Virtual power plant control method based on multi-time scale rolling adjustment

Abstract

The invention provides a virtual power plant control method based on multi-time scale rolling adjustment, and the method comprises the steps: obtaining first target parameters which at least comprise a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter; determining an operation parameter value range of each component based on the second target parameter to obtain a first constraint group; respectively constructing models according to the second target parameter and the third target parameter, the second target parameter and the fourth target parameter, and the second target parameter and the fifth target parameter to obtain an element cost model, a peak regulation cost model and a frequency modulation capacity model, and sequentially solving to obtain a final target operation parameter. And generating a target control instruction according to the target operation parameter, and controlling the virtual power plant to operate. According to the method, the problem that a virtual power plant control method starting from multiple time scales is lacked in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a virtual power plant control method based on multi-time scale rolling adjustment, a control device, a computer-readable storage medium, a processor and a power plant control system. Background Art

[0002] As an aggregate of a large number of flexible resources, the role of virtual power plants in cooperating with the power grid to shave peak loads and fill valleys, and to absorb new energy sources, is becoming increasingly important. However, they currently lack economy and efficiency in market operations, and lack market trading ideas that can be used as reference, resulting in insufficient enthusiasm for their participation in the market.

[0003] The negative performance of virtual power plants in market competition can be explained by the volatility and randomness of distributed renewable resources, which bring a lot of risks, making them too conservative in the day-ahead market bidding and unable to obtain appropriate electricity and benefits. Although virtual power plants gather a large number of flexible resources and controllable loads, etc., and have certain advantages in real-time adjustment of the market, their risks in the electricity market cannot support the corresponding benefits.

[0004] In summary, the virtual power plant control scheme in the existing technology has failed to solve the contradictory performance of virtual power plants in the electric energy market and the ancillary service market. It lacks a virtual power plant control method based on multiple time scales to take into account the advantages of adjustable resources such as transferable loads and energy storage in the real-time market, to make segmented rolling adjustments to the multi-time scale trading stages of the electricity market, and to continue to consider the internal economic operation of the virtual power plant after combining market rules. Summary of the invention

[0005] The main purpose of this application is to provide a virtual power plant control method, control device, computer-readable storage medium, processor and power plant control system based on multi-time scale rolling adjustment, so as to at least solve the problem of the lack of a virtual power plant control method based on multiple time scales in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a virtual power plant control method based on multi-time scale rolling adjustment is provided, wherein the virtual power plant includes at least a thermal power unit, a renewable energy unit, an energy storage device and a flexible load, and the method includes: obtaining a first target parameter, wherein the first target parameter includes at least a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, wherein the first target parameter is an operating parameter of the virtual power plant within a first preset time period, wherein the end time of the first preset time period is a first preset time period and the start time is a second preset time period, wherein the second target parameter includes an operating parameter of each component element, wherein the component element includes the thermal power unit, the renewable energy unit, an energy storage device and a flexible load. group, the energy storage device and the flexible load, the second target parameter includes the operating cost of each of the components, the fourth target parameter includes the peak-shaving cost of each of the components, and the fifth target parameter includes the frequency regulation capacity of each of the components; a first correlation relationship between the operating parameters and the operating costs of each of the components is determined according to the second target parameter and the third target parameter, and a model is constructed according to the first correlation relationship for simulation to obtain a component cost model; based on the second target parameter, a value range of the operating parameters of each of the components is determined to obtain a first constraint group, and the sum of the output parameters of the component cost model is minimized as the first objective function, under the constraints of the first constraint group , calculate the input parameters of the component cost model when the first objective function is minimized to obtain the first candidate parameters; determine the second candidate parameters after the virtual power plant performs peak-shaving operation based on the second objective parameter and the fourth objective parameter, and the second correlation between the deviation between the first candidate parameters and the peak-shaving cost, and construct a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, the input parameters of the peak-shaving cost model are the first candidate parameters; minimize the sum of the output parameters of the peak-shaving cost model as the second objective function, and calculate the input parameters of the peak-shaving cost model when the second objective function is minimized under the constraints of the first constraint group to obtain the second candidate parameters; Based on the second target parameter and the fifth target parameter, the target alternative parameters after the virtual power plant performs frequency regulation operation and the third correlation relationship between the deviation between the second alternative parameters and the frequency regulation capacity are determined, and a corresponding model is constructed according to the third correlation relationship to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second alternative parameter; the sum of the output parameters of the frequency regulation capacity model is maximized as the third objective function, and under the restriction of the first constraint group, the input parameters of the frequency regulation capacity model are calculated when the third objective function is maximum to obtain target operating parameters, target control instructions are generated according to the target operating parameters, and the operation of the virtual power plant is controlled according to the target control instructions.

[0007] Optionally, the renewable energy unit includes a wind turbine unit and a photovoltaic unit, and a first correlation relationship between the operating parameters and the operating costs of each of the components is determined according to the second target parameter and the third target parameter, and a model is constructed according to the first correlation relationship for simulation to obtain a component cost model, including: obtaining a first power generation power, an operating state parameter and a first cost, taking the operating state as an input parameter and a first target cost as an output parameter, and constructing a model corresponding to the thermal power unit according to the first power generation power, the operating state parameter and the first cost to obtain a first component cost model, wherein the first power generation power is the rated power of the thermal power unit, the operating state parameter is a binary variable of the operating state of the thermal power unit, the first cost is the startup cost of the thermal power unit, and the first target cost is the total cost of the thermal power unit: Among them, D Th,t For the first cost, Q Th,t is the first generated power, a Th 、b Th and c Th is the energy consumption coefficient of the thermal power unit, u Th,t is the operating status of the thermal power unit at time t, is the fuel cost of the thermal power unit, is the start-up and shutdown cost of the thermal power unit, r Th is the total revenue of the thermal power unit, is the real-time unit electricity price; obtain the second generated power, the first wind speed, the second wind speed, the third wind speed, the real-time wind speed, the second cost, the sunshine intensity, the solar absorption rate, the solar conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost; take the real-time wind speed and the sunshine intensity as input parameters, take the second target cost as the output parameter, and build the model corresponding to the renewable energy unit according to the second generated power, the first wind speed, the second wind speed, the third wind speed, the second cost, the solar absorption rate, the solar conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost to obtain the second component cost model; the second generated power is the rated power of the wind turbine unit, the first wind speed is the cut-in wind speed of the wind turbine unit, the second wind speed is the cut-out wind speed of the wind turbine unit, the third wind speed is the rated wind speed of the wind turbine unit, the first wind speed is less than the third wind speed, the third wind speed is less than the second wind speed, the second cost is the operating cost of the wind turbine unit, the third cost is the operating cost of the photovoltaic unit, and the second target cost is the total cost of the renewable energy unit:

