggregation unit adjustment rate v u ; because the adjustment durations T u of each aggregation unit within the virtual power plant are the same, use it as the virtual power plant adjustment duration T vpp , and for each virtual power plant adjustment capacity P uAccumulatively calculate the regulation capacity P of the virtual power plant vpp , select the regulation rate v of the aggregation unit u , and take the minimum value as the regulation rate v of the virtual power plant vpp , and enter them into the virtual power plant file information table respectively; bring the regulation capacity P u and the regulation duration T of the aggregation unit u into the formula Q = P * T to calculate the regulation power Q of the aggregation unit u , calculate the regulation deviation rate d of the virtual power plant according to the following formula vpp and enter it into the virtual power plant file information table:

[0063]

[0064] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for quantitatively evaluating the aggregation regulation ability of multiple entities in a virtual power plant according to any one of claims 1 to 7.

[0065] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: 1. Considering the different regulation characteristics of multiple entities inside the virtual power plant can improve the accuracy of the evaluation results and accurately reflect the actual regulation ability of the virtual power plant; 2. By constructing a three-level regulation ability data model of "equipment - aggregation unit - virtual power plant" for the virtual power plant, the transfer and aggregation relationship of the regulation ability can be clearly reflected, and the structured description of the complex regulation ability inside the virtual power plant can be realized; 3. By collecting the adjustable load resource data in real time and entering it into the regulation ability data model, the dynamic update of the regulation ability data model is realized, ensuring the accuracy and timeliness of the evaluation results; 4. The aggregation calculation method of the regulation ability index realizes the step-by-step aggregation calculation of the regulation ability from the equipment level to the aggregation unit level and then to the virtual power plant level, solving the aggregation problem caused by the difference in the regulation ability of different types of equipment. Description of the Drawings

[0066] Figure 1 is a schematic flow chart of the present invention. Detailed Embodiments

[0067] The technical solution of the present invention will be further described below with reference to the drawings.

[0068] The embodiment of the present application discloses a method for quantitatively evaluating the aggregation regulation ability of multiple entities in a virtual power plant, and this method is applied to a system for quantitatively evaluating the aggregation regulation ability of multiple entities in a virtual power plant.

[0069] As Figure 1 shown, this method includes:

[0070] Based on the operating characteristics and regulation characteristics of various adjustable loads in a virtual power plant, extract common parameters and establish an index system for the regulation capabilities of multiple entities in the virtual power plant.

[0071] Specifically, extract key common parameters from the operating characteristics and regulation characteristics of various adjustable loads. These parameters can reflect the regulation capabilities of the equipment. Based on the extracted common parameters, construct a multi-dimensional and multi-level index system to quantitatively evaluate the regulation capabilities of each entity within the virtual power plant.

[0072] Based on the regulation capability index system, construct a three-level regulation capability data model for the virtual power plant's equipment - aggregation unit - virtual power plant to describe the aggregation relationship of adjustable load resources among the equipment, aggregation unit, and virtual power plant levels.

[0073] Specifically, the equipment level records the operating and regulation characteristics of individual adjustable loads. The aggregation unit level classifies and aggregates similar equipment to form a resource set. The virtual power plant level integrates all aggregation units to form the overall regulation capability of the virtual power plant.

[0074] Collect the data of adjustable load resources under the jurisdiction of the virtual power plant and enter the adjustable load resource data into the corresponding information table of the regulation capability data model.

[0075] Specifically, first, it is necessary to collect relevant data on the adjustable load resources (i.e., electrical loads that can be adjusted according to grid demand, such as industrial equipment, energy storage systems, etc.) managed by the virtual power plant. Then, enter the collected adjustable load resource data into the regulation capability data model. The regulation capability data model can be a tool for analyzing and predicting the regulation capabilities of these resources in the power grid. Through this model, it is possible to better manage and optimize the allocation and use of power resources, thereby improving the stability and efficiency of the power grid.

[0076] Calculate the regulation capability indexes of multiple entities in the virtual power plant and enter them into the database.

