Virtual power plant resource scheduling method and device, computer equipment and storage medium

Abstract

The invention relates to a virtual power plant resource scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a first evaluation model and operation state information of a plurality of resource interaction main bodies in a virtual power plant; wherein the resource interaction main body comprises a static electricity utilization main body and a dynamic electricity utilization main body, and the first evaluation model is used for predicting operation power data of the virtual power plant according to operation state information of the static electricity utilization main body; aiming at each dynamic power consumption main body, extracting power characteristic data from the corresponding operation state information; correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model; and inputting the operation state information corresponding to the plurality of resource interaction main bodies into a second evaluation model to obtain target operation power data of the virtual power plant, and adjusting the operation state of the dynamic power utilization main body according to the target operation power data. The method can improve the energy stability of the virtual power plant.

Description

Technical Field

[0001] The present application relates to the technical field of new energy scheduling, and in particular to a virtual power plant resource scheduling method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] As global energy transformation and climate change response strategies advance, virtual power plants, as an innovative energy management system, are gradually becoming an important development direction for the power industry. As the penetration rate of renewable energy increases, its participation rate in the power market is gradually increasing. However, distributed renewable energy often has characteristics such as uncertainty and volatility that are difficult to control, which is not conducive to the stability of the power grid.

[0003] At present, relevant technologies uniformly calculate the input and output of renewable energy power generation systems, gas turbines and other resources in virtual power plants to control the stability of virtual power plants. However, this method is not sufficient to meet the demand for the stability of virtual power plants. Summary of the invention

[0004] Based on this, it is necessary to provide a virtual power plant resource scheduling method, device, computer equipment, computer-readable storage medium and computer program product that can improve the energy stability of virtual power plants in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a virtual power plant resource scheduling method, comprising:

[0006] Acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0007] For each of the dynamic electricity users, extract power characteristic data from the corresponding operating status information;

[0008] Correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0009] The operating status information corresponding to the multiple resource interaction entities is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic electricity user entity is adjusted according to the target operating power data.

[0010] In one embodiment, the dynamic electricity consumer includes an electric hydrogen system; and adjusting the operating state of the dynamic electricity consumer according to the target operating power data includes:

[0011] Obtaining an electric hydrogen scheduling evaluation model, and determining an electric hydrogen scheduling constraint condition according to current operating status information of the electric hydrogen system;

[0012] According to the electric hydrogen scheduling constraint conditions, the electric hydrogen scheduling evaluation model is used to calculate the dispatchable power data of the electric hydrogen system;

[0013] The operating state of the electric hydrogen system is adjusted according to the target operating power data and the dispatchable power data.

[0014] In one embodiment, adjusting the operating state of the electric hydrogen system according to the target operating power data and the dispatchable power data includes:

[0015] Calculating power deviation data according to the target operating power data; wherein the power deviation data is used to characterize that the virtual power plant is currently in a power overflow state or a power shortage state;

[0016] When the dispatchable power data is greater than the power deviation data and the power deviation data indicates a power shortage state, controlling the electric hydrogen system to reduce hydrogen production operations according to the power deviation data;

[0017] When the dispatchable power data is greater than the power deviation data and the power deviation data is characterized by a power overflow state, the electric hydrogen system is controlled to increase hydrogen production operations according to the power deviation data.

[0018] In one embodiment, the dynamic power consumption entity further includes a mobile power consumption entity cluster; and adjusting the operating state of the dynamic power consumption entity according to the target operating power data further includes:

[0019] When the dispatchable power data is less than the power deviation data, determining a load adjustment area according to the location of the static power consumer;

[0020] The charging state of the mobile electricity-consuming entity cluster within the load adjustment area is adjusted according to the power deviation data.

[0021] In one embodiment, adjusting the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data includes:

[0022] Counting the power deviation data within a preset time period and calculating the data change speed of the power deviation data;

[0023] When the data change speed is greater than or equal to a preset fluctuation threshold, adjusting the charging time of the mobile power-consuming entity cluster in the load adjustment area;

[0024] When the data change speed is less than a preset fluctuation threshold, the charging rate of the mobile electricity-consuming entity cluster within the load adjustment area is adjusted.