[0008] Among them, P t WPP is the actual output power of the wind turbine at time t, v t is the real-time wind speed, v in is the first wind speed, v out is the second wind speed, is the second power generation, v ra is the third wind speed, P t PV is the actual output power of the photovoltaic unit at time t, is the sunshine intensity, η PV is the photovoltaic power generation efficiency, loss is the loss ratio, μ inv is the solar energy conversion efficiency, μ sor is the solar energy absorption rate, S PV is the photovoltaic panel area, and are the depreciation costs of the wind turbine and the photovoltaic unit at time t, and are the actual output power of the wind turbine and the photovoltaic unit at time t, respectively, WPP and p PV The second cost and the third cost are the unit power of the wind turbine and the photovoltaic unit respectively; the target capacity, the first target power, the second target power and the fourth cost are obtained, the first target power or the second target power is determined as an input parameter, the third target cost is determined as an output parameter, and a model corresponding to the energy storage device is constructed according to the target capacity, the first target power, the second target power and the fourth cost to obtain a third component cost model, wherein the target capacity is the capacity of the energy storage device, the first target power is the charging power of the energy storage device, the second target power is the discharging power of the energy storage device, the fourth cost is the operating cost of the energy storage device, and the third target cost is the total cost of the energy storage device: C ESS =c 4 *(ESt+1-ESt); where ESt+1 and ESt represent the target capacity at time t+1 and time t, respectively, δ chr and δ disc are the charging and discharging efficiency of the energy storage device, P chr and P disc are the first target power and the second target power of the energy storage device respectively, Δt is the time interval between time t+1 and time t, C ESS is the third target cost, c 4is the fourth cost; obtaining a target transfer amount and a target price, taking the target transfer amount as an input parameter and the fourth target cost as an output parameter, constructing a model corresponding to the flexible load, and obtaining a fourth component cost model, wherein the target transfer amount is the load transfer-in amount or the load transfer-out amount of the flexible load, the target price is the load unit transfer price, and the fourth target cost is the total cost of the flexible load:

[0009]

[0010] in, is the target transfer amount of the nth load at time t, The initial load transfer of the nth load at time t, M is the price demand elasticity matrix, is a binary variable for the transfer in and out of load, represents the rate of change of load at time t corresponding to J hours, where p J (t) represents the load change at time t in J hours, is the initial load state corresponding to the beginning of J hours. is the load transfer amount of the nth load at time t, is the load transfer amount of the nth load at time t, is the target price, C DR is the fourth target cost.

[0011] Optionally, determining the value range of the operating parameter of each of the component elements based on the second target parameter to obtain a first constraint group includes: constructing a load transfer constraint of the flexible load to obtain a first target constraint: in, is the maximum transfer amount of the flexible load; construct the operation constraint of the energy storage device to obtain the second objective constraint: ES min ≤ES t ≤ES max ;in, and are the minimum and maximum values ​​of the charging power of the energy storage device, respectively. and are the minimum and maximum discharge power of the energy storage device, ES min and ES max are the lower and upper limits of the capacity of the electric energy storage device respectively; construct the virtual power plant system balance constraint to obtain the third objective constraint: Where D is the load demand value of the virtual power plant, Q CHP is the active power output of the thermal power unit, Q j,WPPis the active power output of wind turbine j, Q m,pv is the active output of the photovoltaic unit m; construct the output constraint of the thermal power unit to obtain the fourth target constraint: in, and are the maximum dispatchable output and the minimum dispatchable output of the thermal power unit respectively; construct the climbing constraint of the thermal power unit, and obtain the fifth objective constraint: in, and are the lower limit and upper limit of the thermal power unit climbing in the unit monitoring period respectively; the output constraint of the wind turbine unit is constructed to obtain the sixth target constraint: in, and The maximum dispatchable output and the minimum dispatchable output of the wind turbine are processed respectively; the output constraint of the photovoltaic unit is constructed to obtain the seventh target constraint: in, and are the maximum dispatchable output and the minimum dispatchable output of the photovoltaic unit respectively; the backup capacity constraint of the virtual power plant is constructed to obtain the eighth target constraint: Where G is the set of all generating units, D(t) is the load demand of the system at time t, R(t) is the reserve demand of the system at time t, l is the line loss rate of the virtual power plant, θ is the self-use rate of the unit, Q max (t) is the maximum output of the generator set at time t, and the generator set includes the thermal power unit and the renewable energy unit; the first target constraint, the second target constraint, the third target constraint, the fourth target constraint, the fifth target constraint, the sixth target constraint, the seventh target constraint and the eighth target constraint are combined to obtain the first constraint group.

[0012] Optionally, determining a second candidate parameter after the virtual power plant performs a peak-shaving operation and a second correlation between the deviation between the first candidate parameter and the peak-shaving cost based on the second target parameter and the fourth target parameter, and constructing a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, including: constructing the peak-shaving cost model based on the second target parameter and the fourth target parameter: Among them, F t de is the peak load cost at time t, which is determined by the deviation between the quotation of the virtual power plant and the actual electricity price at time t, is the estimated sales volume of the virtual power plant at time t, P t VPP is the actual amount of electricity sold by the virtual power plant at time t, κ is the upper limit of the deviation, λ deis the penalty coefficient, is the clearing price at time t.

[0013] Optionally, before minimizing the sum of the output parameters of the component cost model as the first objective function and calculating the input parameters of the component cost model when the first objective function is minimized under the first constraint group to obtain the first candidate parameters, the method further includes: constructing a corresponding objective function according to the peak-shaving cost model to obtain the first objective function: in, and are the cost of adjusting the virtual power plant upward / downward at time h, are the upward / downward adjustment requirements of the virtual power plant at time h, and are the cost prices of adjusting the thermal power unit, the energy storage device and the flexible load at time h, and are the upward adjustment amounts of the thermal power unit, the energy storage device and the flexible load at time h, and The amounts by which the thermal power unit, the energy storage device and the flexible load are respectively adjusted downward at time h.

[0014] Optionally, determining the target candidate parameters after the virtual power plant performs the frequency regulation operation and a third association relationship between the deviation between the second candidate parameters and the frequency regulation capacity based on the second target parameter and the fifth target parameter, and constructing a corresponding model according to the third association relationship to obtain a frequency regulation capacity model, including: constructing the frequency regulation capacity model based on the second target parameter and the fifth target parameter:

[0015]

[0016] in, and They are respectively the virtual power plant at t n The time period provides downward and upward adjustment capacity. The virtual power plant is n Ancillary service settlement price for the period, t n The starting time of the time period is time t.

[0017] Optionally, before maximizing the sum of the output parameters of the frequency regulation capacity model as a third objective function and calculating the input parameters of the frequency regulation capacity model when the third objective function is maximum under the constraints of the first constraint group to obtain the target operating parameters, the method further includes: constructing a corresponding objective function according to the frequency regulation capacity model to obtain a second objective function: Among them, N X is the total frequency regulation times of the virtual power plant in one day, t n The time interval for frequency modulation of the virtual power plant.