[0077] Specifically, calculating the regulation capability indexes of multiple entities means quantitatively analyzing the regulation potential of these participants. These indexes reflect the contribution capabilities of each entity in power grid balance and stability. Once these indexes are calculated, they need to be entered into the database for data management, query, and analysis, providing data support for the operation decision-making of the virtual power plant to ensure the efficient and stable operation of the power grid.

[0078] Calculate the aggregated regulation capability index of the virtual power plant and enter it into the database.

[0079] Specifically, calculating the aggregated regulation capacity index refers to quantitatively evaluating the overall regulation capacity of the virtual power plant, and these indexes reflect the comprehensive capacity of the virtual power plant in aspects such as power grid frequency modulation and peak regulation. After the calculation is completed, these indexes are entered into the database for data storage, retrieval, and analysis.

[0080] Based on the regulation capacity data model, the regulation capacity indexes of multiple entities, and the aggregated regulation capacity index, a virtual power plant regulation capacity evaluation model is established to quantitatively score the regulation capacity of the virtual power plant and generate an evaluation report.

[0081] Based on the method for quantitatively evaluating the aggregated regulation capacity of multiple entities in a virtual power plant, by extracting the common parameters of various adjustable loads, a multi-dimensional and multi-level index system is constructed, enabling the regulation capacities of each entity within the virtual power plant to be systematically and standardly quantitatively evaluated, providing a basis for subsequent analysis and optimization. Through the three-level model of "equipment - aggregation unit - virtual power plant", it helps to better understand and analyze the regulation capacity of the virtual power plant, and the regulation capacity from individual equipment to the overall virtual power plant can be effectively managed. By collecting and entering the data of adjustable load resources, the regulation capacity data model can be updated in real time and reflect the current state of the virtual power plant, providing accurate data support for subsequent regulation capacity calculation and analysis. Through the quantitative analysis of the regulation capacities of multiple entities, the contribution capacities of each entity in power grid balance and stability can be accurately evaluated, and these indexes are entered into the database for data management, query, and analysis, providing a scientific basis for the operation decision-making of the virtual power plant. Through the quantitative evaluation of the overall regulation capacity of the virtual power plant, the comprehensive capacity of the virtual power plant in aspects such as power grid frequency modulation and peak regulation can be comprehensively understood, and the entry and storage of these indexes provide data support for the efficient and stable operation of the power grid.

[0082] In one embodiment, in order to scientifically characterize the differential regulation characteristics of multiple entities, the virtual power plant multiple entity regulation capacity index system includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate.

[0083] Regulation capacity refers to the maximum regulation ability that a certain entity can provide, usually expressed in power units (such as kilowatts or megawatts). It reflects the amount of power regulation that the entity can provide when the power grid demand changes. For example, the amount of electricity that an energy storage system can store or release, or the amount of electricity consumption that an industrial device can adjust; Regulation duration refers to the length of time that a certain entity can continuously provide regulation ability, usually measured in hours or minutes. It reflects the duration that the entity can maintain the regulation ability when the power grid demand changes. For example, the time that an energy storage system can continuously discharge, or the time that an adjustable load can maintain the adjusted state; Regulation deviation rate refers to the deviation ratio between the actual regulation effect and the expected regulation target, usually expressed as a percentage. It reflects the accuracy and reliability of the entity during the regulation process. For example, if a certain device fails to fully reach the expected target during the regulation process, the deviation rate can quantify this difference; Regulation rate refers to the speed at which a certain entity can respond to the change in power grid demand, usually expressed as a power change rate (such as kilowatts per second or megawatts per minute). It reflects the rapid response ability of the entity when the power grid demand changes. For example, how quickly an energy storage system can switch from the charging state to the discharging state.

[0084] Through the four indicators in this embodiment, the virtual power plant can clearly identify the differences in the regulation characteristics of different entities. For example: The energy storage system may have a high regulation rate and regulation capacity, but the regulation duration is limited; Industrial equipment may have a long regulation duration, but the regulation rate is slow; This differential analysis provides a scientific basis for the resource optimization scheduling of the virtual power plant, and can reasonably allocate resources according to different scenario requirements; Regulation capacity and regulation duration help the virtual power plant provide sufficient regulation ability during power grid load fluctuations, avoiding power shortages or surpluses; The regulation deviation rate ensures the accuracy of the regulation process, reduces the uncertainty in power grid operation, and improves the reliability of the power grid; The regulation rate enables the virtual power plant to quickly respond to sudden changes in power grid demand and enhance the dynamic stability of the power grid.