[0025] In one embodiment, adjusting the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data includes:

[0026] When the power deviation data indicates a power shortage state, reducing the charging amount of the mobile power-consuming entity cluster;

[0027] When the dispatchable power data is less than the power deviation data and the power deviation data indicates a power overflow state, the charging amount of the mobile power-consuming entity cluster is increased.

[0028] In a second aspect, the present application also provides a virtual power plant resource scheduling device, comprising:

[0029] An information acquisition module, used to acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0030] A data analysis module, configured to extract power characteristic data from the corresponding operating status information for each of the dynamic power users;

[0031] A data processing module, used for correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0032] A state adjustment module is used to input the operating state information corresponding to the multiple resource interaction entities into the second evaluation model, obtain the target operating power data of the virtual power plant, and adjust the operating state of the dynamic power user according to the target operating power data.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] Acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0035] For each of the dynamic electricity users, extract power characteristic data from the corresponding operating status information;

[0036] Correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0037] The operating status information corresponding to the multiple resource interaction entities is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic electricity user entity is adjusted according to the target operating power data.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0039] Acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0040] For each of the dynamic electricity users, extract power characteristic data from the corresponding operating status information;

[0041] Correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0042] The operating status information corresponding to the multiple resource interaction entities is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic electricity user entity is adjusted according to the target operating power data.

[0043] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0044] Acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0045] For each of the dynamic electricity users, extract power characteristic data from the corresponding operating status information;

[0046] Correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0047] The operating status information corresponding to the multiple resource interaction entities is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic electricity user entity is adjusted according to the target operating power data.

[0048] The above-mentioned virtual power plant resource scheduling method, device, computer equipment, storage medium and computer program product obtain a first evaluation model for predicting the operating power data of the virtual power plant based on the operating status information of the static power user and the operating status information of multiple resource interaction subjects in the virtual power plant; wherein the resource interaction subjects include static power users and dynamic power users, and the operating status information of the dynamic power users can be dynamically changed through adjustment. Therefore, for each dynamic power user, power characteristic data is extracted from the corresponding operating status information respectively, and the first evaluation model is corrected according to the power characteristic data, so that a more flexible second evaluation model can be obtained. Finally, the operating status information corresponding to the multiple resource interaction subjects is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic power users is adjusted according to the target operating power data, so that the dynamic power users can be considered within the state model of the virtual power plant, and the energy stability of the virtual power plant is improved by adjusting the operating status of the dynamic power users. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 An application environment diagram of a virtual power plant resource scheduling method in one embodiment;

[0051] Figure 2 A schematic diagram of a flow chart of a virtual power plant resource scheduling method in an embodiment;

[0052] Figure 3 A flowchart of step S208 of a virtual power plant resource scheduling method in one embodiment;

[0053] Figure 4 It is a structural block diagram of a virtual power plant resource scheduling device in one embodiment;

[0054] Figure 5 is an internal structure diagram of a computer device in one embodiment;

[0055] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in another embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The virtual power plant resource scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network, and the terminal 102 can be used to collect the operating status information of each resource interaction subject in the virtual power plant, and send the operating status information to the server 104 for analysis and processing. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The data storage system can be used to store the operating status information of multiple dynamic power users and static power users in the virtual power plant. Among them, the terminal 102 can be, but is not limited to, various sensors, monitors, image processing devices, including various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0058] In an exemplary embodiment, Figure 2 As shown, a virtual power plant resource scheduling method is provided, and the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S202 to S208. Among them:

[0059] Step S202: obtaining the first evaluation model and operating status information of multiple resource interaction entities in the virtual power plant.

[0060] The resource interaction subjects include static power consumers and dynamic power consumers. Dynamic power consumers refer to power consumers whose output power can be dynamically adjusted to the target value within a relatively short preset time; static power consumers refer to power consumers whose output power cannot be dynamically adjusted to the target value within a relatively short preset time. The first evaluation model is used to predict the operating power data of the virtual power plant based on the operating status information of the static power consumers.

[0061] Exemplarily, the static electricity users may include a distributed renewable energy power generation system, a demand-side response electricity user, a gas turbine, and an energy storage system. The server 104 may obtain a first evaluation model established according to the electricity consumption characteristics of the static electricity users:

[0062] Distributed renewable energy generation systems refer to small-scale power generation systems installed near users or near electricity consumption locations. These systems usually include photovoltaic and wind power generation facilities. The distributed renewable energy in this example consists of photovoltaic and wind power generation, and its model is as follows:

[0063]

[0064]

[0065] in, and are the actual output and predicted maximum output of wind turbine i; and is the actual output and predicted maximum output of PV unit i; C NE is the total resource value consumption curtailed by renewable energy; γ W and γ PV are the power limit penalty factors for wind power and photovoltaic power respectively; N W and N PV are the number of wind turbines and photovoltaic units, respectively.