[0018] According to another aspect of the present application, a virtual power plant control device based on multi-time scale rolling adjustment is provided, wherein the virtual power plant includes at least a thermal power unit, a renewable energy unit, an energy storage device and a flexible load, and the device includes: a first acquisition unit, for acquiring a first target parameter, wherein the first target parameter includes at least a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, wherein the first target parameter is an operating parameter of the virtual power plant within a first preset time period, wherein the end time of the first preset time period is a first preset time period and the start time is a second preset time period, wherein the second target parameter includes an operating parameter of each component element, and the component elements The components include the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter includes the operating cost of each of the components, the fourth target parameter includes the peak load cost of each of the components, and the fifth target parameter includes the frequency regulation capacity of each of the components; a first construction unit is used to determine a first correlation between the operating parameters and the operating costs of each of the components according to the second target parameter and the third target parameter, and to construct a model according to the first correlation to perform simulation and obtain a component cost model; a first determination unit is used to determine the operating cost of each of the components based on the second target parameter. The first constraint group is obtained by selecting a value range of the row parameter, minimizing the sum of the output parameters of the component cost model as the first objective function, and under the restriction of the first constraint group, calculating the input parameters of the component cost model when the first objective function is minimized to obtain the first candidate parameter; the second construction unit is used to determine the second candidate parameter after the virtual power plant performs the peak-shaving operation based on the second target parameter and the fourth target parameter, and the second correlation relationship between the deviation between the first candidate parameter and the peak-shaving cost, and constructing a model according to the second correlation relationship for simulation to obtain a peak-shaving cost model, wherein the input parameter of the peak-shaving cost model is the first candidate parameter. a first calculation unit, configured to minimize the sum of the output parameters of the peak-shaving cost model as a second objective function, and, under the constraints of the first constraint group, calculate the input parameters of the peak-shaving cost model when the second objective function is minimized to obtain second candidate parameters; a third construction unit, configured to determine, based on the second target parameter and the fifth target parameter, the target candidate parameters after the virtual power plant performs frequency regulation operation and a third correlation between the deviation between the second candidate parameters and the frequency regulation capacity, and to construct a corresponding model according to the third correlation to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second candidate parameter;The second calculation unit is used to maximize the sum of the output parameters of the frequency regulation capacity model as a third objective function, and under the restriction of the first constraint group, calculate the input parameters of the frequency regulation capacity model when the third objective function is maximized, obtain target operating parameters, generate target control instructions according to the target operating parameters, and control the operation of the virtual power plant according to the target control instructions. ;

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the method described in any one of the devices where the computer-readable storage medium is located is controlled.

[0020] According to another aspect of the present application, a virtual power plant control system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0021] Applying the technical solution of the present application, in the above-mentioned virtual power plant control method based on multi-time scale rolling adjustment, first, obtain the first target parameter, the above-mentioned first target parameter at least includes the second target parameter, the third target parameter, the fourth target parameter and the fifth target parameter, the above-mentioned first target parameter is the operating parameter of the above-mentioned virtual power plant within the first preset time period, the end time of the above-mentioned first preset time period is the first preset time and the start time is the second preset time, the above-mentioned second target parameter includes the operating parameters of each component, the above-mentioned component includes the above-mentioned thermal power unit, the above-mentioned renewable energy unit, the above-mentioned energy storage device and the above-mentioned flexible load, the above-mentioned second target parameter includes the operating cost of each of the above-mentioned components, The fourth target parameter includes the peak load cost of each of the above-mentioned components, and the fifth target parameter includes the frequency regulation capacity of each of the above-mentioned components; then, the first correlation between the above-mentioned operating parameters and the above-mentioned operating costs of each of the above-mentioned components is determined according to the above-mentioned second target parameter and the above-mentioned third target parameter, and a model is constructed according to the above-mentioned first correlation to perform simulation to obtain a component cost model; then, based on the above-mentioned second target parameter, the value range of the operating parameters of each of the above-mentioned components is determined to obtain a first constraint group, and the sum of the output parameters of the above-mentioned component cost model is minimized as the first objective function. Under the restriction of the above-mentioned first constraint group, the output parameter of the above-mentioned component cost model when the above-mentioned first objective function is minimized is calculated. Input parameters to obtain first candidate parameters; then, based on the second target parameters and the fourth target parameters, determine the second candidate parameters after the virtual power plant performs peak-shaving operation and the second correlation between the deviation between the first candidate parameters and the peak-shaving cost, and build a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, where the input parameters of the peak-shaving cost model are the first candidate parameters; then, minimize the sum of the output parameters of the peak-shaving cost model as the second objective function, and under the constraints of the first constraint group, calculate the input parameters of the peak-shaving cost model when the second objective function is minimized to obtain the second candidate parameters; then, based on the second target parameters and the fifth target parameter to determine the target alternative parameters after the virtual power plant performs frequency modulation operation, and the third correlation relationship between the deviation between the second alternative parameters and the frequency modulation capacity, and construct a corresponding model based on the third correlation relationship to obtain a frequency modulation capacity model, the input parameter of the frequency modulation capacity model is the second alternative parameter; finally, maximizing the sum of the output parameters of the frequency modulation capacity model is the third target function, and under the restriction of the first constraint group, calculating the input parameters of the frequency modulation capacity model when the third target function is maximum to obtain the target operating parameters, generating target control instructions based on the target operating parameters, and controlling the operation of the virtual power plant according to the target control instructions.This application is based on three perspectives: day-ahead, intraday and mid-day, and considers the cost of day-ahead operation control of the virtual power plant, the cost of intraday peak-shaving operation and the cost of real-time frequency regulation operation, respectively, to construct corresponding models and then solve them in sequence with cost minimization and benefit maximization as the objective functions, thereby solving the problem of the lack of a virtual power plant control method from multiple time scales in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A hardware structure block diagram of a mobile terminal according to a virtual power plant control method based on multi-time scale rolling adjustment provided in an embodiment of the present application is shown;

[0023] Figure 2 A schematic flow chart of a virtual power plant control method based on multi-time scale rolling adjustment provided according to an embodiment of the present application is shown;

[0024] Figure 3 A structural block diagram of a virtual power plant control device based on multi-time scale rolling adjustment provided according to an embodiment of the present application is shown.

[0025] The above drawings include the following reference numerals:

[0026] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] As introduced in the background technology, virtual power plants in the prior art have certain advantages in the real-time adjustment market, but their risks in the electric energy market cannot support the corresponding benefits. In order to solve the problem of the lack of a virtual power plant control method based on multiple time scales, the embodiments of the present application provide a virtual power plant control method, control device, computer-readable storage medium, processor and power plant control system based on multi-time scale rolling adjustment.

