Virtual power plant resource scheduling strategy optimization system and method and medium

Abstract

The invention discloses a virtual power plant resource scheduling strategy optimization system, a virtual power plant resource scheduling strategy optimization method and a medium, mainly relates to the technical field of virtual power plants, and is used for solving the problems that an existing virtual power plant lacks systematic scheduling of resource scheduling control instructions, and when project data changes greatly, the scheduling control instructions cannot be systematically scheduled. The problem that an existing scheduling scheme cannot effectively fit new project data is solved. Comprising an aggregation resource information acquisition module used for acquiring device data of a source network load storage device corresponding to a power user and issuing a device control instruction to the source network load storage device; the scheduling control strategy selection module is used for determining a corresponding scheduling control strategy and prediction equipment data from the control strategy database; the scheduling control instruction execution module is used for issuing a scheduling instruction associated with the scheduling control strategy to the corresponding source network load storage equipment; and the scheduling control strategy optimization module is used for generating an equipment control instruction when the real-time equipment data is inconsistent with the predicted equipment data, and issuing the equipment control instruction to the corresponding source network load storage equipment.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plants, and in particular to a virtual power plant resource scheduling strategy optimization system, method and medium. Background Art

[0002] With the gradual normalization of the electricity spot market, the current virtual power plant is in the transition stage from invitation-based to market-based. The virtual power plant aggregate resource dispatch strategy optimization device is based on the aggregate resource data collection and response capability evaluation and analysis, combined with the power generation plan of the virtual power plant, summarizing experience methods, multi-resource coordination, formulating scientific dispatch strategies and having self-learning optimization capabilities, forming accurate and effectively executable dispatch instructions.

[0003] In the existing technology, the scheduling process of aggregated resources of virtual power plants is mainly: issuing day-ahead clearing scheduling instructions, which are executed to specific resources through manual processing or system automatic control methods. The execution effect is reflected in the deviation assessment of the virtual power plant, but there is a lack of systematic scheduling of resource scheduling control instructions; in addition, the existing scheduling system is mainly applicable to one type of project data. When the project data undergoes major changes, the existing scheduling plan cannot effectively fit the new project data. Summary of the invention

[0004] In response to the above-mentioned deficiencies in the prior art, the present application provides a virtual power plant resource scheduling strategy optimization system, method and medium to solve the problem that the existing virtual power plants lack systematic scheduling of resource scheduling control instructions and the existing scheduling schemes cannot effectively adapt to new project data when there are major changes in project data.

[0005] In a first aspect, the present application provides a virtual power plant resource scheduling strategy optimization system, the system comprising: The aggregate resource information collection module is used to collect the equipment data of the source grid load storage equipment corresponding to the power user and store it in the comprehensive database, and issue equipment control instructions to the source grid load storage equipment based on the equipment data collected initially; A dispatching control strategy selection module is used to obtain a power generation plan of a virtual power plant, determine the planned equipment data included in the power generation plan as predicted equipment data based on the power generation plan of the virtual power plant, and determine a corresponding dispatching control strategy from a control strategy database; A dispatching control instruction execution module is used to send dispatching instructions associated with the dispatching control strategy to the corresponding source, grid, load and storage equipment; The dispatching control strategy optimization module is used to obtain real-time equipment data from the comprehensive database. When the real-time equipment data is inconsistent with the predicted equipment data, it generates equipment control instructions and sends them to the corresponding source, grid, load and storage equipment.

[0006] In one implementation of the present application, the scheduling control strategy selection module includes a scheduling control strategy database, A number of scheduling control strategies are preset in the scheduling control strategy database; Among them, the dispatching control strategy is to control the operation mode of new energy power generation equipment, energy storage system, charging pile, air-conditioning system, lighting system, as well as the production scheduling plan to control the production load.