[0085] In one embodiment, to clearly describe the aggregation relationship of adjustable load resources among different levels, the regulation capacity data model includes a device model, an aggregation unit model, and a virtual power plant model. The aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to the resource type. The device model includes a device profile information table, a device operation information table, and a device detection information table. The aggregation unit model includes an aggregation unit profile information table, and the virtual power plant model includes a virtual power plant profile information table. Among them, the virtual power plant profile information table includes a primary key ID, name, installed capacity, regulation capacity, regulation duration, regulation deviation rate, and regulation rate; the aggregation unit profile information table includes a primary key ID, the ID of the virtual power plant to which it belongs, name, installed capacity, regulation capacity, regulation duration, regulation deviation rate, and regulation rate; the device profile information table includes a primary key ID, the ID of the virtual power plant to which it belongs, the ID of the aggregation unit to which it belongs, installed capacity, regulation capacity, regulation duration, regulation deviation rate, and regulation rate. The device operation information table includes a primary key ID, time, operating load, and baseline load. The device detection information table includes a primary key ID, regulation start time, regulation end time, and regulation target value. Among them, the ID of the aggregation unit to which the device belongs in the device profile information table is consistent with the primary key of the aggregation unit profile information table, and the primary keys of the device profile information table, the device operation information table, and the device detection information table are consistent.

[0086] By constructing a three-level regulation capacity data model including a device model, an aggregation unit model, and a virtual power plant model in this embodiment, the aggregation relationship of adjustable load resources among different levels is clearly described, and the all-round data management from devices to aggregation units to virtual power plants is realized by using the device profile information table, the device operation information table, the device detection information table, the aggregation unit profile information table, and the virtual power plant profile information table. This design ensures the consistency and integrity of data through the association of the primary key ID, and at the same time provides a refined description and quantitative analysis of the regulation capacity of adjustable load resources through hierarchical storage and classification aggregation.

[0087] In one embodiment, to ensure the integrity and consistency of data, collecting the adjustable load resource data under the virtual power plant and entering the adjustable load resource data into the regulation capacity data model can be specifically implemented as follows: obtaining the basic file data of the adjustable load resource and entering it into the equipment file information table. The basic file data of the adjustable load resource includes the first equipment primary key ID, the ID of the affiliated virtual power plant, and the equipment type; obtaining the basic file data of the virtual power plant and entering it into the virtual power plant file information table. The basic file data of the virtual power plant includes the virtual power plant primary key ID, the name of the virtual power plant, and the installed capacity; based on the equipment type, creating corresponding basic file data of the aggregation unit in the aggregation unit file information table. The basic file data of the aggregation unit includes the aggregation unit primary key ID, the name of the aggregation unit, and the ID of the affiliated virtual power plant; adding the aggregation unit primary key ID to the affiliated aggregation unit ID field of the equipment file information table to form an "equipment - aggregation unit - virtual power plant" adjustable load resource aggregation hierarchy relationship; obtaining the adjustable load operation data and entering it into the equipment operation information table. The adjustable load operation data includes the second equipment primary key ID, the operation load data at each moment, and the baseline load data, and the first equipment primary key ID is consistent with the second equipment primary key ID.

[0088] Through the systematic data collection and entry process of this embodiment, the integrity and consistency of the adjustable load resource data under the virtual power plant are ensured. Specifically, it includes obtaining and entering the equipment file information, the virtual power plant file information, and the aggregation unit file information, and constructing a three - level aggregation relationship of "equipment - aggregation unit - virtual power plant" through the association of the primary key IDs. At the same time, the adjustable load operation data is entered into the equipment operation information table; realizing the structured management and hierarchical integration of the adjustable load resource data under the virtual power plant, ensuring the consistency and traceability of the data, and providing a reliable data basis for the regulation capacity analysis, resource optimal scheduling, and power grid stable operation of the virtual power plant.

