Virtual power plant task scheduling processing method, electronic device and storage medium

Abstract

The application provides a kind of virtual power plant task scheduling processing method, electronic equipment and storage medium, belong to power distribution network control technical field.The method comprises: receiving to be scheduled task, to be scheduled task includes target power and power deviation threshold;According to to be scheduled task, select multiple aggregation units with different aggregation types and distribution positions from the regulation unit library as the first unit, and issue the first scheduling instruction of executing target power to the first unit;Select multiple aggregation units matched with the aggregation type and distribution position of the first unit from the margin unit library as the second unit, and issue the second scheduling instruction of executing power deviation threshold to the second unit;According to the response result of first scheduling instruction and second scheduling instruction, the first unit that does not satisfy the first condition is adjusted to the margin unit library, and the second unit that satisfies the second condition is adjusted to the regulation unit library.The application improves the test dynamicity and accuracy of aggregation unit response deviation degree.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network regulation, and particularly relates to a virtual power plant task scheduling processing method, an electronic device and a storage medium. BACKGROUND

[0002] With the development of the power system towards intelligence and marketization, as an innovative form of distributed energy aggregation, the virtual power plant (VPP) gradually becomes an important means of power grid regulation. The virtual power plant can provide key services such as peak clipping and valley filling, load regulation, and the like in power grid dispatching by integrating energy storage units, adjustable loads and other distributed energy resources, thereby enhancing the flexibility and stability of the power grid. However, the efficient operation of the virtual power plant highly depends on the accurate scheduling of the internal aggregated units and the response deviation degree evaluation.

[0003] Due to the extremely high real-time requirement of power grid dispatching, embedding the accuracy evaluation into the real-time dispatching will interfere with the normal dispatching process, leading to delay in dispatching or untimely response, thereby affecting the stability and reliability of the power grid. Therefore, the traditional accuracy evaluation method is usually carried out in an offline environment or for a short test when the power grid load is light, which is relatively independent of the actual dispatching process. For example, when the energy storage unit is first connected to the virtual power plant, it is tested whether the energy storage unit can accept the virtual power plant dispatching command to charge and discharge, and the air conditioner reduces the load. This kind of method not only cannot produce economic value in actual operation, but also due to the lack of dynamic tracking mechanism, it is difficult to capture the time-varying characteristics of the performance of the aggregated units.

[0004] Therefore, there is an urgent need for a virtual power plant task scheduling method that can dynamically test the response deviation degree of the aggregated units without affecting the overall response deviation degree of the virtual power plant. SUMMARY

[0005] The present application aims to provide a virtual power plant task scheduling processing method, an electronic device and a storage medium to solve at least one of the above technical problems.

[0006] To achieve the above purpose, in a first aspect, the present application provides a virtual power plant task scheduling processing method, which comprises:

[0007] receiving a to-be-scheduled task, the to-be-scheduled task comprising a target power, a target execution time period and a power deviation threshold, the power deviation threshold being less than the target power;

[0008] According to the to-be-scheduled task and the unit characteristics of each aggregated unit in the adjustment unit library, first unit balancing selection is performed to select multiple aggregated units with different aggregation types and distribution positions from the adjustment unit library as first units, and first scheduling instructions for executing the target power are issued to the first units, the adjustment unit library being composed of aggregated units with a first comprehensive response deviation less than a first preset deviation;

[0009] Multiple aggregated units with the same aggregation type and distribution position as the first units are selected from the margin unit library as second units, and second scheduling instructions for executing the power deviation threshold are issued to the second units, the margin unit library being composed of aggregated units with a second comprehensive response deviation greater than or equal to a second preset deviation and aggregated units with an execution frequency less than a preset frequency threshold;

[0010] According to the response results of the first scheduling instructions and the second scheduling instructions, the first units that do not meet a first condition are adjusted to the margin unit library, and the second units that meet a second condition are adjusted to the adjustment unit library.

[0011] In some embodiments, the unit characteristics include aggregation type, distribution position, real-time occupied power, and unit power constraint parameter, and the first unit balancing selection from the adjustment unit library includes:

[0012] An energy map is constructed based on the distribution positions of each aggregated unit in the adjustment unit library, each node in the energy map corresponding to an aggregated unit;

[0013] An energy-time space is constructed on the energy map based on the unit characteristics of each node;

[0014] Based on the target power and the target execution time period, first unit balancing selection is performed in the energy-time space to generate multiple groups of candidate power output paths, each candidate power output path including multiple aggregated units with different aggregation types and distribution positions;

[0015] The comprehensive performance indicators of each candidate power output path are calculated respectively, the candidate power output path with the lowest comprehensive performance indicator is determined as a target power output path, and the multiple aggregated units included in the target power output path are selected as the first units.

[0016] In some embodiments, the construction of the energy-time space on the energy map based on the unit characteristics of each node includes:

[0017] The target execution time period is divided into multiple sub-time periods, and based on the unit characteristics of each node, the available power range of each node within the multiple sub-time periods is predicted;

[0018] On the energy map, an energy layer is constructed for each node in each sub-time period. The energy layer is used to represent the available power range of the corresponding aggregation unit in the corresponding sub-time period.

[0019] The energy layers of each node are superimposed across all sub-time periods to form an energy-time space, where the energy layer change trajectory of each node on the time axis is used to represent the predicted available power of the corresponding aggregation unit in different time periods.

[0020] In some implementations, selecting multiple aggregation units from the margin unit library that match the aggregation type and distribution location of the first unit as the second unit includes:

[0021] The second unit is selected based on the number of times each aggregation unit in the margin unit library is executed. Multiple aggregation units that match the aggregation type and distribution position of the first unit are selected from the margin unit library as the second unit.

[0022] Before issuing the second scheduling instruction to the second unit to execute the power deviation threshold, the process includes:

[0023] Obtain historical execution data for each second unit, and calculate the power fluctuation value for each second unit based on the historical execution data, unit characteristics, and the power deviation threshold.

[0024] Based on the power fluctuation value and the previous execution power, the current execution power of each second unit is determined, so that the sum of the current execution power of each second unit matches the power deviation threshold.

[0025] The second scheduling instruction is formed based on the current execution power of each second unit.