[0066] For demand-side response electricity users, it is used to encourage electricity users to reduce or delay electricity consumption during peak hours of the power grid through incentives, so as to balance the supply and demand of the power grid. Loads that can participate in demand response include industrial and commercial users, residential users, etc. Industrial and commercial users usually have large electricity loads and can reduce electricity consumption by adjusting production plans, using high-efficiency equipment, or temporarily shutting down non-essential equipment. Residential users can participate in demand response by adjusting the use time of household appliances (such as washing machines, dryers, air conditioners, etc.). The model is as follows:

[0067]

[0068] Among them, C DR is the total resource value consumed by demand response; N DR is the number of flexible loads; and is the resource value consumption coefficient of demand response, t represents the usage time, t=1,2,…,T,D i,t is the load change of flexible load i.

[0069] For gas turbines, they are heat engines that convert the chemical energy of fuel into useful work. Due to their fast start and stop speed, they can be used for backup needs. In addition to the resource value consumption of coal consumption, the resource value consumption of carbon dioxide emissions should also be considered. The model is as follows:

[0070]

[0071]

[0072] Among them, C GT and C GT,CO2 They are the total coal consumption resource value consumption and CO2 emission resource value consumption; N GT is the number of gas turbines; and is the resource value consumption coefficient of coal consumption; is the power generation of gas turbine i; t represents the usage time, t=1,2,…,T, and c GT are the CO2 emission coefficient and its related resource value consumption coefficient; R i GT,up and R i GT,down is the maximum up and down ramp rate of gas turbine i.

[0073] For energy storage systems, they can store excess energy when renewable energy generation exceeds demand, and release this energy when production is insufficient, thereby improving the utilization of renewable energy and the overall energy efficiency of the power grid. The model is as follows:

[0074]

[0075] In the formula, C SE is the total resource value consumption of energy storage equipment; N SE is the number of energy storage devices; SE is the resource value consumption coefficient of energy storage; t represents the usage time, t=1,2,…,T, is the charge and discharge power of the energy storage device, SOC i,t is the energy level; η c and η d is the charging and discharging efficiency.

[0076] Step S204: for each dynamic electricity user, extract power characteristic data from the corresponding operating status information.

[0077] Among them, the dynamic electricity-using subject may include an electric hydrogen system. The electric hydrogen system consists of an electrolyzer, a compressor and a hydrogen tank. It uses the electrolysis process to decompose water into hydrogen and oxygen, and compresses and stores the generated hydrogen for use when needed. Among them, the electrolyzer, as the core component of the electric-to-hydrogen system, is responsible for executing the electrolysis process of water. Its working principle is: a DC voltage is applied between the anode (positive electrode) and the cathode (negative electrode) of the electrolyzer, and water molecules decompose at the anode to produce oxygen and at the cathode to produce hydrogen; the role of the compressor is to compress the hydrogen produced by the electrolyzer to a certain pressure for easy storage and transportation. Hydrogen has a large volume under low pressure and is not convenient for storage and transportation. Therefore, it is necessary to increase its pressure through a compressor. The efficiency and energy consumption of the compressor directly affect the economy of the entire system; the hydrogen tank is used to store compressed hydrogen.

[0078] Exemplarily, the server 104 may extract power characteristic data based on the operating status information of the electrolyzer, compressor and hydrogen tank in the electric hydrogen system, including data such as the power constraint of the electrolyzer, the power constraint of the compressor, and the air pressure constraint of the hydrogen tank.

[0079] Step S206: modify the first evaluation model according to the power characteristic data to obtain a second evaluation model.

[0080] Exemplarily, the power constraints of the electrolyzer are as follows:

[0081]

[0082] in, is the hydrogen production of the system, is the hydrogen production rate of the system, is the power consumption of the electrolyzer, P i EL,R,max is the maximum ramp power of the electrolyzer.