[0089] In one embodiment, to comprehensively reflect the regulation performance of the adjustable load resource, calculating the multi - entity regulation capacity index of the virtual power plant and entering it into the database can be specifically implemented as follows: obtaining the adjustable load regulation target value P 目标 , setting the coefficient k, and setting k*P 目标 as the adjustable load regulation effective threshold value P 门槛 ; obtaining and recording the load regulation response value at each moment and its corresponding baseline load and time; screening out the load regulation response values {P1 P2 … P 门槛} greater than P n} and the corresponding times {T1 T2 … T n}, calculating the regulation duration T 调节 = T n - T1 and entering it into the equipment file information table; calculating the regulation response mean value P of the load regulation response values {P1 P2 … P n}响应 , obtain the baseline load values at each moment {T1, T2, …, T n} and calculate their average value P 基线 , calculate the regulation capacity P 调节} = |P 响应 - P 基线 | and record it in the equipment file information table; based on the regulation target value and the regulation capacity, calculate the regulation deviation rate and record it in the equipment file information table; obtain the moment T 门槛 when the first regulation reaches P 门槛 , the moment T 指令 when the regulation instruction is issued and its corresponding instruction load value P 指令 , calculate the regulation rate and record it in the equipment file information table.

[0090] Through this embodiment, the regulation target value, regulation response value and baseline load data of the adjustable load are obtained. Combining with the set regulation effective threshold value, key indicators such as regulation duration, regulation capacity, regulation deviation rate and regulation rate are calculated, and these indicators are recorded in the equipment file information table, so as to comprehensively reflect the regulation performance of the adjustable load resource; realizing the refined quantitative evaluation of the regulation ability of the adjustable load resource, providing scientific and accurate data support for the resource optimal dispatching, grid stable operation and efficient decision-making of the virtual power plant, and at the same time improving the systematicness and operability of the regulation ability management of the virtual power plant.

[0091] In one embodiment, in order to scientifically evaluate the regulation ability of the aggregation unit, calculating the virtual power plant aggregation regulation ability index and recording it in the database can be specifically executed as follows: obtain the primary key ID of each aggregation unit from the aggregation unit file information table, and obtain the corresponding adjustable load information from the equipment file information table according to the primary key ID of each aggregation unit, including the adjustable load regulation capacity P1, the adjustable load regulation duration T1, the adjustable load regulation deviation rate d1 and the adjustable load regulation rate v1; obtain the regulation sustainable time required for the virtual power plant to participate in the power grid regulation scenario as the aggregation unit regulation duration T u . Since the regulation rates of the same type of equipment are similar, take the average of the adjustable load regulation rates v1 of each to calculate the aggregation unit regulation rate v u and record it in the aggregation unit file information table; compare the adjustable load regulation duration T1 with the aggregation unit regulation duration T u , and let the adjustable loads with T1 greater than T u participate in the regulation in full, and record the regulation capacity as Substitute the aggregation unit regulation duration T u into Q =}P * T to calculate the regulation power Let the adjustable loads with T1 less than T u participate in the regulation according to the power sharing, and record the regulation capacity as Calculate the regulated power by substituting Q = P * T Calculate the regulation capacity P of the aggregation unit respectively according to the following formula u and the regulation deviation rate d of the aggregation unit u and record them in the aggregation unit file information table

[0092]

[0093] Obtain the virtual power plant primary key ID from the virtual power plant file information table, and obtain the corresponding aggregation unit information from the aggregation unit file information table according to the virtual power plant primary key ID, including the aggregation unit regulation capacity P u the regulation duration T of the aggregation unit u the regulation deviation rate d of the aggregation unit u and the regulation rate v of the aggregation unit u ; Since the regulation duration T of each aggregation unit within the virtual power plant u is the same, use it as the virtual power plant regulation duration T vpp , sum up the regulation capacities P of each virtual power plant u to calculate the virtual power plant regulation capacity P vpp , select the minimum value of the regulation rate v of the aggregation unit u as the virtual power plant regulation rate v vpp , and record them in the virtual power plant file information table respectively; Substitute the aggregation unit regulation capacity P u and the aggregation unit regulation duration T u into the formula Q = P * T to calculate the aggregation unit regulation power Q u , calculate the virtual power plant regulation deviation rate d according to the following formula vpp and record it in the virtual power plant file information table