[0026] In some implementations, the historical execution data includes historical execution power and corresponding historical response deviation, the unit characteristics include real-time power consumption and unit power constraint parameters, and the calculation of the power fluctuation value of each second unit based on the historical execution data, unit characteristics, and the power deviation threshold includes:

[0027] Calculate the response deviation weighting factor based on the historical response deviation in historical execution data;

[0028] The basic fluctuation amplitude is calculated based on the real-time occupied power and unit power constraint parameters;

[0029] calculating an initial fluctuation value of each second unit based on the response deviation weight factor, the base fluctuation amplitude, and a preset influence weight;

[0030] calculating an initial sum of current execution powers of all second units based on the initial fluctuation value of each second unit and the last historical execution power;

[0031] scaling the initial fluctuation value of each second unit according to a ratio of the initial sum and the power deviation threshold, and calculating a power fluctuation value of each second unit.

[0032] In some embodiments, the first condition includes that a first comprehensive response deviation degree is less than a first preset deviation degree; the second condition includes that a second comprehensive response deviation degree is less than a second preset deviation degree, and an execution frequency is greater than or equal to a preset frequency threshold; the first scheduling instruction includes a response target value of each first unit, the second scheduling instruction includes a response target value of each second unit, a response result of the first scheduling instruction includes an execution value of each first unit, and a response result of the second scheduling instruction includes an execution value of each second unit;

[0033] adjusting, according to the response results of the first scheduling instruction and the second scheduling instruction, a first unit that does not meet the first condition to a margin unit library and a second unit that meets the second condition to an adjustment unit library, including:

[0034] calculating a single-time response deviation degree of each first unit according to the first scheduling instruction and the response result of the first scheduling instruction;

[0035] calculating a first comprehensive response deviation degree of the first unit based on the single-time response deviation degree of the first unit and a historical response deviation degree in historical execution data of the first unit;

[0036] adjusting the first unit that does not meet that the first comprehensive response deviation degree is less than the first preset deviation degree to the margin unit library;

[0037] calculating a single-time response deviation degree of each second unit according to the second scheduling instruction and the response result of the second scheduling instruction;

[0038] calculating a second comprehensive response deviation degree of the second unit based on the single-time response deviation degree of the second unit and a historical response deviation degree in historical execution data of the second unit;

[0039] adjusting the second unit that meets that the second comprehensive response deviation degree is less than the second preset deviation degree and the execution frequency is greater than or equal to the preset frequency threshold to the adjustment unit library.

[0040] In some embodiments, after the receiving the task to be scheduled, the method further includes:

[0041] obtain a historical deviation record of the margin unit library, and input the historical deviation record into a Bayesian estimator to obtain a posterior deviation distribution output by the Bayesian estimator;

[0042] determine a margin power according to the posterior deviation distribution and the power deviation threshold;

[0043] the second scheduling instruction for the second unit to execute the power deviation threshold comprises:

[0044] the second scheduling instruction for the second unit to execute the margin power, so that the total power output by the second unit matches the power deviation threshold.

[0045] In some embodiments, the obtaining of the historical deviation record of the margin unit library and the input of the historical deviation record into the Bayesian estimator to obtain the posterior deviation distribution output by the Bayesian estimator comprises:

[0046] obtain a historical deviation record of the margin unit library and historical covariate information, the historical covariate information comprising at least one of weather type and holiday type;

[0047] obtain current covariate information corresponding to the historical covariate information;

[0048] input the historical deviation record, the historical covariate information and the current covariate information into the Bayesian estimator to obtain the posterior deviation distribution output by the Bayesian estimator.

[0049] In a second aspect, to achieve the above object, the present application further provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the processing method for virtual power plant task scheduling as described above.

[0050] In a third aspect, to achieve the above object, the present application further provides a computer storage medium, the storage medium storing executable instructions, the instructions being executed by a processor to make the processor execute the processing method for virtual power plant task scheduling as described above.

[0051] Compared with the prior art, the beneficial effects of the present application include:

[0052] In the first aspect, by separating the to-be-scheduled tasks of the virtual power plant into target power and power deviation threshold, and respectively executing by the adjusting unit library and the margin unit library, the target power is completed by the high-precision adjusting unit library, so as to ensure that the overall response deviation degree of the virtual power plant does not decrease, and the risk of power grid assessment is reduced. At the same time, the power deviation threshold is executed by the low-precision / aggregated unit newly connected in the margin unit library to test or bottom out, so as not to affect the power grid assessment settlement, and to dynamically test the comprehensive response deviation degree, thereby improving the dynamicity and accuracy of the response deviation degree evaluation.

[0053] In the second aspect, when selecting the first unit, the distribution position and type of the aggregated unit are considered for balanced selection, so as to avoid concentrating all the first units in the same geographical location or the same type, thereby reducing the risk of failure of all the first units due to local faults (such as device failure in a certain area, natural disasters, etc.). This decentralized strategy enhances the robustness of the virtual power plant, and even if part of the first units fail, the first units in other regions or types can still continue to work.

[0054] In the third aspect, by selecting multiple aggregated units matching the aggregation type and distribution position of the first units as the second units, on the one hand, since the second units match the first units in aggregation type and distribution position, this helps to enhance the coordination within the virtual power plant, and when the power grid is disturbed or the demand changes, the second units can better understand and adapt to the working state and mode of the first units, thereby more effectively cooperating with the first units to adjust the power and maintain the stable operation of the power grid. At the same time, this matching reduces the risk of power fluctuations and system instability caused by the incoordination between units, and improves the reliability and quality of the entire virtual power plant in providing external power services. On the other hand, this provides an ideal contrast environment for the evaluation of the response deviation degree. Under the same type and position conditions, by comparing the response of the first units and the second units when performing tasks, other interference factors such as inherent differences between different types of units or differences in power grid conditions in different positions can be effectively reduced, thereby more accurately evaluating the response deviation degree of the second units. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope of the present application.