[0007] In one implementation of the present application, the dispatch control strategy database includes a normal operating mode, The normal operation mode at least includes: controlling the new energy power generation equipment to operate according to active power first, the energy storage system to charge in valleys and discharge in peaks, the charging piles, air conditioning system, and production load scheduling are all operated according to the preset conventional mode, and the lighting system is turned on 100% according to the current demand; The dispatch control strategy database includes an economic operation mode, and the economic operation mode at least includes: controlling the new energy power generation equipment to operate according to active power first, the energy storage system to charge during valley hours and discharge during peak hours, the charging piles to charge according to the power limit during peak and valley hours, the air conditioning system to operate according to the preset economic mode, the production load scheduling plan to be arranged during valley hours, and the lighting system to be turned on according to 75% of the current demand; The dispatch control strategy database includes zero-carbon operation mode, The zero-carbon operation mode at least includes: controlling the new energy power generation equipment to operate according to the active power first, obtaining the daily power consumption, calculating the charging and discharging time period based on the daily power consumption, and then issuing the charging and discharging instructions of the energy storage system within the charging and discharging time period, and determining whether to limit the charging pile to limited power charging according to the difference between the daily power consumption and the preset conventional power consumption, determining the operating temperature of the air-conditioning system according to the daily power consumption and determining whether to arrange the production load scheduling plan, and determining the degree of opening of the lighting system according to the relationship between the daily power consumption and the preset lighting demand.

[0008] In one implementation of the present application, the aggregate resource information collection module includes a control instruction issuing unit, Used to determine the corresponding device control instruction according to the device adjustment interval corresponding to the device data; Among them, there is a preset corresponding relationship between the device control instruction and the device adjustment interval.

[0009] In one implementation of the present application, the scheduling control strategy selection module includes a scheduling control strategy selection unit, Used to read the project text corresponding to the power generation plan of the virtual power plant, identify the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant, and determine the corresponding dispatch control strategy and forecast equipment data from the control strategy database; Among them, there is a preset correspondence between the keywords and the scheduling control strategy and the predicted equipment data.

[0010] In one implementation of the present application, the scheduling control strategy optimization module includes a scheduling control strategy optimization unit, When the real-time device data is inconsistent with the predicted device data, the device control instructions are determined based on the data differences and the preset correspondence between the preset differences and the device control instructions, and sent to the corresponding source, grid, load and storage devices.

[0011] In a second aspect, the present application provides a method for optimizing a virtual power plant resource scheduling strategy, the method comprising: Collect the equipment data of the source-grid-load-storage equipment corresponding to the power user and store it in the comprehensive database, and issue equipment control instructions to the source-grid-load-storage equipment based on the equipment data collected initially; Obtaining a power generation plan of a virtual power plant, determining, based on the power generation plan of the virtual power plant, planned equipment data included in the power generation plan as forecasted equipment data, and determining a corresponding dispatching control strategy from a control strategy database; Send the dispatching instructions associated with the dispatching control strategy to the corresponding source, grid, load and storage equipment; Acquire real-time equipment data from the comprehensive database. When the real-time equipment data is inconsistent with the predicted equipment data, generate equipment control instructions and send them to the corresponding source, grid, load and storage equipment.

[0012] In one implementation of the present application, based on the power generation plan of the virtual power plant, determining the planned equipment data included in the power generation plan as the predicted equipment data, and determining the corresponding dispatching control strategy from the control strategy database specifically includes: Read the project text corresponding to the power generation plan of the virtual power plant, identify the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant, and determine the corresponding dispatch control strategy and forecast equipment data from the control strategy database; Among them, there is a preset correspondence between the keywords and the scheduling control strategy and the predicted equipment data.

[0013] In one implementation of the present application, when the real-time device data is inconsistent with the predicted device data, a device control instruction is generated and sent to the corresponding source grid load storage device, specifically including: When the real-time device data is inconsistent with the predicted device data, the device control instruction is determined based on the difference in the data and the preset correspondence between the preset difference and the device control instruction, and is sent to the corresponding source grid load storage device.