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0032] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a virtual power plant control method based on multi-time scale rolling adjustment according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] In this embodiment, a virtual power plant control method based on multi-time scale rolling adjustment is provided, which runs on a mobile terminal, a computer terminal or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] Figure 2 is a flow chart of a virtual power plant control method based on multi-time scale rolling adjustment according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0036] Step S201, obtaining a first target parameter, the first target parameter at least includes a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, the first target parameter is an operating parameter of the virtual power plant within a first preset time period, the end time of the first preset time period is the first preset time period and the start time is the second preset time period, the second target parameter includes an operating parameter of each component, the component includes the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter includes an operating cost of each of the above components, the fourth target parameter includes a peak regulation cost of each of the above components, and the fifth target parameter includes a frequency regulation capacity of each of the above components;

[0037] Specifically, within the first preset time period, the operating parameters, operating costs, peak load costs, frequency regulation capacity and other parameters of the thermal power units, renewable energy units, energy storage devices, flexible loads and other component groups in the virtual power plant are obtained as the basis for formulating the control strategy.

[0038] Step S202, determining the relationship between the operating parameters and the operating costs of each of the component elements according to the second target parameter and the third target parameter, and constructing a model for simulation to obtain a component cost model;

[0039] Specifically, by analyzing the operating parameters and operating costs of each component in the virtual power plant, determining the relationship between the operating parameters and operating costs of each component, identifying how different parameters interact with each other and how these interactions affect the overall operating costs, and building and applying component cost models, the virtual power plant can more accurately predict and control costs and optimize the scheduling of power generation resources.

[0040] Step S203, determining the range of operating parameters of each of the above-mentioned components based on the above-mentioned second target parameter, obtaining a first constraint group, and solving the input parameters of the above-mentioned component cost model under the restriction of the above-mentioned first constraint group with minimization of the sum of the output parameters of the above-mentioned component cost model as the objective function to obtain the first candidate parameters;

[0041] Specifically, based on the operating parameters and operating costs of each component in the virtual power plant, under the constraints of technical conditions, safety performance, economic conditions and other factors of the virtual power plant operation, the reasonable value range of the operating parameters of each component is determined, and a first constraint group is constructed. Under the constraints of the first constraint group, the input parameters of the component cost model are solved with the goal of minimizing the total operating cost of the virtual power plant, and the minimum operating cost of the virtual power plant is obtained while meeting the grid demand and maximizing economic benefits, that is, the above-mentioned first alternative parameter.

[0042] Step S204, determining the correlation between the deviation between the second candidate parameter and the first candidate parameter after the virtual power plant performs the peak-shaving operation and the peak-shaving cost based on the second target parameter and the fourth target parameter, and constructing a model for simulation to obtain a peak-shaving cost model, wherein the input parameter of the peak-shaving cost model is the first candidate parameter;

[0043] Specifically, based on the operating parameters and peak-shaving costs of each component in the virtual power plant, the impact of peak-shaving operation on cost is analyzed, the relationship between peak-shaving operation and cost is determined, and the second alternative parameter after the virtual power plant performs peak-shaving operation is obtained. Based on the deviation between the second alternative parameter and the first alternative parameter, the association between the cost generated by the peak-shaving operation is constructed to obtain a peak-shaving cost model to evaluate the impact of different peak-shaving strategies on cost.

[0044] Step S205, taking minimization of the sum of the output parameters of the peak-shaving cost model as the objective function and under the constraints of the first constraint group, solving the input parameters of the peak-shaving cost model to obtain second candidate parameters;

[0045] Specifically, a peak-shaving strategy is formulated so that the total peak-shaving cost is minimized while meeting the peak-shaving demand of the power grid. Under the condition of satisfying the first constraint group, the peak-shaving cost model is solved with minimizing the peak-shaving cost as the objective function, and a second alternative parameter is obtained that can meet the peak-shaving demand of the power grid while effectively controlling the peak-shaving cost.

[0046] Step S206, determining the correlation between the deviation between the target candidate parameter after the virtual power plant performs frequency regulation operation and the second candidate parameter and the frequency regulation capacity based on the second target parameter and the fifth target parameter, and constructing a corresponding model to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second candidate parameter;

[0047] Specifically, according to the operating parameters and frequency regulation capacity of each component in the virtual power plant, and taking into account the frequency regulation capacity and grid frequency stability, the target parameters for frequency regulation operation of the virtual power plant are determined, the impact of the frequency regulation operation on the grid, and the deviation between the target alternative parameters and the second alternative parameters after the frequency regulation operation are analyzed, and a corresponding model is constructed to evaluate how the frequency regulation capacity of the virtual power plant changes under different frequency regulation operations, and to obtain a frequency regulation capacity model.

[0048] Step S207, taking the maximization of the sum of the output parameters of the frequency regulation capacity model as the objective function and under the constraints of the first constraint group, solving the input parameters of the frequency regulation capacity model to obtain target operating parameters, generating target control instructions according to the target operating parameters, and controlling the operation of the virtual power plant according to the target control instructions.

[0049] Specifically, under the constraints of the first constraint group, a frequency regulation capacity model is used to determine an operating parameter of a virtual power plant so as to maximize the frequency regulation capacity of the virtual power plant, and corresponding control instructions are generated according to the target operating parameters to control the operation of each component of the virtual power plant.

[0050] Through this embodiment, first, a first target parameter is obtained, the first target parameter at least includes a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, the first target parameter is an operating parameter of the virtual power plant within a first preset time period, the end time of the first preset time period is the first preset time period and the start time is the second preset time period, the second target parameter includes the operating parameters of each component, the component includes the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter includes the operating cost of each of the above components, the fourth target parameter includes the peak load cost of each of the above components, The fifth target parameter includes the frequency modulation capacity of each of the above-mentioned components; then, the first correlation between the above-mentioned operating parameters and the above-mentioned operating costs of each of the above-mentioned components is determined according to the second target parameter and the third target parameter, and a model is constructed according to the first correlation to perform simulation, so as to obtain a component cost model; then, based on the second target parameter, the value range of the operating parameters of each of the above-mentioned components is determined to obtain a first constraint group, and the sum of the output parameters of the above-mentioned component cost model is minimized as the first target function. Under the restriction of the first constraint group, the input parameters of the above-mentioned component cost model when the first target function is minimized are calculated to obtain the first candidate parameters; then Then, based on the second target parameter and the fourth target parameter, determine the second candidate parameter after the virtual power plant performs the peak-shaving operation, and the second correlation between the deviation between the first candidate parameter and the peak-shaving cost, and build a model according to the second correlation to perform simulation, so as to obtain a peak-shaving cost model, wherein the input parameter of the peak-shaving cost model is the first candidate parameter; then, minimize the sum of the output parameters of the peak-shaving cost model as the second target function, and under the restriction of the first constraint group, calculate the input parameters of the peak-shaving cost model when the second target function is minimized, so as to obtain the second candidate parameter; then, based on the second target parameter and the fifth target parameter, calculate the second candidate parameter. The parameters determine the target alternative parameters of the virtual power plant after the frequency regulation operation and the third correlation between the deviation between the second alternative parameters and the frequency regulation capacity, and construct a corresponding model based on the third correlation to obtain a frequency regulation capacity model, and the input parameters of the frequency regulation capacity model are the second alternative parameters; finally, the sum of the output parameters of the frequency regulation capacity model is maximized as the third objective function, and under the restriction of the first constraint group, the input parameters of the frequency regulation capacity model when the third objective function is maximum are calculated to obtain the target operating parameters, generate the target control instructions based on the target operating parameters, and control the operation of the virtual power plant according to the target control instructions.This application is based on three perspectives: day-ahead, intraday and mid-day, and considers the cost of day-ahead operation control of the virtual power plant, the cost of intraday peak-shaving operation and the cost of real-time frequency regulation operation, respectively, to construct corresponding models and then solve them in sequence with cost minimization and benefit maximization as the objective functions, thereby solving the problem of the lack of a virtual power plant control method from multiple time scales in the prior art.