[0056] Figure 1 A flowchart of a processing method for task scheduling of a virtual power plant in an embodiment;

[0057] Figure 2A flowchart of a refinement process of selecting the first units according to the characteristics of the units in the library of the to-be-scheduled tasks and the adjusting units for each aggregate unit in an embodiment;

[0058] Figure 3 A flowchart of how to form the second scheduling instruction of the second unit in an embodiment;

[0059] Figure 4 A flowchart of how to determine the margin power in an embodiment;

[0060] Figure 5 A structural diagram of an electronic device related to the processing method of the virtual power plant task scheduling in the embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0062] All the terms used in the present application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted to have meanings consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0063] For example, the terms "first", "second", etc. used in the present application can be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first unit can be referred to as the second unit, and similarly, the second unit can be referred to as the first unit. Both the first unit and the second unit are units, but they are not the same unit.

[0064] For another example, the terms "include", "contain", etc. used in the present application indicate the presence of features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0065] As mentioned above, since the real-time requirement of power grid dispatching is very high, embedding accuracy evaluation into real-time dispatching will interfere with the normal dispatching process, cause dispatching delay or untimely response, and thus affect the stability and reliability of the power grid. Therefore, the traditional accuracy evaluation method is usually carried out in an offline environment or for a short test when the load of the power grid is light, and is relatively independent of the actual dispatching process. For example, when the energy storage unit is first connected to the virtual power plant, it is tested whether the energy storage unit can accept the virtual power plant dispatching command to charge and discharge, and the air conditioner load reduction. Such a method not only cannot produce economic value in actual operation, but also is difficult to capture the time-varying characteristics of the performance of the aggregation unit due to the lack of a dynamic tracking mechanism. Therefore, there is an urgent need to propose a method that can dynamically test the response deviation degree of the aggregation unit without affecting the overall response deviation degree of the virtual power plant. To this end, the present application provides a virtual power plant task scheduling processing method, an electronic device and a storage medium, which improves the dynamicity and accuracy of the test of the response deviation degree of the aggregation unit.

[0066] As shown in Figure 1 The present application provides a virtual power plant task scheduling processing method, which comprises the following steps:

[0067] Step S10, receiving a task to be dispatched.

[0068] In the present embodiment, the task to be dispatched refers to the task issued by the power grid dispatching center to the virtual power plant, including target power, target execution time period and power deviation threshold. For example, in the t1-t2, t2-t3, t3-t4 three time periods, the load is increased by P1, P2, P3. Then the target power corresponding to the three target execution time periods is P0+P1, P0+P2, P0+P3 respectively. Wherein, P0 is the baseline power before the increase, and P1, P2 and P3 are the increase power of the three time periods respectively.

[0069] The target power refers to the power output target that the virtual power plant needs to reach. The target execution time period refers to the time range in which the target power needs to be executed. The power deviation threshold refers to the maximum deviation between the actual power and the target power, and the power deviation threshold is less than the target power. It should be noted that the power deviation threshold is set according to the evaluation rules of the power grid dispatching center. For example, the evaluation rules of a certain power grid clearly stipulate that "filling valley: the actual adjustment power of the third party independent subject is greater than 120% of the day-ahead winning power, and the part exceeding 120% at this sampling point is not compensated. The actual adjustment power is less than 70% of the day-ahead winning power, and this sampling point is not compensated." Then the power deviation threshold is 20% of the target power.

[0070] In step S20, a first unit balancing selection is performed according to the to-be-scheduled task and the unit characteristics of each aggregated unit in the regulating unit library, a plurality of aggregated units with different aggregation types and distribution positions are selected from the regulating unit library as first units, and a first scheduling instruction for executing the target power is issued to the first units.

[0071] It should be noted that in the virtual power plant in the embodiment, all the schedulable aggregated units are divided into a regulating unit library and a margin unit library. The regulating unit library is composed of aggregated units with a first comprehensive response deviation degree less than a first preset deviation degree, and the margin unit library is composed of aggregated units with a second comprehensive response deviation degree greater than or equal to a second preset deviation degree and aggregated units with an execution frequency less than a preset frequency threshold. The first comprehensive response deviation degree and the second comprehensive response deviation degree are respectively the comprehensive response deviation degrees of the aggregated units in the regulating unit library and the margin unit library calculated in the task scheduling.

[0072] The first preset deviation degree is an index for measuring the response deviation degrees of the aggregated units in the regulating unit library, and represents the upper limit of the response deviation degrees of these units in historical scheduling tasks. Only the units with a comprehensive response deviation degree less than the first preset deviation degree are included in the regulating unit library, so as to ensure a high overall response deviation degree of the regulating unit library, thereby enabling the virtual power plant to stably and accurately respond to the scheduling instructions of the power grid. The second preset deviation degree is an index for measuring the response deviation degrees of the aggregated units in the margin unit library, and represents the lower limit of the response deviation degrees of these units in historical scheduling tasks. The values of the first preset deviation degree and the second preset deviation degree can be the same or different.

[0073] The preset frequency threshold is related to the number of times the aggregated units are executed by the scheduling instructions. It can be set to 0, so as to directly put the newly accessed aggregated units into the margin unit library for scheduling test, thereby enabling the newly accessed aggregated units to test the response deviation degrees in actual scheduling. The preset frequency threshold can also be set to other appropriate values, indicating that only when the aggregated units pass a certain number of scheduling tests and meet the corresponding accuracy requirements, the aggregated units are put into the regulating unit library. Such a setting can ensure that the units in the regulating unit library not only meet the accuracy requirements, but also reach a certain level in terms of actual execution experience, thereby improving the reliability and stability of the regulating unit library.

[0074] The unit characteristics of the aggregation unit include one or more of the following: aggregation type, distribution location, real-time occupied power, and unit power constraint parameters. The aggregation type refers to the functional category of the aggregation unit, such as energy storage unit, air conditioning load, photovoltaic power station, and traditional power station. Different types of aggregation units have different roles and functions in the power system. For example, energy storage units can be used for power regulation and emergency backup, while photovoltaic power stations mainly provide renewable power. The distribution location refers to the geographical distribution of the aggregation unit, which can be a specific physical location (such as a certain community, a certain street, etc.), or a regional distribution (such as a certain urban area), without necessarily being accurate to a specific latitude and longitude location point. The real-time occupied power represents the actual output or consumption power of the aggregation unit at a certain time, reflecting the real-time operating state of the aggregation unit. The unit power constraint refers to the safety boundary constraint of each aggregation unit. Taking the energy storage aggregation unit as an example, the unit power constraint parameters include power constraint and energy constraint.