[0014] In a third aspect, the present application provides a non-volatile computer storage medium having computer instructions stored thereon, which, when executed, implement a virtual power plant resource scheduling strategy optimization method such as any one of the above items.

[0015] Those skilled in the art can understand that the present application has at least the following beneficial effects: The present application provides a virtual power plant resource scheduling strategy optimization system, method and medium. Through the scheduling control strategy optimization module, the real-time equipment data in the comprehensive database is obtained and compared with the predicted equipment data. When the real-time equipment data is inconsistent with the predicted equipment data, an equipment control instruction is generated and sent to the corresponding source network load storage device. By real-time monitoring of equipment data, the deviation between the prediction and the actual is promptly discovered and corrected to ensure the effectiveness and accuracy of the scheduling control strategy. During the entire adjustment process, all equipment data is systematically obtained, and then the overall equipment control instructions are generated, which solves the problem of the lack of systematic scheduling of resource scheduling control instructions in existing virtual power plants. In addition, the scheduling control strategy selection module in the present application can determine the corresponding scheduling control strategy and predicted equipment data from the control strategy database based on the current virtual power plant's power generation plan. When the project data changes significantly, the present application can correct the scheduling control strategy and predicted equipment data, thereby solving the problem that the scheduling plan cannot effectively fit the new project data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the internal structure of a virtual power plant resource scheduling strategy optimization system provided in an embodiment of the present application.

[0018] Figure 2 It is a logic flow chart of the economic operation mode in the optimization of the resource scheduling strategy of a virtual power plant provided in an embodiment of the present application.

[0019] Figure 3 It is a logic flow chart of a zero-carbon operation mode in the optimization of a virtual power plant resource scheduling strategy provided in an embodiment of the present application.

[0020] Figure 4 It is a flow chart of a virtual power plant resource scheduling strategy optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0022] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] This application Figure 1 A virtual power plant resource scheduling strategy optimization system is provided in the embodiment of the present application. Figure 1 As shown, the system provided in the embodiment of the present application mainly includes: The aggregate resource information collection module 110 is used to collect the equipment data of the source grid load storage equipment corresponding to the power user and store it in the comprehensive database, and issue equipment control instructions to the source grid load storage equipment based on the equipment data collected initially.

[0025] It should be noted that the equipment data of source-grid-load-storage equipment includes: operation status data, power generation data, power consumption data, etc., which are stored in the comprehensive database and control instructions are issued. The collection and control methods of source-grid-load-storage equipment include system integration collection, direct connection of equipment and installation of sensor equipment. For example, the real-time data interface is developed with the photovoltaic operation and maintenance platform of the photovoltaic system to collect data, access the photovoltaic inverter equipment, collect power consumption data by installing aggregation gateways, infrared meter readers, etc., and install control switches such as air conditioners. Distributed photovoltaic systems need to collect power generation and electricity and issue power generation control strategies. Distributed wind power collects power generation and electricity and issues power generation control strategies. The distribution system collects grid connection points and load data. The charging pile system collects charging pile load data and issues charging pile control strategies. The air conditioning system collects air conditioning load data and issues air conditioning control strategies. The lighting system collects lighting load data and issues lighting control strategies. The production load collects production load data and issues production power control strategies. The energy storage system collects energy storage operation modes and charging and discharging power and electricity and issues charging and discharging control strategies.

[0026] In addition, the aggregate resource information collection module 110 includes a control instruction issuing unit, which can determine the corresponding device control instruction according to the device adjustment interval corresponding to the device data; wherein there is a preset corresponding relationship between the device control instruction and the device adjustment interval.

[0027] The dispatching control strategy selection module 120 is used to obtain the power generation plan of the virtual power plant, determine the planned equipment data included in the power generation plan as predicted equipment data based on the power generation plan of the virtual power plant, and determine the corresponding dispatching control strategy from the control strategy database.