[0051] In order to obtain the component cost model, in an optional implementation, the above step S202 includes:

[0052] Step S2021, obtaining a first power generation, an operating state parameter and a first cost, taking the operating state as an input parameter and the first target cost as an output parameter, constructing a model corresponding to the thermal power unit according to the first power generation, the operating state parameter and the first cost, and obtaining a first component cost model, wherein the first power generation is the rated power of the thermal power unit, the operating state parameter is a binary variable of the operating state of the thermal power unit, the first cost is the startup cost of the thermal power unit, and the first target cost is the total cost of the thermal power unit:

[0053]

[0054]

[0055] Among them, D Th,t is the first cost mentioned above, Q Th,t is the first power generation, a Th 、b Th and c Th is the energy consumption coefficient of the above thermal power unit, u Th,t is the operating status of the above thermal power unit at time t, is the fuel cost of the above thermal power unit, is the start-up and shutdown cost of the above thermal power unit, r Th is the total revenue of the above thermal power units, is the real-time unit electricity price;

[0056] Step S2022, obtaining the second generated power, the first wind speed, the second wind speed, the third wind speed, the real-time wind speed, the second cost, the sunshine intensity, the solar absorption rate, the solar energy conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost, taking the real-time wind speed and the sunshine intensity as input parameters, and taking the second target cost as output parameter, according to the second generated power, the first wind speed, the second wind speed, the third wind speed, the second cost, the solar absorption rate, the solar energy conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost The third cost constructs a model corresponding to the renewable energy unit to obtain a second component cost model. The second power generation is the rated power of the wind turbine unit. The first wind speed is the cut-in wind speed of the wind turbine unit. The second wind speed is the cut-out wind speed of the wind turbine unit. The third wind speed is the rated wind speed of the wind turbine unit. The first wind speed is less than the third wind speed. The third wind speed is less than the second wind speed. The second cost is the operating cost of the wind turbine unit. The third cost is the operating cost of the photovoltaic unit. The second target cost is the total cost of the renewable energy unit:

[0057]

[0058] Among them, P t WPP is the actual output power of the wind turbine at time t, v t is the above real-time wind speed, v in is the first wind speed mentioned above, v out is the second wind speed mentioned above, is the second power generation, v ra is the third wind speed mentioned above, P t PV is the actual output power of the above photovoltaic unit at time t, is the above-mentioned sunshine intensity, η PV is the photovoltaic power generation efficiency, loss is the loss ratio, μ inv is the above solar energy conversion rate, μ sor is the above solar energy absorption rate, S PV is the area of ​​the photovoltaic panel mentioned above, and are the depreciation costs of the wind turbine and photovoltaic units at time t, and are the actual output power of the wind turbine and photovoltaic units at time t, respectively, WPP and p PV The second cost and the third cost per unit power of the wind turbine generator set and the photovoltaic generator set, respectively;

[0059] Step S2023, obtaining the target capacity, the first target power, the second target power and the fourth cost, determining the first target power or the second target power as an input parameter, determining the third target cost as an output parameter, constructing a model corresponding to the energy storage device according to the target capacity, the first target power, the second target power and the fourth cost, and obtaining a third component cost model, wherein the target capacity is the capacity of the energy storage device, the first target power is the charging power of the energy storage device, the second target power is the discharging power of the energy storage device, the fourth cost is the operating cost of the energy storage device, and the third target cost is the total cost of the energy storage device:

[0060]

[0061] C ESS =c 4 *(ES t+1 -ES t );

[0062] Among them, ES t+1 and ES t denote the above target capacities at time t+1 and time t, respectively, δ chr and δ disc are the charging and discharging efficiencies of the above energy storage devices, P chr and P disc are the first target power and the second target power of the energy storage device, respectively; Δt is the time interval between time t+1 and time t; C ESS is the third target cost mentioned above, c 4 For the fourth cost mentioned above;

[0063] Step S2024, obtaining a target transfer amount and a target price, taking the target transfer amount as an input parameter and the fourth target cost as an output parameter, constructing a model corresponding to the flexible load, and obtaining a fourth component cost model, wherein the target transfer amount is the load transfer-in amount or the load transfer-out amount of the flexible load, the target price is the load unit transfer price, and the fourth target cost is the total cost of the flexible load:

[0064]

[0065] in, is the target transfer amount of the nth load at time t, The initial load transfer of the nth load at time t, M is the price demand elasticity matrix, is a binary variable for the transfer in and out of load, p j (t) is, is the capacity of the virtual power plant adjusted downward and upward during the period, is the load transfer amount of the nth load at time t, is the load transfer amount of the nth load at time t, is the target price mentioned above, C DR This is the fourth target cost mentioned above.

[0066] In order to obtain the first constraint group, in an optional implementation, the above step S203 includes:

[0067] Step S2031, construct the load transfer constraint of the above flexible load to obtain the first target constraint:

[0068]

[0069] in, is the maximum transfer amount of the above flexible load;

[0070] Step S2032: construct the operation constraints of the energy storage device to obtain the second target constraint:

[0071]

[0072] in, and are the minimum and maximum values ​​of the charging power of the above energy storage device, respectively. and are the minimum and maximum discharge powers of the above energy storage devices, ES min and ES max are the lower and upper limits of the capacity of the electric energy storage device, respectively;

[0073] Step S2033, construct the above virtual power plant system balance constraint to obtain the third target constraint:

[0074]

[0075] Where D is the load demand value of the virtual power plant, Q CHP is the active power output of the above thermal power unit, Q j,WPP is the active power output of the above wind turbine j, Q m,pv is the active power output of the above photovoltaic unit m;

[0076] Step S2034, construct the output constraint of the thermal power unit to obtain the fourth target constraint:

[0077]

[0078] in, and are the maximum dispatchable output and the minimum dispatchable output of the above thermal power units respectively;