[0075] The power constraint means that the sum of the baseline power and the upward adjusted target power cannot exceed the maximum charging power at any time, or the sum of the baseline power and the downward adjusted target power cannot exceed the maximum discharging power.

[0076] The energy constraint means that the integral of the upward adjusted power over the adjustment time period cannot exceed the maximum remaining chargeable amount at the start time of the scheduling, or the integral of the downward adjusted power over the adjustment time cannot exceed the maximum remaining dischargeable amount at the start time of the scheduling.

[0077] The first unit balancing selection refers to selecting multiple aggregation units with different aggregation types and different distribution locations from the adjustment unit library as the first units. Through balanced selection, the risk of regional failure can be reduced by avoiding selecting units with too concentrated or single aggregation type or location distribution.

[0078] In some optional embodiments of the first unit balancing selection, an optimization algorithm (such as linear programming, genetic algorithm, etc.) can be used to select units based on the scheduling task and the unit characteristics of each unit in the adjustment unit library. A machine learning model can also be introduced to predict the performance of different combinations of units based on historical scheduling data to assist the optimization algorithm in selection. This ensures that the selected first units can efficiently and reliably complete the target power output, while improving the stability and flexibility of the virtual power plant.

[0079] The first scheduling instruction refers to the specific operation command issued by the virtual power plant to the first units. The first scheduling instruction contains the response target value of each first unit corresponding to each target execution time period. The response target value of each unit can be calculated based on the target power and the unit characteristics of each first unit, thereby generating the first scheduling instruction.

[0080] Step S30, selecting a plurality of aggregated units matching the aggregation type and distribution position of the first unit as second units from the margin unit library, and issuing a second scheduling instruction of executing the power deviation threshold to the second units.

[0081] In the embodiment, the second units refer to the aggregated units selected from the margin unit library for executing the power deviation threshold, and the aggregation type and distribution position of the second units match the aggregation type and distribution position of the first units, thereby enhancing the coordination of the adjustment unit library and the margin unit library. The second scheduling instruction contains the response target value of each second unit.

[0082] In some embodiments, the second unit balanced selection can be based on the execution times of each aggregated unit in the margin unit library, and a plurality of aggregated units matching the aggregation type and distribution position of the first unit are selected as the second units from the margin unit library. The second unit balanced selection includes: selecting a plurality of aggregated units matching the aggregation type and distribution position of the first unit as margin units, sorting the margin units according to the execution times, and dividing the sorting results into a plurality of time intervals, and balanced selecting a number of margin units in each time interval as the second units to ensure that aggregated units with different execution times can have the opportunity to participate in the scheduling test.

[0083] Step S40, according to the response results of the first scheduling instruction and the second scheduling instruction, adjusting the first units not satisfying the first condition to the margin unit library, and adjusting the second units satisfying the second condition to the adjustment unit library.

[0084] In the embodiment, the first condition includes that the first comprehensive response deviation degree is less than the first preset deviation degree. The second condition includes that the second comprehensive response deviation degree is less than the second preset deviation degree, and the execution time is greater than or equal to the preset time threshold. The response result of the first scheduling instruction contains the execution value of each first unit. The response result of the second scheduling instruction contains the execution value of each second unit.

[0085] In some implementations, the single response deviation of each first unit can be calculated based on the first scheduling instruction and its response result. A first comprehensive response deviation of the first unit can be calculated based on the single response deviation and the historical response deviation in the historical execution data of the first unit. First units whose first comprehensive response deviation is less than a first preset deviation are adjusted to the margin unit library. Similarly, the single response deviation of each second unit can be calculated based on the second scheduling instruction and its response result. A second comprehensive response deviation of the second unit can be calculated based on the single response deviation and the historical response deviation in the historical execution data of the second unit. Second units whose second comprehensive response deviation is less than a second preset deviation and whose execution count is greater than or equal to a preset threshold are adjusted to the adjustment unit library.

[0086] For example, the formula for calculating the single response deviation of a certain aggregation unit in the task to be scheduled is shown in formula (1):

[0087] (1)

[0088] in, To address the deviation, T represents the execution time of all target execution time periods for the task to be scheduled, t represents a specific execution time, and n represents the number of target execution time periods. The target value of the response to be sent to this aggregation unit. This is the execution value of the aggregation unit.

[0089] The formula for calculating the overall response deviation is shown in formula (2):

[0090] (2)

[0091] Where cR is the overall response deviation, and m is the number of times the aggregation unit executes the scheduling instruction. The single response deviation when executing the m-th scheduling instruction. The historical response deviation for executing the i-th scheduling instruction.

[0092] Formula (2) combines the single response deviation ( ) and historical response bias ( The overall response deviation (cR) is calculated using this method, which comprehensively reflects the overall response performance of the aggregation unit across multiple scheduling tasks. This comprehensive evaluation method not only considers the unit's response deviation in the current task but also takes into account its historical response performance, avoiding test bias caused by the randomness of a single task, thus more accurately measuring the response capability of the aggregation unit.

[0093] In the virtual power plant task scheduling method proposed in this application, firstly, by separating the tasks to be scheduled in the virtual power plant into target power and power deviation threshold, and executing them respectively by the regulating unit library and the margin unit library, the target power is achieved by the high-precision regulating unit library, thereby ensuring that the overall response deviation of the virtual power plant does not decrease and reducing the risk of grid assessment. Simultaneously, the power deviation threshold is executed by the low-precision / newly connected aggregation unit in the margin unit library for testing or as a fallback. This does not affect grid assessment and settlement, and allows for dynamic testing of the comprehensive response deviation, improving the dynamism and accuracy of response deviation assessment.

[0094] Secondly, when selecting the first unit, a balanced selection is made by considering the distribution location and type of the aggregated units, avoiding the concentration of all first units in the same geographical location or of the same type. This reduces the risk of all first units failing due to localized failures (such as equipment failure in a certain area or natural disasters). This decentralized strategy enhances the robustness of the virtual power plant, ensuring that even if some first units fail, first units in other regions or of different types can continue to operate.