[0094]

[0095] Through this embodiment, the adjustable load and the regulation capacity data of the aggregation unit are extracted from the equipment file information table and the aggregation unit file information table. Combining with the regulation requirements of the virtual power plant, the key indicators such as the regulation capacity, regulation duration, regulation deviation rate and regulation rate of the aggregation unit and the virtual power plant are systematically calculated, and these indicators are recorded in the database; realizing a multi-level and refined evaluation of the regulation ability of the virtual power plant, providing a reliable data basis for power grid dispatching, optimizing resource allocation and operation efficiency at the same time, and enhancing the response ability and stability of the virtual power plant in scenarios such as power grid frequency modulation and peak shaving.

[0096] Figure 1 is a schematic flow chart of the method for quantitatively evaluating the aggregation regulation ability of multiple subjects of a virtual power plant in an embodiment. It should be understood that although Figure 1Each step in the flowchart is displayed in sequence according to the arrow indication, but these steps are not necessarily executed in the order indicated by the arrows; unless otherwise explicitly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders; and Figure 1 at least a part of the steps may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0097] Based on the above method, an embodiment of the present application also discloses a virtual power plant multi-agent aggregation regulation ability quantification and evaluation system, which includes the following modules:

[0098] An index system establishment module, configured to extract common parameters and establish a virtual power plant multi-agent regulation ability index system, where the virtual power plant multi-agent regulation ability index system includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate;

[0099] A data model construction module, configured to construct a three-level regulation ability data model of virtual power plant equipment-aggregation unit-virtual power plant based on the regulation ability index system, where the regulation ability data model includes an equipment model, an aggregation unit model, and a virtual power plant model, the aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to the resource type, the equipment model includes an equipment file information table and an equipment operation information table, the aggregation unit model includes an aggregation unit file information table, and the virtual power plant model includes a virtual power plant file information table;

[0100] A data collection and entry module, configured to collect adjustable load resource data under the virtual power plant and enter the adjustable load resource data into the information table corresponding to the regulation ability data model;

[0101] A regulation ability calculation module, configured to calculate the virtual power plant multi-agent regulation ability index and the virtual power plant aggregation regulation ability index;

[0102] A regulation ability evaluation module, configured to establish a virtual power plant regulation ability evaluation model, quantitatively score the regulation ability of the virtual power plant, and generate an evaluation report.

[0103] An embodiment of the present application also discloses a computer-readable storage medium.

[0104] Specifically, a computer-readable storage medium is used to store a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented. Those skilled in the art can understand that all or part of the processes in the above method embodiments of the present application can be completed by instructing relevant hardware through a computer program. This program can be stored in a computer-readable storage medium. When this program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0105] This specific embodiment is only an interpretation of the present invention and does not limit the present invention. After reading this specification, those skilled in the art can make modifications to this embodiment that do not contribute creatively according to needs, but as long as it is within the scope of the claims of the present invention, it is protected by the patent law.

Claims

1. A method for quantitatively evaluating the aggregation and regulation capabilities of multiple entities in a virtual power plant, characterized in that, Including: Based on the operation characteristics and regulation characteristics of various adjustable loads in a virtual power plant, extracting common parameters to establish an index system for the regulation capabilities of multiple entities in the virtual power plant; Based on the regulation capability index system, constructing a three-level regulation capability data model of virtual power plant equipment - aggregation unit - virtual power plant, which is used to describe the aggregation relationship of adjustable load resources among the equipment, aggregation unit, and virtual power plant levels; Collecting the data of adjustable load resources under the jurisdiction of the virtual power plant and entering the adjustable load resource data into the information table corresponding to the regulation capability data model; Calculating the regulation capability indexes of multiple entities in the virtual power plant and the aggregated regulation capability index of the virtual power plant; Based on the regulation capability data model, the regulation capability indexes of multiple entities, and the aggregated regulation capability index, establishing a regulation capability evaluation model for the virtual power plant, quantitatively scoring the regulation capability of the virtual power plant, and generating an evaluation report.