[0079] Step S2035, construct the ramp constraint of the thermal power unit to obtain the fifth target constraint:

[0080]

[0081] in, and They are respectively the lower limit and upper limit of the ramp rate of the thermal power units mentioned above within the unit monitoring period;

[0082] Step S2036, construct the output constraint of the above wind turbine generator set to obtain the sixth target constraint:

[0083]

[0084] in, and They are respectively the maximum dispatchable output and the minimum dispatchable output of the above wind turbines;

[0085] Step S2037, construct the output constraint of the photovoltaic unit to obtain the seventh target constraint:

[0086]

[0087] in, and They are the maximum dispatchable output and the minimum dispatchable output of the above photovoltaic units respectively;

[0088] Step S2038, construct the reserve capacity constraint of the virtual power plant to obtain the eighth target constraint:

[0089]

[0090] Where G is the set of all generating units, D(t) is the load demand of the system at time t, R(t) is the reserve demand of the system at time t, l is the line loss rate of the virtual power plant, θ is the self-use rate of the unit, Q max (t) is the maximum output of the above-mentioned generator set at time t, and the above-mentioned generator set includes the above-mentioned thermal power unit and the above-mentioned renewable energy unit;

[0091] Step S2039, jointly combining the first target constraint, the second target constraint, the third target constraint, the fourth target constraint, the fifth target constraint, the sixth target constraint, the seventh target constraint and the eighth target constraint to obtain the first constraint group.

[0092] In order to construct a peak load cost model, in an optional implementation, the above step S204 includes:

[0093] Step S2041: construct the peak load cost model based on the second target parameter and the fourth target parameter:

[0094]

[0095] Among them, F t de is the peak load cost at time t, which is determined by the deviation between the quotation of the virtual power plant and the actual electricity price at time t. is the estimated sales volume of the virtual power plant at time t, P t VPP is the actual amount of electricity sold by the virtual power plant at time t, κ is the upper limit of the deviation, and λ de is the penalty coefficient, is the clearing price at time t.

[0096] In order to obtain the first objective function, in an optional implementation, before the input parameters of the peak-shaving cost model are solved by minimizing the sum of the output parameters of the peak-shaving cost model as the objective function and obtaining the second candidate parameters under the constraints of the first constraint group, the method further includes:

[0097] Step S301: construct a corresponding objective function according to the peak load cost model to obtain a first objective function:

[0098]

[0099] in, and are the cost of adjusting the virtual power plant upward / downward at time h, are the upward / downward adjustment requirements of the virtual power plant at time h, and are the cost prices of adjusting the thermal power unit, the energy storage device and the flexible load at time h, and are the upward adjustment amounts of the thermal power unit, the energy storage device and the flexible load at time h, and The amounts by which the thermal power unit, the energy storage device and the flexible load are respectively adjusted downward at time h.

[0100] In order to obtain the frequency modulation capacity model, in an optional implementation manner, the above step S206 includes:

[0101] Step S2061: construct the frequency modulation capacity model based on the second target parameter and the fifth target parameter:

[0102]

[0103] in, and are the capacities of the virtual power plant adjusted downward and upward during the period, The virtual power plant is n Ancillary service settlement price for the period, t n The starting time of the time period is time t.

[0104] In order to obtain the second target model, in an optional implementation manner, before obtaining the target operating parameters by solving the input parameters of the frequency regulation capacity model under the constraints of the first constraint group with maximizing the sum of the output parameters of the frequency regulation capacity model as the objective function, the method further includes:

[0105] According to the above frequency regulation capacity model, the corresponding objective function is constructed to obtain the second objective function:

[0106]

[0107] Among them, N X is the total frequency regulation times of the virtual power plant in one day, t n The time interval for frequency modulation of the above virtual power plant.

[0108] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0109] The embodiment of the present application also provides a virtual power plant control device based on multi-time scale rolling adjustment. It should be noted that the virtual power plant control device based on multi-time scale rolling adjustment of the embodiment of the present application can be used to execute the virtual power plant control method based on multi-time scale rolling adjustment provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0110] The following introduces a virtual power plant control device based on multi-time scale rolling adjustment provided in an embodiment of the present application.

[0111] Figure 3 is a structural block diagram of a virtual power plant control device based on multi-time scale rolling adjustment according to an embodiment of the present application. Figure 3As shown, the device comprises:

[0112] A first acquisition unit 10 is used to acquire a first target parameter, wherein the first target parameter at least includes a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, wherein the first target parameter is an operation parameter of the virtual power plant within a first preset time period, wherein the end time of the first preset time period is the first preset time period and the start time is the second preset time period, wherein the second target parameter includes an operation parameter of each component element, wherein the component elements include the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, wherein the second target parameter includes an operation cost of each of the above-mentioned components, wherein the fourth target parameter includes a peak regulation cost of each of the above-mentioned components, and wherein the fifth target parameter includes a frequency regulation capacity of each of the above-mentioned components;

[0113] Specifically, within the first preset time period, the operating parameters, operating costs, peak load costs, frequency regulation capacity and other parameters of the thermal power units, renewable energy units, energy storage devices, flexible loads and other component groups in the virtual power plant are obtained as the basis for formulating the control strategy.

[0114] A first construction unit 20 is used to determine the relationship between the operating parameters and the operating costs of each of the above-mentioned components according to the above-mentioned second target parameters and the above-mentioned third target parameters, and to construct a model for simulation to obtain a component cost model;

[0115] Specifically, by analyzing the operating parameters and operating costs of each component in the virtual power plant, determining the relationship between the operating parameters and operating costs of each component, identifying how different parameters interact with each other and how these interactions affect the overall operating costs, and building and applying component cost models, the virtual power plant can more accurately predict and control costs and optimize the scheduling of power generation resources.

[0116] A first determination unit 30 is used to determine the range of operating parameters of each of the above-mentioned components based on the above-mentioned second target parameter, obtain a first constraint group, and solve the input parameters of the above-mentioned component cost model under the restriction of the above-mentioned first constraint group with minimization of the sum of the output parameters of the above-mentioned component cost model as the objective function to obtain first candidate parameters;

[0117] Specifically, based on the operating parameters and operating costs of each component in the virtual power plant, under the constraints of technical conditions, safety performance, economic conditions and other factors of the virtual power plant operation, the reasonable value range of the operating parameters of each component is determined, and a first constraint group is constructed. Under the constraints of the first constraint group, the input parameters of the component cost model are solved with the goal of minimizing the total operating cost of the virtual power plant, and the minimum operating cost of the virtual power plant is obtained while meeting the grid demand and maximizing economic benefits, that is, the above-mentioned first alternative parameter.