[0095] Thirdly, by selecting multiple aggregation units that match the aggregation type and distribution location of the first unit as the second unit, on the one hand, the matching of the second unit with the first unit in terms of aggregation type and distribution location helps to enhance the coordination within the virtual power plant. When the grid experiences disturbances or changes in demand, the second unit can more quickly understand and adapt to the working state and mode of the first unit, thereby more effectively cooperating with the first unit in power regulation and jointly maintaining the stable operation of the grid. At the same time, this matching also reduces the risk of power fluctuations and system instability caused by incoordination between units, improving the reliability and quality of the power service provided by the entire virtual power plant. On the other hand, this provides an ideal comparison environment for evaluating response deviation. Under the same type and location conditions, by comparing the response of the first unit and the second unit when performing tasks, other interfering factors can be effectively reduced, such as the inherent differences between different types of units or the differences in grid conditions at different locations, thereby more accurately evaluating the response deviation of the second unit.

[0096] In one embodiment, such as Figure 2 As shown, step S20 includes:

[0097] Step S21: Construct an energy map based on the distribution location of each aggregation unit in the regulation unit library.

[0098] In this embodiment, each aggregation unit in the regulating unit library can be mapped to an energy map as a node. Each node in the energy map corresponds to an aggregation unit, and the node location corresponds to the actual geographical location of the aggregation unit.

[0099] Step S22, constructing an energy-time space on the energy map based on the unit characteristics of each node.

[0100] In some embodiments, the energy-time space can be constructed on the energy map based on the target execution time period and the unit characteristics of each node.

[0101] Specifically, the target execution time period can be divided into multiple sub-time periods, and the available power range of each node in the multiple sub-time periods can be predicted based on the unit characteristics of each node, such as the aggregation type, the real-time occupied power, the unit power constraint parameter, the associated real-time weather forecast data, etc., through time series analysis or deep learning model technology. An energy layer is constructed for each node in each sub-time period on the energy map, and the energy layer is a visualization layer for representing the available power range of the corresponding aggregation unit in the corresponding sub-time period. The visualization parameters of the energy layer, such as color, shape, and transparency, can be adjusted according to the available power range to enhance the visualization effect. The energy layers of each node in all sub-time periods are superimposed to form an energy-time space, wherein the energy layer change track of each node on the time axis is used to represent the predicted available power of the corresponding aggregation unit in different time periods. This embodiment makes the power prediction more refined by dividing the target execution time period into multiple sub-time periods and predicting the available power range of each aggregation unit in each sub-time period.

[0102] Exemplarily, for a virtual power plant containing multiple solar photovoltaic units, the energy-time space can show the power generation power change of each photovoltaic unit at different time periods (such as morning, noon, and evening) in a day.

[0103] Step S23, performing first unit balancing selection in the energy-time space based on the target power and the target execution time period to generate multiple groups of candidate power output paths.

[0104] In this embodiment, based on the target power and the target execution time period, multiple groups of candidate power output paths can be generated in the energy-time space by using optimization algorithms (such as dynamic programming, genetic algorithm), and each candidate power output path includes a group of multiple aggregation units with different aggregation types and distribution positions.

[0105] From the perspective of visualization, the candidate power output path can be regarded as a curve formed in the energy-time space of the energy map. This curve connects the aggregation units with different aggregation types and distribution positions within the target execution time period.

[0106] For example, assume that a virtual power plant needs to provide a target power of 100 MW in a target execution time period (e.g., 2:00 pm to 5:00 pm). Using an optimization algorithm, multiple candidate power output paths that combine different aggregation types and distribution locations of aggregation units to meet the target power of 100 MW can be obtained.

[0107] Candidate power output path example one: energy storage unit A (east), discharging power 40 MW; energy storage unit B (west), discharging power 30 MW; adjustable industrial load (middle), adjusted power 20 MW; distributed power generation unit (south), power generation 10 MW.

[0108] Total power: 40 MW + 30 MW + 20 MW + 10 MW = 100 MW.

[0109] Candidate power output path example two: distributed photovoltaic power station (south), power generation 50 MW; distributed wind power station (north), power generation 30 MW; energy storage unit B (west), discharging power 20 MW. Total power: 50 MW + 30 MW + 20 MW = 100 MW.

[0110] Candidate power output path example three: energy storage unit (east), discharging power 25 MW; adjustable industrial load (middle), adjusted power 35 MW; distributed photovoltaic power station (south), power generation 25 MW; small gas turbine (west), power generation 15 MW. Total power: 25 MW + 35 MW + 25 MW + 15 MW = 100 MW.

[0111] In step S23, based on the target power and the target execution time period, an optimization algorithm (such as dynamic programming, genetic algorithm) can be used to make balanced selection of the first unit in the energy-time space, and generate multiple groups of candidate power output paths. Each candidate power output path includes a group of multiple aggregation units with different aggregation types and distribution locations, and also includes corresponding execution time period and response target value. These units work together on the time axis to meet the target power demand.

[0112] Step S24, respectively calculate the comprehensive performance index of each candidate power output path, determine the candidate power output path with the highest comprehensive performance index as the target power output path, and select the multiple aggregation units included in the target power output path as the first unit.

[0113] The comprehensive performance indicator in this embodiment is a quantitative indicator for comprehensively evaluating the performance of the candidate power output path, which can include economic cost indicators, response deviation indicators, and environmental impact indicators, and the like in multiple dimensions. The economic cost indicators include fuel costs, operation and maintenance costs, and the like, which can be calculated by integrating the power output and cost coefficients. The response deviation indicator is used to measure the total response deviation of each candidate power output path, which can be predicted by weighted summation according to the historical response deviation of each aggregation unit in the candidate power output path. The weight of each aggregation unit is determined according to its power output proportion. The environmental impact indicator can be calculated according to the emission factor and power output of each aggregation unit. The emission factor reflects the amount of pollutants emitted per unit of electricity generated, such as carbon dioxide, sulfur dioxide, and the like. By multiplying the emission factor of each aggregation unit by its power output and then accumulating, the environmental impact indicator can be obtained.