[0083] The power constraints of the compressor are as follows:

[0084]

[0085] in, is the power of the compressor, is the specific heat constant of hydrogen, T in is the temperature of hydrogen injection, κ is the isentropic coefficient of hydrogen, α COM is the power of the compressor, and Q is the compression ratio of the compressor.

[0086] The air pressure constraints of the hydrogen tank are as follows:

[0087]

[0088] Prs i,1 =Prs i,T

[0089] Among them, Prs i,t is the internal pressure of the hydrogen tank, is the internal temperature of the hydrogen tank, It is the reference value of hydrogen capacity of hydrogen tank. is the molar mass of hydrogen, is the hydrogen load of the electric hydrogen system.

[0090] The operation and maintenance resource value consumption and power consumption of the electric hydrogen system are as follows:

[0091]

[0092] Step S208, inputting the operating status information corresponding to the multiple resource interaction entities into the second evaluation model, obtaining the target operating power data of the virtual power plant, and adjusting the operating status of the dynamic power consumption entity according to the target operating power data.

[0093] Exemplarily, server 104 may first obtain an electric hydrogen scheduling evaluation model, and determine the electric hydrogen scheduling constraints based on the current operating status information of the electric hydrogen system; based on the electric hydrogen scheduling constraints, use the electric hydrogen scheduling evaluation model to calculate the dispatchable power data of the electric hydrogen system; and adjust the operating status of the electric hydrogen system based on the target operating power data and the dispatchable power data.

[0094] Furthermore, the server 104 can calculate the power deviation data based on the target operating power data; wherein the power deviation data is used to characterize that the current virtual power plant is in a power overflow state or a power shortage state; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power shortage state, the electric hydrogen system is controlled to reduce the hydrogen production operation according to the power deviation data; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power overflow state, the electric hydrogen system is controlled to increase the hydrogen production operation according to the power deviation data.

[0095] Exemplarily, the server 104 can adopt a hydrogen load transfer method of load curve peak shaving and valley filling as a flexible load to optimize the operation of the power system to establish the electric hydrogen system part in the second evaluation model. Reduce the operation of water electrolysis to produce hydrogen during peak electricity demand periods to reduce the load on the power grid, and increase the electric hydrogen conversion operation during low electricity demand periods to utilize excess electricity and store hydrogen. In this way, the electric hydrogen conversion system can not only regulate its own hydrogen production, but also assist the power grid in load management and achieve the goal of peak shaving and valley filling, while the electric hydrogen system is subject to power balance constraints during operation. Its model is as follows:

[0096]

[0097]

[0098] in, is the hydrogen load after conversion; ρ t is the ideal hydrogen load; P t L,pu is the unit value of system load; L t O is the original hydrogen load. The second evaluation model to maximize the value gain of the virtual power plant is as follows:

[0099]

[0100] E VPP,NET =E VPP -C vPP

[0101]

[0102] C VPP =C P2H +C NE +C DR +C GT +C GT,CO2 +C SE +C EVch

[0103] Among them, N S is the number of typical scenarios; p s is the probability of scenario i; E VPP,NET and E VPP is the net income and total income of the virtual power plant; C VPP is the total resource value consumption of the virtual power plant; t It is the price at which the virtual power plant sells electricity to the grid.

[0104] In the second evaluation model of the virtual power plant, in addition to analyzing the constraints of the above-mentioned distributed resources themselves, the server 104 also needs to analyze the overall constraints. Among them, the system constraints may include power balance constraints, line safety constraints and spare capacity constraints, as follows:

[0105]

[0106] Among them, N L and N LINE are the system load and the number of system branches respectively; P t VPP is the power output of the virtual power plant; is the power of load i; is the transmission power of branch i, I i,t is the committed state of unit i; μ is the reserve capacity factor.

[0107] In the above-mentioned virtual power plant resource scheduling method, a first evaluation model for predicting the operating power data of the virtual power plant based on the operating status information of the static power consumer and the operating status information of multiple resource interaction subjects in the virtual power plant are obtained; wherein the resource interaction subjects include static power consumers and dynamic power consumers, and the operating status information of the dynamic power consumers can be dynamically changed through adjustment. Therefore, for each dynamic power consumer, power characteristic data is extracted from the corresponding operating status information respectively, and the first evaluation model is corrected according to the power characteristic data, so that a more flexible second evaluation model can be obtained. Finally, the operating status information corresponding to the multiple resource interaction subjects is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic power consumer is adjusted according to the target operating power data, so that the dynamic power consumer can be considered within the state model of the virtual power plant, and the energy stability of the virtual power plant can be improved by adjusting the operating status of the dynamic power consumer.