2. The method for quantitatively evaluating the aggregation and regulation ability of multiple entities in a virtual power plant according to claim 1, wherein The regulation capability index system of multiple entities in the virtual power plant includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate.

3. The method for quantitatively evaluating the aggregation and regulation ability of multiple entities in a virtual power plant according to claim 1, wherein The regulation capability data model includes an equipment model, an aggregation unit model, and a virtual power plant model. The aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to resource types. The equipment model includes an equipment file information table and an equipment operation information table. The aggregation unit model includes an aggregation unit file information table. The virtual power plant model includes a virtual power plant file information table.

4. The method for quantitatively evaluating the aggregation regulation ability of multiple entities in a virtual power plant according to claim 3, wherein The collecting the data of adjustable load resources under the jurisdiction of the virtual power plant and entering the adjustable load resource data into the information table corresponding to the regulation capability data model includes: Obtaining the basic file data of the adjustable load resources and entering it into the equipment file information table. The basic file data of the adjustable load resources includes the first equipment primary key ID, the ID of the virtual power plant to which it belongs, and the equipment type; Obtaining the basic file data of the virtual power plant and entering it into the virtual power plant file information table. The basic file data of the virtual power plant includes the virtual power plant primary key ID, the name of the virtual power plant, and the installed capacity; Based on the equipment type, creating corresponding basic file data of the aggregation unit in the aggregation unit file information table. The basic file data of the aggregation unit includes the aggregation unit primary key ID, the name of the aggregation unit, and the ID of the virtual power plant to which it belongs; Adding the aggregation unit primary key ID to the aggregation unit ID field of the equipment file information table to form an "equipment - aggregation unit - virtual power plant" adjustable load resource aggregation hierarchy relationship; Obtaining the adjustable load operation data and entering it into the equipment operation information table. The adjustable load operation data includes the second equipment primary key ID, the operation load data at each moment, and the baseline load data. The first equipment primary key ID is the same as the second equipment primary key ID.

5. The method for quantitatively evaluating the aggregation regulation ability of multiple subjects in a virtual power plant according to claim 1, wherein The calculating the regulation capability indexes of multiple entities in the virtual power plant includes: Obtain the adjustable load regulation target value P 目标 , set the coefficient k, and take k*P 目标 as the adjustable load regulation effective threshold value P 门槛 ; Obtaining and recording the load regulation response values at each moment, as well as the corresponding baseline load and time; Filter out the load regulation response values greater than P 门槛 {P1, P2, …, P n} and the corresponding moments {T1, T2, …, T n}, calculate the regulation duration T 调节 = T n - T1 and enter it into the equipment file information table; Calculate the average regulation response value {P1P2…P n} of the regulation response as P 响应 , obtain the baseline load values at each moment {T1T2…T n} and calculate their average value as P 基线 , calculate the regulation capacity P 调节} = |P 响应 - P 基线 | and record it in the equipment file information table; Calculate the regulation deviation rate d based on the regulation target value and regulation capacity 调节 And enter it into the equipment file information table. The calculation formula is as follows: Get the first adjustment to P 门槛 Time T 门槛 , Adjustment instruction issuance time T 指令 And its corresponding instruction load value P 指令 , calculate the adjustment rate v 调节 And enter the equipment file information table, the calculation formula is as follows:

6. The method for quantitatively evaluating the aggregation and regulation ability of multiple entities in a virtual power plant according to claim 1, wherein The calculating the aggregated regulation capability index of the virtual power plant includes: Obtain the primary key IDs of each aggregation unit from the aggregation unit file information table, and obtain the corresponding adjustable load information from the device file information table according to the primary key IDs of each aggregation unit, including the adjustable load regulation capacity P1, the adjustable load regulation duration T1, the adjustable load regulation deviation rate d1, and the adjustable load regulation rate v1; Obtain the adjustable sustainable time required for the virtual power plant to participate in the power grid regulation scenario as the adjustable duration T of the aggregation unit u , because the regulation rates of the same type of equipment are similar, the regulation rates v1 of each adjustable load are averaged to calculate the regulation rate v of the aggregation unit u And enter it into the aggregation unit file information table; Compare the adjustable load regulation duration T1 with the aggregation unit regulation duration T u , and all the adjustable loads with T1 greater than T u participate in the regulation in full, and the regulation capacity is denoted as Substitute the aggregation unit regulation duration T u into Q = P * T to calculate the regulation power For the adjustable loads with T1 less than T u , perform regulation by sharing the power, and the regulation capacity is denoted as Substitute into Q = P * T to calculate the regulation power Calculate the aggregation unit regulation capacity P u and the aggregation unit regulation deviation rate d u according to the following formulas respectively, and record them in the aggregation unit file information table:

7. The method for quantitatively evaluating the aggregation regulation ability of multiple entities in a virtual power plant according to claim 1, characterized in that, The calculation of the virtual power plant aggregation regulation ability index further includes: Obtain the virtual power plant primary key ID from the virtual power plant archive information table, and obtain the corresponding aggregation unit information from the aggregation unit archive information table according to the virtual power plant primary key ID, including the regulation capacity P of the aggregation unit u , the regulation duration T of the aggregation unit u , the regulation deviation rate d of the aggregation unit u and the regulation rate v of the aggregation unit u ; Since the regulation duration T of each aggregation unit within the virtual power plant is u the same, it is used as the regulation duration T of the virtual power plant vpp . The regulation capacity P of each virtual power plant is u accumulated to calculate the regulation capacity P of the virtual power plant vpp . The minimum value of the regulation rate v of the aggregation unit is selected as u the regulation rate v of the virtual power plant vpp and entered into the virtual power plant file information table respectively; Adjust the capacity P of the aggregation unit u and the adjustment duration T of the aggregation unit u into the formula Q = P * T to calculate the adjustment power Q of the aggregation unit u , calculate the regulation deviation rate d of the virtual power plant according to the following formula vpp and record it in the virtual power plant file information table:

8. A quantization evaluation system for the aggregation regulation ability of multiple subjects in a virtual power plant, characterized in that, including: An index system establishment module, which is used to extract common parameters and establish a virtual power plant multi-agent regulation ability index system. The virtual power plant multi-agent regulation ability index system includes regulation capacity, regulation duration, regulation deviation rate, and regulation rate; A data model construction module, which is used to construct a three-level regulation ability data model of virtual power plant equipment-aggregation unit-virtual power plant based on the regulation ability index system. The regulation ability data model includes an equipment model, an aggregation unit model, and a virtual power plant model. The aggregation unit model is a model formed by classifying and aggregating the adjustable load resources of the virtual power plant according to the resource type. The equipment model includes an equipment file information table and an equipment operation information table. The aggregation unit model includes an aggregation unit file information table. The virtual power plant model includes a virtual power plant file information table; A data collection and entry module, which is used to collect the adjustable load resource data under the virtual power plant and enter the adjustable load resource data into the corresponding information table of the regulation ability data model; A regulation ability calculation module, which is used to calculate the virtual power plant multi-agent regulation ability index and the virtual power plant aggregation regulation ability index; A regulation ability evaluation module, which is used to establish a virtual power plant regulation ability evaluation model, quantitatively score the regulation ability of the virtual power plant, and generate an evaluation report.