[0114] It can be understood that when evaluating the performance of the candidate power output path, the economic cost indicator, the response deviation indicator, and the environmental impact indicator are all the smaller, the better the performance. Therefore, a comprehensive performance indicator can be calculated by summation, weighted summation, and the like. The lower the comprehensive performance indicator, the better the performance. The candidate power output path with the lowest comprehensive performance indicator is determined as the target power output path, and the multiple aggregation units included in the target power output path are selected as the first unit.

[0115] Exemplarily, candidate power output path 1: economic cost indicator (yuan), 1000; response deviation indicator (%), 5; environmental impact indicator (kg CO2), 200. Candidate power output path 2: economic cost indicator (yuan), 800; response deviation indicator (%), 8; environmental impact indicator (kg CO2), 150. Candidate power output path 3: economic cost indicator (yuan), 900; response deviation indicator (%), 6; environmental impact indicator (kg CO2), 180.

[0116] Assuming that the weight of the economic cost indicator is 0.4, the weight of the response deviation indicator is 0.3, and the weight of the environmental impact indicator is 0.3, the comprehensive performance indicator of the candidate power output path 1 is: 0.4x1000+0.3x5+0.3x200=400+1.5+60=461.5. The comprehensive performance indicator of the candidate power output path 2 is: 0.4x800+0.3x8+0.3x150=320+2.4+45=367.4. The comprehensive performance indicator of the candidate power output path 3 is: 0.4x900+0.3x6+0.3x180=360+1.8+54=415.8.

[0117] Therefore, candidate power output path 2 has the lowest overall performance index, and is selected as the target power output path.

[0118] In the virtual power plant task scheduling processing method proposed in this application embodiment, by constructing an energy map and energy-time space, the distribution location of each aggregation unit and the available power range in different time periods can be intuitively displayed, which improves the accuracy and efficiency of determining the first unit by filtering out the target power output path.

[0119] In one embodiment, such as Figure 3 As shown, in step S30, after selecting multiple aggregation units from the margin unit library that match the aggregation type and distribution location of the first unit as the second unit, and before issuing a second scheduling instruction to the second unit to execute the power deviation threshold, the following steps are included:

[0120] Step S31: Obtain historical execution data for each second unit, and calculate the power fluctuation value for each second unit based on the historical execution data, unit characteristics, and the power deviation threshold.

[0121] In this embodiment, historical execution data includes historical execution power and the corresponding historical response deviation. The power fluctuation value refers to the magnitude of change in the target response value when the second unit executes historical scheduling instructions.

[0122] Specifically, let the historical execution data sequence H = { , ,..., }, where the i-th scheduling instruction is executed. Includes historical execution power and the corresponding single response deviation Where n is the total number of executions. The characteristics of each second unit include real-time power consumption. and power constraint parameters (including maximum power limit) and minimum power limit ).

[0123] Calculate the response deviation weighting factor based on historical response deviation data. The calculation formula is shown in formula (3):

[0124] (3)

[0125] Here, λ is the historical data decay factor, which is used to give higher weight to recent historical execution data.

[0126] For each second unit, a basic fluctuation amplitude B is calculated based on the real-time occupied power, the maximum power limit and the minimum power limit, and the calculation process is shown in formula (4):

[0127] (4)

[0128] The basic fluctuation amplitude B obtained by formula (4) is used to reflect the relative position of the real-time occupied power of the corresponding second unit in its power range, as the basic amplitude of fluctuation.

[0129] Based on the response deviation degree weight factor, the basic fluctuation amplitude B and the preset influence weight (empirical data), an initial fluctuation value is calculated, and the specific calculation process is shown in formula (5):

[0130] (5)

[0131] Based on the initial fluctuation value corresponding to each second unit and the last execution power, an initial sum of the current execution power of all second units is calculated, and the initial fluctuation value is scaled according to the ratio of the initial sum to the power deviation threshold, to calculate the power fluctuation value of each second unit.

[0132] Specifically, when the initial sum is greater than the power deviation threshold, the initial fluctuation values of all second units are proportionally reduced according to the ratio of the initial sum to the power deviation threshold, to obtain the power fluctuation values corresponding to all second units. When the initial sum is less than the power deviation threshold, the initial fluctuation values of all second units are proportionally enlarged according to the ratio of the initial sum to the power deviation threshold, to obtain the power fluctuation values corresponding to all second units. When the initial sum is equal to the power deviation threshold, the initial fluctuation value is determined as the power fluctuation value.

[0133] It should be noted that the power fluctuation value is proportional to the response deviation degree weight factor, that is, the lower the response deviation degree in the historical execution data, the higher the historical response accuracy, which can be tested in a wider power range, that is, the larger the power fluctuation value.

[0134] Step S32, based on the power fluctuation value and the last execution power, the current execution power of each second unit is determined, so that the sum of the current execution power of each second unit matches the power deviation threshold.

[0135] Step S33, based on the current execution power of each second unit, a second scheduling instruction is formed.

[0136] In this embodiment, the current execution power of each second unit is determined by summing the power fluctuation value and the last execution power. Based on the determined second units and the current execution power of each second unit corresponding to the target execution time period, specific operation commands for the second units are formed, i.e., the second scheduling instructions.

[0137] In the virtual power plant task scheduling processing method proposed in the embodiments of the present application, in a first aspect, the current execution power of the second unit is adjusted by calculating the power fluctuation value, ensuring that each second unit can run at different power levels during each scheduling. This differentiated testing method helps to comprehensively evaluate the response capability of each unit at different power outputs, avoiding test bias caused by long-term fixed power running. In a second aspect, the power fluctuation value determined based on historical execution data makes the power fluctuation range of the unit with higher historical response accuracy larger, which can be tested and adjusted in a wider power range. It also avoids allocating too large power variation to the unit with low historical response accuracy, reduces the power adjustment failure caused by insufficient unit performance, and improves the efficiency of the overall system.

[0138] In one embodiment, as shown in FIG. 10, after step S10, the method further includes: Figure 4

[0139] In step S11, the historical deviation record of the margin unit library is obtained, and the historical deviation record is input into the Bayesian estimator to obtain the posterior deviation distribution output by the Bayesian estimator.