[0108] In an exemplary embodiment, Figure 3 As shown, the dynamic electricity user may also include a mobile electricity user cluster; step S208 may also include steps S302 to S304. Among them:

[0109] Step S302: when the dispatchable power data is less than the power deviation data, determine the load adjustment area according to the location of the static power user.

[0110] Among them, the mobile electricity user cluster can be an electric vehicle cluster, a mobile charging station cluster, and so on. The electric vehicle cluster can respond to demand by adjusting the charging time and charging rate. During peak electricity demand periods, virtual power plants can dispatch electric vehicles to reduce charging or delay charging, thereby reducing the load on the power grid. During low demand periods, charging can be increased to balance the load on the power grid and improve energy efficiency. The batteries of electric vehicles can also serve as energy storage devices, storing excess electricity and releasing energy when needed. This energy storage function can help virtual power plants manage the supply and demand balance of the power grid, especially when the power generation of renewable energy fluctuates, the batteries of electric vehicles can serve as buffer storage energy.

[0111] Exemplarily, when the dispatchable power data is less than the power deviation data, the server 104 can determine the locations of the static power consumers with excess and shortage power output according to the location data of the static power consumers, and then divide the area where the virtual power plant is located into multiple sub-areas according to the location of the consumers and the power output, so as to determine the load adjustment area.

[0112] Step S304: adjusting the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data.

[0113] For example, taking electric vehicles as an example, the server 104 may model the mobile electricity user as follows:

[0114]

[0115]

[0116] in, and is the charging and discharging power of electric vehicle i at time t; k i,t is a 0-1 variable, ensuring that the electric vehicle will not be charged and discharged at the same time; P EV,max is the maximum value of charge and discharge power; t i,arrival and t i,leave is the time when electric vehicle i arrives at and leaves the charging station; η evc and η evd is the charging and discharging efficiency of electric vehicles; E EV,i is the capacity of electric vehicle i; and are the upper and lower limits of the SOC (State of Charge) of electric vehicle i at time t; is the expected SOC of electric vehicle i when it leaves the charging station.

[0117] The model of the electric vehicle cluster is as follows:

[0118]

[0119] P t EV =P t EVAdch

[0120]

[0121] in, is the charging power of all electric vehicles at time t, is the discharge power of all electric vehicles at time t, is the power of the electric vehicle cluster participating in the market at time t, C EVch is the total resource value consumption of electric vehicle cluster charging, is the charging electricity price of the electric vehicle at time t.

[0122] Exemplarily, the server 104 can count the power deviation data within a preset time period and calculate the data change rate of the power deviation data; when the data change rate is greater than or equal to a preset fluctuation threshold, adjust the charging time of the mobile power user cluster in the load adjustment area; when the data change rate is less than the preset fluctuation threshold, adjust the charging rate of the mobile power user cluster in the load adjustment area.

[0123] Exemplarily, the server 104 may reduce the charge amount of the mobile power user cluster when the power deviation data indicates a power shortage state; and increase the charge amount of the mobile power user cluster when the dispatchable power data is less than the power deviation data and the power deviation data indicates a power overflow state.

[0124] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0125] Based on the same inventive concept, the embodiment of the present application also provides a virtual power plant resource scheduling device for implementing the virtual power plant resource scheduling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more virtual power plant resource scheduling device embodiments provided below can refer to the limitations of the virtual power plant resource scheduling method above, and will not be repeated here.

[0126] In an exemplary embodiment, Figure 4 As shown, a virtual power plant resource scheduling device is provided, including: an information acquisition module 402, a data analysis module 404, a data processing module 406 and a state adjustment module 408, wherein:

[0127] The information acquisition module 402 is used to acquire the first evaluation model and the operating status information of multiple resource interaction subjects in the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects;

[0128] The data analysis module 404 is used to extract power characteristic data from the corresponding operation status information for each dynamic power user;

[0129] A data processing module 406, configured to modify the first evaluation model according to the power characteristic data to obtain a second evaluation model;

[0130] The state adjustment module 408 is used to input the operating state information corresponding to multiple resource interaction entities into the second evaluation model, obtain the target operating power data of the virtual power plant, and adjust the operating state of the dynamic power consumption entity according to the target operating power data.