[0028] It should be noted that the dispatching control strategy selection module 120 includes a dispatching control strategy database, in which several dispatching control strategies are preset; among them, the dispatching control strategy is to control the operation mode of new energy power generation equipment, energy storage system, charging pile, air conditioning system, lighting system, and control the production load scheduling. In addition, the dispatching control strategy includes at least normal operation mode, economic operation mode, power-limited operation mode, power-limited operation mode, microgrid operation mode, and zero-carbon operation mode. Specifically, as shown in the following table: Dispatch control strategy database table:

[0029] As an example, the economic operation mode (the logical flow of the economic operation mode is shown in Figure 2 ) Combined with the peak and valley periods of electricity prices, control the new energy generation, energy storage, and controllable loads, and issue instructions after calculation. Power limit operation mode, power limit operation mode, microgrid operation mode, zero carbon operation mode (zero carbon operation mode logic flow see Figure 3 ) is mainly used to check the power and electricity of the grid-connected points, control the new energy power generation, energy storage, and controllable loads, and issue instructions after calculating according to the target values.

[0030] In addition, the solution for determining the corresponding strategy may be: Through the dispatching control strategy selection unit in the dispatching control strategy selection module 120, the project text corresponding to the power generation plan of the virtual power plant is read, the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant are identified, and the corresponding dispatching control strategy and prediction equipment data are determined from the control strategy database; wherein, there is a preset corresponding relationship between the keywords and the dispatching control strategy and prediction equipment data.

[0031] The dispatching control instruction execution module 130 is used to send the dispatching instructions associated with the dispatching control strategy to the corresponding source grid load storage equipment.

[0032] As an example, the dispatching control strategy selection module 120 receives the dispatching instruction. The received dispatching instruction is parsed to clarify the key information such as the target device of the instruction, the operation type (such as start, stop, adjust power, etc.), and the operation time. According to the parsed instruction information, the corresponding source grid load storage device is searched and matched from the comprehensive database. This usually involves matching the device identification (such as device ID, device type, etc.). The dispatching instruction is sent to the matching source grid load storage device through a communication interface (such as a network, wireless signal, etc.). This may involve interaction with the device control system to ensure that the instruction can be correctly received and executed by the device.

[0033] The dispatch control strategy optimization module 140 is used to obtain real-time equipment data in the comprehensive database. When the real-time equipment data is inconsistent with the predicted equipment data, it generates equipment control instructions and sends them to the corresponding source, grid, load and storage equipment.

[0034] Among them, the scheduling control strategy optimization module 140 includes a scheduling control strategy optimization unit, which can determine the device control instructions from the preset correspondence between the preset differences and the device control instructions according to the differences in the data when the real-time device data is inconsistent with the predicted device data, and send them to the corresponding source network load storage equipment.

[0035] It can be understood by those skilled in the art that the dispatching control strategy optimization unit first monitors the real-time equipment data in the comprehensive database. These data reflect the current operating status of the source, grid, load and storage equipment, including but not limited to key indicators such as power output, energy consumption, and load demand. When there is a difference between the real-time equipment data and the predicted equipment data, the dispatching control strategy optimization unit will immediately start the response mechanism. This difference may be caused by various factors, such as weather changes, equipment failures, load fluctuations, etc. Then, the dispatching control strategy optimization unit will find the appropriate control instructions from the correspondence between the preset differences and the equipment control instructions according to the differences in the data. This correspondence is pre-established based on historical data, expert experience, and machine learning algorithms, aiming to ensure that the corresponding control instructions can be generated quickly and accurately under various differences. Finally, the dispatching control strategy optimization unit will send the determined equipment control instructions to the corresponding source, grid, load and storage equipment. These instructions are intended to adjust the operating status of the equipment to eliminate or reduce the difference between the real-time data and the predicted data, thereby optimizing the resource scheduling and energy utilization efficiency of the entire virtual power plant.