[0118] A second construction unit 40 is used to determine the correlation between the deviation between the second candidate parameter and the first candidate parameter after the virtual power plant performs the peak-shaving operation and the peak-shaving cost based on the second target parameter and the fourth target parameter, and to construct a model for simulation to obtain a peak-shaving cost model, wherein an input parameter of the peak-shaving cost model is the first candidate parameter;

[0119] Specifically, based on the operating parameters and peak-shaving costs of each component in the virtual power plant, the impact of peak-shaving operation on cost is analyzed, the relationship between peak-shaving operation and cost is determined, and the second alternative parameter after the virtual power plant performs peak-shaving operation is obtained. Based on the deviation between the second alternative parameter and the first alternative parameter, the association between the cost generated by the peak-shaving operation is constructed to obtain a peak-shaving cost model to evaluate the impact of different peak-shaving strategies on cost.

[0120] A first calculation unit 50 is used to solve the input parameters of the peak-shaving cost model under the constraints of the first constraint group by taking the minimization of the sum of the output parameters of the peak-shaving cost model as the objective function to obtain second candidate parameters;

[0121] Specifically, a peak-shaving strategy is formulated so that the total peak-shaving cost is minimized while meeting the peak-shaving demand of the power grid. Under the condition of satisfying the first constraint group, the peak-shaving cost model is solved with minimizing the peak-shaving cost as the objective function, and a second alternative parameter is obtained that can meet the peak-shaving demand of the power grid while effectively controlling the peak-shaving cost.

[0122] The third construction unit 60 is used to determine the association between the deviation between the target candidate parameter after the virtual power plant performs the frequency regulation operation and the second candidate parameter and the frequency regulation capacity based on the second target parameter and the fifth target parameter, and to construct a corresponding model to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second candidate parameter;

[0123] Specifically, according to the operating parameters and frequency regulation capacity of each component in the virtual power plant, and taking into account the frequency regulation capacity and grid frequency stability, the target parameters for frequency regulation operation of the virtual power plant are determined, the impact of the frequency regulation operation on the grid, and the deviation between the target alternative parameters and the second alternative parameters after the frequency regulation operation are analyzed, and a corresponding model is constructed to evaluate how the frequency regulation capacity of the virtual power plant changes under different frequency regulation operations, and to obtain a frequency regulation capacity model.

[0124] The second calculation unit 70 is used to solve the input parameters of the frequency regulation capacity model under the constraints of the first constraint group with the maximization of the sum of the output parameters of the frequency regulation capacity model as the objective function, to obtain target operating parameters, generate target control instructions according to the target operating parameters, and control the operation of the virtual power plant according to the target control instructions.

[0125] Specifically, under the constraints of the first constraint group, a frequency regulation capacity model is used to determine an operating parameter of a virtual power plant so as to maximize the frequency regulation capacity of the virtual power plant, and corresponding control instructions are generated according to the target operating parameters to control the operation of each component of the virtual power plant.

[0126] Through this embodiment, the first acquisition unit acquires the first target parameter, and the above-mentioned first target parameter at least includes the second target parameter, the third target parameter, the fourth target parameter and the fifth target parameter. The above-mentioned first target parameter is the operating parameter of the above-mentioned virtual power plant within the first preset time period. The end time of the above-mentioned first preset time period is the first preset time and the start time is the second preset time. The above-mentioned second target parameter includes the operating parameters of each component. The above-mentioned components include the above-mentioned thermal power unit, the above-mentioned renewable energy unit, the above-mentioned energy storage device and the above-mentioned flexible load. The above-mentioned second target parameter includes the operating cost of each of the above-mentioned components. The above-mentioned fourth target parameter includes the peak-shaving cost of each of the above-mentioned components. The five target parameters include the frequency modulation capacity of each of the above-mentioned components; the first construction unit is used to determine the first correlation between the above-mentioned operating parameters and the above-mentioned operating costs of each of the above-mentioned components according to the above-mentioned second target parameter and the above-mentioned third target parameter, and to construct a model according to the above-mentioned first correlation to perform simulation, so as to obtain a component cost model; the first determination unit determines the value range of the operating parameters of each of the above-mentioned components based on the above-mentioned second target parameter, obtains a first constraint group, minimizes the sum of the output parameters of the above-mentioned component cost model as the first objective function, and under the restriction of the above-mentioned first constraint group, calculates the input parameters of the above-mentioned component cost model when the above-mentioned first objective function is minimized, so as to obtain a first candidate parameter; the first The second construction unit determines the second alternative parameter after the virtual power plant performs peak-shaving operation based on the second target parameter and the fourth target parameter, and the second correlation between the deviation between the first alternative parameter and the peak-shaving cost, and constructs a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, wherein the input parameter of the peak-shaving cost model is the first alternative parameter; the first calculation unit minimizes the sum of the output parameters of the peak-shaving cost model as the second objective function, and calculates the input parameters of the peak-shaving cost model when the second objective function is minimized under the restriction of the first constraint group to obtain the second alternative parameter; the third construction unit constructs a model according to the second target parameter and the fourth target parameter to obtain the second alternative parameter. The five target parameters determine the target alternative parameters after the virtual power plant performs frequency regulation operation and the third correlation between the deviation between the second alternative parameters and the frequency regulation capacity, and construct a corresponding model based on the third correlation to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second alternative parameter; the second calculation unit maximizes the sum of the output parameters of the frequency regulation capacity model as the third target function, and under the restriction of the first constraint group, calculates the input parameters of the frequency regulation capacity model when the third target function is maximum to obtain the target operation parameters, generates the target control instructions based on the target operation parameters, and controls the operation of the virtual power plant based on the target control instructions.This application is based on three perspectives: day-ahead, intraday and mid-day, and considers the cost of day-ahead operation control of the virtual power plant, the cost of intraday peak-shaving operation and the cost of real-time frequency regulation operation, respectively, to construct corresponding models and then solve them in sequence with cost minimization and benefit maximization as the objective functions, thereby solving the problem of the lack of a virtual power plant control method from multiple time scales in the prior art.

[0127] The virtual power plant control device based on multi-time scale rolling adjustment includes a processor and a memory. The first acquisition unit, the first construction unit, the first determination unit, the second construction unit, the first calculation unit, the third construction unit, the second calculation unit, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The above modules are all located in the same processor; or, the above modules are located in different processors in any combination.

[0128] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the virtual power plant control can be optimized by adjusting the kernel parameters.

[0129] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0130] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant control method based on multi-time scale rolling adjustment.

[0131] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the virtual power plant control method based on multi-time scale rolling adjustment when running.

[0132] An embodiment of the present invention provides a virtual power plant control system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of the above-mentioned virtual power plant control method based on multi-time scale rolling adjustment.

[0133] The present application also provides a computer program product which, when executed on a data processing device, is suitable for executing a program that initializes at least the above-mentioned virtual power plant control method steps based on multi-time scale rolling adjustment.

[0134] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0142] 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.