[0131] In one embodiment, the dynamic electricity user includes an electric hydrogen system; the state adjustment module 408 includes:

[0132] A first information acquisition unit is used to acquire an electric hydrogen scheduling evaluation model and determine an electric hydrogen scheduling constraint condition according to the current operating status information of the electric hydrogen system;

[0133] A first data processing unit is used to calculate the dispatchable power data of the electric hydrogen system using the electric hydrogen dispatch evaluation model according to the electric hydrogen dispatch constraint conditions;

[0134] The first scheduling unit is used to adjust the operating state of the electric hydrogen system according to the target operating power data and the dispatchable power data.

[0135] In one of the embodiments, the first scheduling unit is specifically used to: calculate power deviation data based on target operating power data; wherein the power deviation data is used to characterize that the current virtual power plant is in a power overflow state or a power shortage state; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power shortage state, the electric hydrogen system is controlled to reduce hydrogen production operations according to the power deviation data; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power overflow state, the electric hydrogen system is controlled to increase hydrogen production operations according to the power deviation data.

[0136] In one embodiment, the dynamic power consumption entity further includes a mobile power consumption entity cluster; the state adjustment module 408 further includes:

[0137] A second data processing unit is used to determine a load adjustment area according to the location of the static power user when the dispatchable power data is less than the power deviation data;

[0138] The second scheduling unit is used to adjust the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data.

[0139] In one of the embodiments, the second scheduling unit is specifically used to: count the power deviation data within a preset time period and calculate the data change rate of the power deviation data; when the data change rate is greater than or equal to a preset fluctuation threshold, adjust the charging time of the mobile power user cluster in the load adjustment area; when the data change rate is less than the preset fluctuation threshold, adjust the charging rate of the mobile power user cluster in the load adjustment area.

[0140] In one of the embodiments, the second scheduling unit is also used to: reduce the charging amount of the mobile power user cluster when the power deviation data is characterized by a power shortage state; and increase the charging amount of the mobile power user cluster when the schedulable power data is less than the power deviation data and the power deviation data is characterized by a power overflow state.

[0141] Each module in the above-mentioned virtual power plant resource scheduling device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0142] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as operating status information of multiple dynamic power users and static power users in a virtual power plant. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a virtual power plant resource scheduling method is implemented.

[0143] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a virtual power plant resource scheduling method is implemented.

[0144] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0145] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining a first evaluation model and operating status information of multiple resource interaction subjects in a virtual power plant; wherein the resource interaction subjects include static power users and dynamic power users, and the first evaluation model is used to predict the operating power data of the virtual power plant based on the operating status information of the static power users; for each dynamic power user, respectively extracting power characteristic data from the corresponding operating status information; correcting the first evaluation model based on the power characteristic data to obtain a second evaluation model; inputting the operating status information corresponding to the multiple resource interaction subjects into the second evaluation model to obtain the target operating power data of the virtual power plant, and adjusting the operating status of the dynamic power users based on the target operating power data.

[0146] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining an electric hydrogen scheduling evaluation model, and determining the electric hydrogen scheduling constraints based on the current operating status information of the electric hydrogen system; calculating the dispatchable power data of the electric hydrogen system using the electric hydrogen scheduling evaluation model based on the electric hydrogen scheduling constraints; and adjusting the operating status of the electric hydrogen system based on the target operating power data and the dispatchable power data.

[0147] In one embodiment, when the processor executes the computer program, the following steps are also implemented: calculating power deviation data based on target operating power data; wherein the power deviation data is used to characterize that the current virtual power plant is in a power overflow state or a power shortage state; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power shortage state, controlling the electric hydrogen system to reduce hydrogen production operations according to the power deviation data; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power overflow state, controlling the electric hydrogen system to increase hydrogen production operations according to the power deviation data.

[0148] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the dispatchable power data is less than the power deviation data, determining the load adjustment area according to the location of the static power user; and adjusting the charging status of the cluster of mobile power users within the load adjustment area according to the power deviation data.