[0036] In addition, the embodiment provides a virtual power plant resource scheduling strategy optimization method, such as Figure 4 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 210: collect the equipment data of the source-grid-load-storage equipment corresponding to the power user and store it in the integrated database, and issue equipment control instructions to the source-grid-load-storage equipment based on the equipment data collected initially.

[0037] Step 220: Obtain a power generation plan of the virtual power plant, determine that the planned equipment data included in the power generation plan is predicted equipment data based on the power generation plan of the virtual power plant, and determine a corresponding dispatching control strategy from a control strategy database.

[0038] Based on the power generation plan of the virtual power plant, the planned equipment data included in the power generation plan is determined as the predicted equipment data, and the corresponding dispatch control strategy is determined from the control strategy database, specifically including: Read the project text corresponding to the power generation plan of the virtual power plant, identify the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant, and determine the corresponding dispatch control strategy and forecast equipment data from the control strategy database; Among them, there is a preset correspondence between the keywords and the scheduling control strategy and the predicted equipment data.

[0039] Step 230: Send the dispatching instructions associated with the dispatching control strategy to the corresponding source grid load storage equipment.

[0040] Step 240: Acquire real-time device data in the comprehensive database. When the real-time device data is inconsistent with the predicted device data, generate device control instructions and send them to the corresponding source, grid, load and storage devices.

[0041] In some embodiments, when the real-time device data is inconsistent with the predicted device data, a device control instruction is generated and sent to the corresponding source grid load storage device, specifically including: When the real-time device data is inconsistent with the predicted device data, the device control instruction is determined based on the difference in the data and the preset correspondence between the preset difference and the device control instruction, and is sent to the corresponding source grid load storage device.

[0042] In addition, an embodiment of the present application further provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, a virtual power plant resource scheduling strategy optimization method as described above is implemented.

[0043] So far, the technical solutions of the present disclosure have been described in combination with the above multiple embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without departing from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-mentioned various embodiments, and can also make equivalent changes or replacements to the relevant technical features. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principle of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. A virtual power plant resource scheduling strategy optimization system, characterized in that: The system comprises: The aggregate resource information collection module is used to collect the equipment data of the source grid load storage equipment corresponding to the power user and store it in the comprehensive database, and issue equipment control instructions to the source grid load storage equipment based on the equipment data collected initially; A dispatching control strategy selection module is used to obtain a power generation plan of a virtual power plant, determine the planned equipment data included in the power generation plan as predicted equipment data based on the power generation plan of the virtual power plant, and determine a corresponding dispatching control strategy from a control strategy database; A dispatch control instruction execution module is used to send dispatch instructions associated with the dispatch control strategy to the corresponding source, grid, load and storage equipment; The dispatching control strategy optimization module is used to obtain real-time equipment data from the comprehensive database. When the real-time equipment data is inconsistent with the predicted equipment data, it generates equipment control instructions and sends them to the corresponding source, grid, load and storage equipment.

2. The virtual power plant resource scheduling strategy optimization system according to claim 1, characterized in that: The dispatch control strategy selection module includes a dispatch control strategy database, A number of scheduling control strategies are preset in the scheduling control strategy database; Among them, the dispatching control strategy is to control the operation mode of new energy power generation equipment, energy storage system, charging pile, air-conditioning system, lighting system, as well as the production scheduling plan to control the production load.