[0140] In this embodiment, the historical deviation record includes the target scheduling power and the actual output power of the margin unit library in the historical scheduling task. The Bayesian estimator is a statistical tool that can calculate the posterior probability distribution according to the deviation record of the target scheduling power and the actual output power in the historical scheduling task. The posterior deviation distribution describes the possible distribution of future deviations, such as the value range and corresponding probability of future deviations under different target scheduling powers.

[0141] ​In some embodiments, historical deviation records of the margin unit library and historical covariate information including at least one of weather types (sunny, cloudy, rainy, etc.) and holiday types (workday, weekend, statutory holiday, etc.) can be obtained. Current covariate information corresponding to the historical covariate information is obtained. The type of information of the current covariate information is consistent with the type of the historical covariate information, and the current weather type when the to-be-scheduled task is executed can be obtained through a weather forecast interface, and whether the date when the to-be-scheduled task is executed is a holiday can be determined through a calendar service. The historical deviation records, the historical covariate information and the current covariate information are input into the Bayesian estimator to obtain a posterior deviation distribution output by the Bayesian estimator. By integrating the covariate information, the prediction of the Bayesian estimator is closer to the actual condition, and the accuracy of the deviation prediction is improved.

[0142] In step S12, the margin power is determined according to the posterior deviation distribution and the power deviation threshold.

[0143] Based on the posterior deviation distribution, the power deviation threshold is taken as the target scheduling power, the value range of the future deviation and the corresponding probability are obtained, and the value corresponding to the maximum probability is taken as the deviation value (which can be positive, negative or zero). The sum of the deviation value and the power deviation threshold is calculated to obtain the margin power, which refers to the power that can be additionally reserved based on the target power.

[0144] For example, assuming that the target power is 10 MW, the power deviation threshold is 2 MW, and the posterior deviation distribution shows that when the target scheduling power is 2 MW, the deviation value is between +1 MW and -1 MW, and the deviation value corresponding to the maximum probability is +0.5 MW. Then the margin power is 2 MW + (+0.5 MW) = 2.5 MW.

[0145] Further, in step S20, the second scheduling instruction for executing the power deviation threshold for the second unit includes a second scheduling instruction for executing the margin power for the second unit, so that the total power output by the second unit matches the power deviation threshold.

[0146] In the processing method for virtual power plant task scheduling proposed in the embodiments of the present application, the most likely deviation value is predicted through the posterior deviation distribution, so that the calculation of the margin power is more accurate, and the accuracy of the virtual power plant in responding to the grid scheduling is improved. Moreover, the margin power is calculated by combining the deviation value and the power deviation threshold, so that the total power actually output by the second unit can meet the grid evaluation requirements and avoid excessive or insufficient reservation, so that the virtual power plant can operate in a more economical way under the premise of meeting the grid evaluation requirements, thereby reducing the operating cost.

[0147] In one embodiment, a computer storage medium is provided that stores executable instructions that, when executed by a processor, cause the processor to perform the steps in the above method embodiments.

[0148] In one embodiment, an electronic device is also provided, including one or more processors; and a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the steps in the above method embodiments.

[0149] In one embodiment, such as Figure 5 The diagram illustrates the structure of an electronic device used to implement an embodiment of this application. The electronic device includes a central processing unit (CPU) 101, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 102 or a program loaded from a storage portion 108 into a random access memory (RAM) 103. The RAM 103 also stores various programs and data required for the operation of the electronic device. The CPU 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0150] The following components are connected to I / O interface 105: an input section 106 including a keyboard, mouse, etc.; an output section 107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 108 including a hard disk, etc.; and a communication section 109 including a network interface card such as a LAN card, modem, etc. The communication section 109 performs communication processing via a network such as the Internet. A drive 110 is also connected to I / O interface 105 as needed. A removable medium 111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 110 as needed so that computer programs read from it can be installed into storage section 108 as needed.

[0151] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer-readable medium carrying instructions that, in such embodiments, can be downloaded and installed from a network via communication section 109, and / or installed from removable medium 111. When the instructions are executed by central processing unit (CPU) 101, the various method steps described in this application are performed.

[0152] Finally, it should be noted that the above-described embodiments are merely intended for describing and illustrating, but not limiting the technical solutions of the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some or all of the technical features thereof; and such modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0153] Furthermore, those skilled in the art will appreciate that a combination of features from different embodiments can be meant to be within the scope of the present application and form a different embodiment, even though some embodiments herein include certain features but not others that are included in other embodiments. For example, any of the above-described claimed embodiments or aspects can be used in any combination. Information disclosed in the Background section is intended only to enhance understanding of the general background of the present application and should not be considered to constitute general knowledge in the art nor be used to convey recognition of any art in the field.

Claims

1. A method for processing virtual power plant task scheduling, characterized in that, The method includes: Receive a task to be scheduled, the task to be scheduled includes a target power, a target execution time period and a power deviation threshold, wherein the power deviation threshold is less than the target power; Based on the unit characteristics of the task to be scheduled and each aggregation unit in the adjustment unit library, a first unit balancing selection is performed. Multiple aggregation units with different aggregation types and distribution locations are selected from the adjustment unit library as the first unit, and a first scheduling instruction to execute the target power is issued to the first unit. The adjustment unit library is composed of aggregation units with a first comprehensive response deviation less than a first preset deviation. The first preset deviation is the upper limit of the response deviation of the aggregation units in the adjustment unit library in historical scheduling tasks. Multiple aggregation units that match the aggregation type and distribution location of the first unit are selected from the margin unit library as the second unit, and a second scheduling instruction to execute the power deviation threshold is issued to the second unit. The margin unit library consists of aggregation units whose second comprehensive response deviation is greater than or equal to the second preset deviation and aggregation units whose execution number is less than the preset number threshold. The second preset deviation is the lower limit of the response deviation of the aggregation units in the margin unit library in historical scheduling tasks. Based on the response results of the first scheduling instruction and the second scheduling instruction, the first unit that does not meet the first condition is adjusted to the margin unit library, and the second unit that meets the second condition is adjusted to the adjustment unit library. The first condition includes that the first comprehensive response deviation is less than the first preset deviation, and the second condition includes that the second comprehensive response deviation is less than the second preset deviation, and the number of executions is greater than or equal to the preset number of executions threshold.