[0149] In one embodiment, when the processor executes the computer program, the following steps are also implemented: counting the power deviation data within a preset time period and calculating the data change rate of the power deviation data; when the data change rate is greater than or equal to a preset fluctuation threshold, adjusting the charging time of the cluster of mobile power users in the load adjustment area; when the data change rate is less than the preset fluctuation threshold, adjusting the charging rate of the cluster of mobile power users in the load adjustment area.

[0150] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the power deviation data is characterized by a power shortage state, the charge amount of the cluster of mobile power users is reduced; when the schedulable power data is less than the power deviation data and the power deviation data is characterized by a power overflow state, the charge amount of the cluster of mobile power users is increased.

[0151] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining a first evaluation model and operating status information of multiple resource interaction subjects in a virtual power plant; wherein the resource interaction subjects include static power users and dynamic power users, and the first evaluation model is used to predict the operating power data of the virtual power plant based on the operating status information of the static power users; for each dynamic power user, respectively extracting power characteristic data from the corresponding operating status information; correcting the first evaluation model based on the power characteristic data to obtain a second evaluation model; inputting the operating status information corresponding to the multiple resource interaction subjects into the second evaluation model to obtain the target operating power data of the virtual power plant, and adjusting the operating status of the dynamic power users based on the target operating power data.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining an electric hydrogen scheduling evaluation model, and determining the electric hydrogen scheduling constraints based on the current operating status information of the electric hydrogen system; calculating the dispatchable power data of the electric hydrogen system using the electric hydrogen scheduling evaluation model based on the electric hydrogen scheduling constraints; and adjusting the operating status of the electric hydrogen system based on the target operating power data and the dispatchable power data.

[0153] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: calculating power deviation data based on the target operating power data; wherein the power deviation data is used to characterize that the current virtual power plant is in a power overflow state or a power shortage state; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power shortage state, controlling the electric hydrogen system to reduce hydrogen production operations according to the power deviation data; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power overflow state, controlling the electric hydrogen system to increase hydrogen production operations according to the power deviation data.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the dispatchable power data is less than the power deviation data, determining the load adjustment area according to the location of the static power user; adjusting the charging status of the cluster of mobile power users within the load adjustment area according to the power deviation data.

[0155] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: counting the power deviation data within a preset time period and calculating the data change rate of the power deviation data; when the data change rate is greater than or equal to a preset fluctuation threshold, adjusting the charging time of the cluster of mobile power users in the load adjustment area; when the data change rate is less than the preset fluctuation threshold, adjusting the charging rate of the cluster of mobile power users in the load adjustment area.

[0156] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the power deviation data is characterized by a power shortage state, the charge amount of the mobile power user cluster is reduced; when the dispatchable power data is less than the power deviation data and the power deviation data is characterized by a power overflow state, the charge amount of the mobile power user cluster is increased.

[0157] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: obtaining a first evaluation model and operating status information of multiple resource interaction subjects in a virtual power plant; wherein the resource interaction subjects include static power users and dynamic power users, and the first evaluation model is used to predict the operating power data of the virtual power plant based on the operating status information of the static power users; for each dynamic power user, respectively extracting power characteristic data from the corresponding operating status information; correcting the first evaluation model based on the power characteristic data to obtain a second evaluation model; inputting the operating status information corresponding to the multiple resource interaction subjects into the second evaluation model to obtain the target operating power data of the virtual power plant, and adjusting the operating status of the dynamic power users based on the target operating power data.

[0158] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining an electric hydrogen scheduling evaluation model, and determining the electric hydrogen scheduling constraints based on the current operating status information of the electric hydrogen system; calculating the dispatchable power data of the electric hydrogen system using the electric hydrogen scheduling evaluation model based on the electric hydrogen scheduling constraints; and adjusting the operating status of the electric hydrogen system based on the target operating power data and the dispatchable power data.

[0159] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: calculating power deviation data based on the target operating power data; wherein the power deviation data is used to characterize that the current virtual power plant is in a power overflow state or a power shortage state; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power shortage state, controlling the electric hydrogen system to reduce hydrogen production operations according to the power deviation data; when the dispatchable power data is greater than the power deviation data, and the power deviation data is characterized as a power overflow state, controlling the electric hydrogen system to increase hydrogen production operations according to the power deviation data.

[0160] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the dispatchable power data is less than the power deviation data, determining the load adjustment area according to the location of the static power user; adjusting the charging status of the cluster of mobile power users within the load adjustment area according to the power deviation data.