3. The virtual power plant resource scheduling strategy optimization system according to claim 2 is characterized in that: The dispatch control strategy database includes normal operation mode, The normal operation mode at least includes: controlling the new energy power generation equipment to operate according to active power first, the energy storage system to charge in valleys and discharge in peaks, the charging piles, air conditioning system, and production load scheduling are all operated according to the preset conventional mode, and the lighting system is turned on 100% according to the current demand; The dispatch control strategy database includes economic operation mode, The economic operation mode at least includes: controlling the new energy power generation equipment to operate according to active power first, the energy storage system to charge during valley hours and discharge during peak hours, charging piles to charge according to power limits during peak and valley hours, the air conditioning system to operate according to the preset economic mode, arranging the production load scheduling during valley hours, and the lighting system to be turned on at 75% of the current demand; The dispatch control strategy database includes zero-carbon operation mode, The zero-carbon operation mode at least includes: controlling the new energy power generation equipment to operate according to the active power first, obtaining the daily power consumption, calculating the charging and discharging time period based on the daily power consumption, and then issuing the charging and discharging instructions of the energy storage system within the charging and discharging time period, and determining whether to limit the charging pile to limited power charging according to the difference between the daily power consumption and the preset conventional power consumption, determining the operating temperature of the air-conditioning system according to the daily power consumption and determining whether to arrange the production load scheduling plan, and determining the degree of opening of the lighting system according to the relationship between the daily power consumption and the preset lighting demand.

4. The virtual power plant resource scheduling strategy optimization system according to claim 1, characterized in that: The aggregate resource information collection module includes a control instruction issuing unit, Used to determine the corresponding device control instruction according to the device adjustment interval corresponding to the device data; Among them, there is a preset corresponding relationship between the device control instruction and the device adjustment interval.

5. The virtual power plant resource scheduling strategy optimization system according to claim 1, characterized in that: The dispatch control strategy selection module includes a dispatch control strategy selection unit, Used to read the project text corresponding to the power generation plan of the virtual power plant, identify the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant, and determine the corresponding dispatch control strategy and forecast equipment data from the control strategy database; There is a preset corresponding relationship between the keywords, the dispatch control strategy and the predicted equipment data, and the planned equipment data included in the power generation plan is the predicted equipment data.

6. The virtual power plant resource scheduling strategy optimization system according to claim 1, characterized in that: The dispatch control strategy optimization module includes a dispatch control strategy optimization unit. When the real-time device data is inconsistent with the predicted device data, the device control instructions are determined based on the data differences and the preset correspondence between the preset differences and the device control instructions, and sent to the corresponding source, grid, load and storage devices.

7. A virtual power plant resource scheduling strategy optimization method, characterized in that: The method comprises: Collect the equipment data of the source-grid-load-storage equipment corresponding to the power user and store it in the comprehensive database, and issue equipment control instructions to the source-grid-load-storage equipment based on the equipment data collected initially; Obtaining a power generation plan of a virtual power plant, determining, based on the power generation plan of the virtual power plant, planned equipment data included in the power generation plan as forecasted equipment data, and determining a corresponding dispatching control strategy from a control strategy database; Send the dispatching instructions associated with the dispatching control strategy to the corresponding source, grid, load and storage equipment; Acquire real-time equipment data from the comprehensive database. When the real-time equipment data is inconsistent with the predicted equipment data, generate equipment control instructions and send them to the corresponding source, grid, load and storage equipment.

8. The virtual power plant resource scheduling strategy optimization method according to claim 7, characterized in that: Based on the power generation plan of the virtual power plant, the planned equipment data included in the power generation plan is determined as the predicted equipment data, and the corresponding dispatch control strategy is determined from the control strategy database, specifically including: Read the project text corresponding to the power generation plan of the virtual power plant, identify the keywords corresponding to the preset indicator data in the power generation plan of the virtual power plant, and determine the corresponding dispatch control strategy and forecast equipment data from the control strategy database; Among them, there is a preset correspondence between the keywords and the scheduling control strategy and the predicted equipment data.

9. The virtual power plant resource scheduling strategy optimization method according to claim 7, characterized in that: When the real-time device data is inconsistent with the predicted device data, a device control instruction is generated and sent to the corresponding source, grid, load and storage device, including: When the real-time device data is inconsistent with the predicted device data, the device control instruction is determined based on the difference in the data and the preset correspondence between the preset difference and the device control instruction, and is sent to the corresponding source grid load storage device.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, they implement a virtual power plant resource scheduling strategy optimization method as described in any one of claims 7-9.

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