9. The virtual power plant multi-agent aggregation regulation ability quantification and evaluation system according to claim 8, wherein The data collection and entry module is specifically used for: Obtain the basic file data of the adjustable load resource and enter it into the equipment file information table. The basic file data of the adjustable load resource includes the first equipment primary key ID, the affiliated virtual power plant ID, and the equipment type; Obtain the basic file data of the virtual power plant and enter it into the virtual power plant file information table. The basic file data of the virtual power plant includes the virtual power plant primary key ID, the virtual power plant name, and the installed capacity; Based on the equipment type, create the corresponding aggregation unit basic file data in the aggregation unit file information table. The aggregation unit basic file data includes the aggregation unit primary key ID, the aggregation unit name, and the affiliated virtual power plant ID; Add the aggregation unit primary key ID to the affiliated aggregation unit ID field of the equipment file information table to form an "equipment-aggregation unit-virtual power plant" adjustable load resource aggregation hierarchy; Obtain the adjustable load operation data and enter it into the equipment operation information table. The adjustable load operation data includes the second equipment primary key ID, the operation load data at each moment, and the baseline load data. The first equipment primary key ID is the same as the second equipment primary key ID; The multi-agent regulation ability calculation module is specifically used for: Obtain the adjustable load regulation target value P 目标 , set the coefficient k, and set k*P 目标 as the effective threshold value P for the adjustable load regulation 门槛 ; Obtain and record the load regulation response values at each moment, as well as the corresponding baseline loads and moments; Screen out the load regulation response values {P1, P2, …, P 门槛} greater than P n} and the corresponding moments {T1, T2, …, T n}, calculate the regulation duration T 调节 = T n - T1 and record it in the equipment file information table; Calculate the average regulation response P n of the load regulation response values {P1, P2, …, P 响应}, obtain the baseline load values at each moment {T1, T2, …, T n} and calculate their average P 基线 , calculate the regulation capacity P 调节 = |P 响应 - P 基线 | and record it in the equipment file information table; Calculate the adjustment deviation rate d based on the adjustment target value and adjustment capacity 调节 And enter it into the equipment file information table, and the calculation formula is as follows: Obtain the moment T when adjusted to P for the first time 门槛 , the moment T when the adjustment instruction is issued 门槛 and its corresponding instruction load value P 指令 , calculate the adjustment rate v 指令 调节 and record it in the equipment file information table. The calculation formula is as follows:​ The aggregation regulation ability calculation module is specifically used for: Obtain the primary key IDs of each aggregation unit from the aggregation unit file information table, and obtain the corresponding adjustable load information from the device file information table according to the primary key IDs of each aggregation unit, including the adjustable load regulation capacity P1, the adjustable load regulation duration T1, the adjustable load regulation deviation rate d1, and the adjustable load regulation rate v1; obtain the regulation sustainable time required for the virtual power plant to participate in the power grid regulation scenario as the aggregation unit regulation duration T u , since the regulation rates of devices of the same type are similar, average the adjustable load regulation rates v1 of each to calculate the aggregation unit regulation rate v u And record it in the aggregation unit file information table; compare the adjustable load regulation duration T1 with the aggregation unit regulation duration T u , for the adjustable load where T1 is greater than T u , all of it participates in the regulation, and the regulation capacity is recorded as Substitute the aggregation unit regulation duration T u into Q = P * T to calculate the regulation power , for the adjustable load where T1 is less than T u , adjust it according to the power sharing, and the regulation capacity is recorded as Substitute it into Q = P * T to calculate the regulation power Calculate the aggregation unit regulation capacity P u and the aggregation unit regulation deviation rate d u respectively according to the following formula and record them in the aggregation unit file information table: Obtain the virtual power plant primary key ID from the virtual power plant archive information table, and obtain the corresponding aggregation unit information from the aggregation unit archive information table according to the virtual power plant primary key ID, including the regulation capacity P of the aggregation unit u , the regulation duration T of the aggregation unit u , the regulation deviation rate d of the aggregation unit u and the regulation rate v of the aggregation unit u ; Since the regulation durations T of the aggregation units within the virtual power plant u are the same, use it as the regulation duration T of the virtual power plant vpp , accumulate the regulation capacities P of each virtual power plant u to calculate the regulation capacity P of the virtual power plant vpp , select the minimum value of the regulation rate v of the aggregation unit u as the regulation rate v of the virtual power plant vpp , and record them into the virtual power plant archive information table respectively; Adjust the capacity P of the aggregation unit u and the adjustment duration T of the aggregation unit u into the formula Q = P * T to calculate the adjusted power Q of the aggregation unit u , calculate the adjustment deviation rate d of the virtual power plant according to the following formula vpp and enter it into the virtual power plant file information table:

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the virtual power plant multi-agent aggregation regulation ability quantitative evaluation method described in any one of claims 1 to 7.

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