2. The virtual power plant task scheduling processing method according to claim 1, characterized in that, The unit characteristics include aggregation type, distribution location, real-time power consumption, and unit power constraint parameters. The first unit balancing selection is performed based on the unit characteristics of each aggregation unit in the task to be scheduled and the adjustment unit library. This involves selecting multiple aggregation units with different aggregation types and distribution locations from the adjustment unit library as the first unit, including: An energy map is constructed based on the distribution location of each aggregation unit in the regulation unit library, and each node in the energy map corresponds to an aggregation unit; Based on the unit characteristics of each node, an energy-time space is constructed on the energy map; Based on the target power and the target execution time period, a first unit equalization selection is performed in the energy-time space to generate multiple sets of candidate power output paths. Each candidate power output path includes a set of multiple aggregation units with different aggregation types and distribution locations. Calculate the comprehensive performance index of each candidate power output path, determine the candidate power output path with the lowest comprehensive performance index as the target power output path, and select multiple aggregation units included in the target power output path as the first unit.

3. The virtual power plant task scheduling processing method according to claim 2, characterized in that, The construction of an energy-time space on the energy map based on the unit characteristics of each node includes: The target execution time period is divided into multiple sub-time periods, and based on the unit characteristics of each node, the available power range of each node within the multiple sub-time periods is predicted; On the energy map, an energy layer is constructed for each node in each sub-time period. The energy layer is used to represent the available power range of the corresponding aggregation unit in the corresponding sub-time period. The energy layers of each node are superimposed across all sub-time periods to form an energy-time space, where the energy layer change trajectory of each node on the time axis is used to represent the predicted available power of the corresponding aggregation unit in different time periods.

4. The virtual power plant task scheduling processing method according to claim 1, characterized in that, The step of selecting multiple aggregation units from the margin unit library that match the aggregation type and distribution location of the first unit as the second unit includes: The second unit is selected based on the number of times each aggregation unit in the margin unit library is executed. Multiple aggregation units that match the aggregation type and distribution position of the first unit are selected from the margin unit library as the second unit. Before issuing the second scheduling instruction to the second unit to execute the power deviation threshold, the process includes: Obtain historical execution data for each second unit, and calculate the power fluctuation value for each second unit based on the historical execution data, unit characteristics, and the power deviation threshold. Based on the power fluctuation value and the previous execution power, the current execution power of each second unit is determined, so that the sum of the current execution power of each second unit matches the power deviation threshold. The second scheduling instruction is formed based on the current execution power of each second unit.

5. The virtual power plant task scheduling processing method according to claim 4, characterized in that, The historical execution data includes historical execution power and corresponding historical response deviation. The unit features include real-time power consumption and unit power constraint parameters. The calculation of the power fluctuation value for each second unit based on the historical execution data, unit features, and the power deviation threshold includes: Calculate the response deviation weighting factor based on the historical response deviation in historical execution data; The basic fluctuation amplitude is calculated based on the real-time occupied power and unit power constraint parameters; Based on the response deviation weighting factor, the basic fluctuation amplitude, and the preset influence weight, the initial fluctuation value of each second unit is calculated. Based on the initial fluctuation value of each second unit and the historical execution power of the previous time, the initial sum of the current execution power of all second units is calculated; Based on the ratio of the initial sum to the power deviation threshold, the initial fluctuation value of each second unit is scaled to calculate the power fluctuation value of each second unit.

6. The virtual power plant task scheduling processing method according to claim 1, characterized in that, The first scheduling instruction contains the response target value of each first unit, the second scheduling instruction contains the response target value of each second unit, the response result of the first scheduling instruction contains the execution value of each first unit, and the response result of the second scheduling instruction contains the execution value of each second unit. The step of adjusting the first unit that does not meet the first condition to the margin unit library and adjusting the second unit that meets the second condition to the adjustment unit library based on the response results of the first scheduling instruction and the second scheduling instruction includes: Based on the first scheduling instruction and its response result, the single response deviation of each first unit is calculated. The first comprehensive response deviation of the first unit is calculated based on the single response deviation of the first unit and the historical response deviation in the historical execution data of the first unit; The first unit that does not meet the requirement of the first comprehensive response deviation being less than the first preset deviation will be adjusted to the margin unit library; Based on the second scheduling instruction and its response result, the single response deviation of each second unit is calculated. The second comprehensive response deviation of the second unit is calculated based on the single response deviation of the second unit and the historical response deviation in the historical execution data of the second unit. The second unit that meets the requirement that the second comprehensive response deviation is less than the second preset deviation and the number of executions is greater than or equal to the preset number threshold will be adjusted to the adjustment unit library.

7. The virtual power plant task scheduling processing method according to claim 1, characterized in that, After receiving the task to be scheduled, the following is also included: Obtain historical deviation records from the margin unit library and input these records into a Bayesian estimator to obtain the posterior deviation distribution output by the Bayesian estimator. The margin power is determined based on the posterior bias distribution and the power bias threshold. The second scheduling instruction issued to the second unit to execute the power deviation threshold includes: A second scheduling instruction is issued to the second unit to execute the margin power, so that the total power output by the second unit matches the power deviation threshold.

8. The virtual power plant task scheduling processing method according to claim 7, characterized in that, The step of obtaining historical deviation records of the margin unit library and inputting these historical deviation records into a Bayesian estimator to obtain the posterior deviation distribution output by the Bayesian estimator includes: Obtain historical deviation records and historical covariate information from the margin unit library, wherein the historical covariate information includes at least one of weather type and holiday type; Obtain the current covariate information corresponding to the historical covariate information; The historical bias records, the historical covariate information, and the current covariate information are input into the Bayesian estimator to obtain the posterior bias distribution output by the Bayesian estimator.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the virtual power plant task scheduling processing method as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The storage medium stores executable instructions, which, when executed by a processor, cause the processor to perform the virtual power plant task scheduling processing method as described in any one of claims 1 to 8.

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