[0161] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: counting the power deviation data within a preset time period and calculating the data change rate of the power deviation data; when the data change rate is greater than or equal to a preset fluctuation threshold, adjusting the charging time of the cluster of mobile power users in the load adjustment area; when the data change rate is less than the preset fluctuation threshold, adjusting the charging rate of the cluster of mobile power users in the load adjustment area.

[0162] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the power deviation data is characterized by a power shortage state, the charge amount of the mobile power user cluster is reduced; when the dispatchable power data is less than the power deviation data and the power deviation data is characterized by a power overflow state, the charge amount of the mobile power user cluster is increased.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0164] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A virtual power plant resource scheduling method, characterized in that: The method comprises: Acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects; For each of the dynamic electricity users, extract power characteristic data from the corresponding operating status information; Correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model; The operating status information corresponding to the multiple resource interaction entities is input into the second evaluation model to obtain the target operating power data of the virtual power plant, and the operating status of the dynamic electricity user entity is adjusted according to the target operating power data.

2. The method according to claim 1, characterized in that The dynamic electricity-consuming subject includes an electric-hydrogen system; and adjusting the operating state of the dynamic electricity-consuming subject according to the target operating power data includes: Obtaining an electric hydrogen scheduling evaluation model, and determining an electric hydrogen scheduling constraint condition according to current operating status information of the electric hydrogen system; According to the electric hydrogen scheduling constraint conditions, the electric hydrogen scheduling evaluation model is used to calculate the dispatchable power data of the electric hydrogen system; The operating state of the electric hydrogen system is adjusted according to the target operating power data and the dispatchable power data.

3. The method according to claim 2, characterized in that The adjusting the operating state of the electric hydrogen system according to the target operating power data and the dispatchable power data includes: Calculating power deviation data according to the target operating power data; wherein the power deviation data is used to characterize that the virtual power plant is currently in a power overflow state or a power shortage state; When the dispatchable power data is greater than the power deviation data and the power deviation data indicates a power shortage state, controlling the electric hydrogen system to reduce hydrogen production operations according to the power deviation data; When the dispatchable power data is greater than the power deviation data and the power deviation data is characterized by a power overflow state, the electric hydrogen system is controlled to increase hydrogen production operations according to the power deviation data.

4. The method according to claim 3, characterized in that The dynamic power consumption entity further includes a mobile power consumption entity cluster; and the step of adjusting the operation state of the dynamic power consumption entity according to the target operation power data further includes: When the dispatchable power data is less than the power deviation data, determining a load adjustment area according to the location of the static power consumer; The charging state of the mobile electricity-consuming entity cluster within the load adjustment area is adjusted according to the power deviation data.

5. The method according to claim 4, characterized in that The step of adjusting the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data includes: Counting the power deviation data within a preset time period and calculating the data change speed of the power deviation data; When the data change speed is greater than or equal to a preset fluctuation threshold, adjusting the charging time of the mobile power-consuming entity cluster in the load adjustment area; When the data change speed is less than a preset fluctuation threshold, the charging rate of the mobile electricity-consuming entity cluster within the load adjustment area is adjusted.

6. The method according to claim 4, characterized in that The step of adjusting the charging state of the mobile power-consuming entity cluster within the load adjustment area according to the power deviation data includes: When the power deviation data indicates a power shortage state, reducing the charging amount of the mobile power-consuming entity cluster; When the dispatchable power data is less than the power deviation data and the power deviation data indicates a power overflow state, the charging amount of the mobile power-consuming entity cluster is increased.

7. A virtual power plant resource scheduling device, characterized in that: The device comprises: An information acquisition module, used to acquire the operating status information of a plurality of resource interaction subjects in the first evaluation model and the virtual power plant; wherein the resource interaction subjects include static power consumption subjects and dynamic power consumption subjects, and the first evaluation model is used to predict the operating power data of the virtual power plant according to the operating status information of the static power consumption subjects; A data analysis module, configured to extract power characteristic data from the corresponding operating status information for each of the dynamic power users; A data processing module, used for correcting the first evaluation model according to the power characteristic data to obtain a second evaluation model; A state adjustment module is used to input the operating state information corresponding to the multiple resource interaction entities into the second evaluation model, obtain the target operating power data of the virtual power plant, and adjust the operating state of the dynamic power user according to the target operating power data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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