[0143] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0144] The virtual power plant control method based on multi-time scale rolling adjustment of the present application firstly obtains a first target parameter, the first target parameter at least includes a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, the first target parameter is the operating parameter of the virtual power plant within a first preset time period, the end time of the first preset time period is the first preset time period and the start time is the second preset time period, the second target parameter includes the operating parameters of each component, the component includes the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter includes the operating cost of each component, the fourth target parameter includes the peak load cost of each component, and the fifth target parameter includes the frequency regulation capacity of each component; then, the correlation between the operating parameters and the operating cost of each component is determined according to the second target parameter and the third target parameter, and a model is constructed for simulation to obtain a component cost model; then, the value range of the operating parameters of each component is determined based on the second target parameter to obtain a first constraint group, and the component cost model is simulated under the restriction of the first constraint group by taking the minimization of the sum of the output parameters of the component cost model as the objective function. The input parameters of the model are solved to obtain the first candidate parameters; then, based on the second target parameter and the fourth target parameter, the relationship between the deviation between the second candidate parameter and the first candidate parameter after the virtual power plant performs the peak-shaving operation and the peak-shaving cost is determined, and a model is constructed for simulation to obtain a peak-shaving cost model, wherein the input parameter of the peak-shaving cost model is the first candidate parameter; then, with the sum of the output parameters of the peak-shaving cost model minimized as the objective function, the input parameters of the peak-shaving cost model are solved under the constraints of the first constraint group to obtain the second candidate parameters; then, based on the second target parameter The target parameter and the fifth target parameter determine the correlation between the deviation between the target candidate parameter and the second candidate parameter after the frequency regulation operation of the virtual power plant, and the frequency regulation capacity, and construct a corresponding model to obtain a frequency regulation capacity model, the input parameter of the frequency regulation capacity model is the second candidate parameter; finally, the sum of the output parameters of the frequency regulation capacity model is maximized as the objective function under the restriction of the first constraint group, the input parameters of the frequency regulation capacity model are solved to obtain the target operation parameters, and the target control instructions are generated according to the target operation parameters, and the operation of the virtual power plant is controlled according to the target control instructions. This application is based on three perspectives: day-ahead, intraday and mid-day, and considers the cost of day-ahead operation control of the virtual power plant, the cost of intraday peak-shaving operation and the cost of real-time frequency regulation operation, respectively, to construct corresponding models, and then solves them in turn with cost minimization and benefit maximization as the objective functions, which solves the problem of lack of a virtual power plant control method from multiple time scales in the prior art.

[0145] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A virtual power plant control method based on multi-time scale rolling adjustment, characterized in that: The virtual power plant comprises at least a thermal power unit, a renewable energy unit, an energy storage device and a flexible load, and the method comprises: Obtaining a first target parameter, the first target parameter at least includes a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, the first target parameter is an operating parameter of the virtual power plant within a first preset time period, the end time of the first preset time period is the first preset time period and the start time is the second preset time period, the second target parameter includes an operating parameter of each component element, the component elements include the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter includes an operating cost of each component element, the fourth target parameter includes a peak regulation cost of each component element, and the fifth target parameter includes a frequency regulation capacity of each component element; Determining a first correlation between the operating parameter and the operating cost of each of the component elements according to the second target parameter and the third target parameter, and constructing a model according to the first correlation to perform simulation to obtain a component cost model; Determine the value range of the operating parameters of each of the component elements based on the second target parameter to obtain a first constraint group, minimize the sum of the output parameters of the component cost model as a first target function, and calculate the input parameters of the component cost model when the first target function is minimized under the restriction of the first constraint group to obtain a first candidate parameter; Determine a second candidate parameter after the virtual power plant performs peak-shaving operation based on the second target parameter and the fourth target parameter, and a second correlation between the deviation between the first candidate parameter and the peak-shaving cost, and construct a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, wherein an input parameter of the peak-shaving cost model is the first candidate parameter; Taking the minimization of the sum of the output parameters of the peak-shaving cost model as the second objective function, under the restriction of the first constraint group, calculating the input parameters of the peak-shaving cost model when the second objective function is minimized, and obtaining second candidate parameters; Determine the target candidate parameter after the virtual power plant performs frequency regulation operation based on the second target parameter and the fifth target parameter, and determine the third correlation relationship between the deviation between the second candidate parameter and the frequency regulation capacity, and construct a corresponding model according to the third correlation relationship to obtain a frequency regulation capacity model, wherein the input parameter of the frequency regulation capacity model is the second candidate parameter; The third objective function is to maximize the sum of the output parameters of the frequency regulation capacity model. Under the constraints of the first constraint group, the input parameters of the frequency regulation capacity model when the third objective function is maximum are calculated to obtain target operating parameters, and target control instructions are generated according to the target operating parameters. The operation of the virtual power plant is controlled according to the target control instructions.

2. The method according to claim 1, characterized in that The renewable energy unit includes a wind turbine unit and a photovoltaic unit. The first correlation between the operating parameter and the operating cost of each component is determined according to the second target parameter and the third target parameter, and a model is constructed according to the first correlation to perform simulation to obtain a component cost model, including: A first power generation power, an operating state parameter, and a first cost are obtained, the operating state is used as an input parameter, and a first target cost is used as an output parameter. A model corresponding to the thermal power unit is constructed according to the first power generation power, the operating state parameter, and the first cost to obtain a first component cost model, wherein the first power generation power is the rated power of the thermal power unit, the operating state parameter is a binary variable of the operating state of the thermal power unit, the first cost is the startup cost of the thermal power unit, and the first target cost is the total cost of the thermal power unit: Among them, D Th,t For the first cost, Q Th,t is the first generated power, a Th 、b Th and c Th is the operating coefficient of the thermal power unit, u Th,t is the operating status of the thermal power unit at time t, is the fuel cost of the thermal power unit, is the start-up and shutdown cost of the thermal power unit, r Th is the total revenue of the thermal power unit, is the real-time unit electricity price; The second generated power, the first wind speed, the second wind speed, the third wind speed, the real-time wind speed, the second cost, the sunshine intensity, the solar absorption rate, the solar conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost are obtained. The real-time wind speed and the sunshine intensity are used as input parameters, and the second target cost is used as an output parameter. According to the second generated power, the first wind speed, the second wind speed, the third wind speed, the second cost, the solar absorption rate, the solar conversion rate, the photovoltaic power generation efficiency, the photovoltaic panel area, the loss ratio and the third cost, a model corresponding to the renewable energy unit is constructed to obtain a second component cost model. The second generated power is the rated power of the wind turbine unit. The first wind speed is the cut-in wind speed of the wind turbine unit. The second wind speed is the cut-out wind speed of the wind turbine unit. The third wind speed is the rated wind speed of the wind turbine unit. The first wind speed is less than the third wind speed. The third wind speed is less than the second wind speed. The second cost is the operating cost of the wind turbine unit. The third cost is the operating cost of the photovoltaic unit. The second target cost is the total cost of the renewable energy unit: Among them, P t WPP is the actual output power of the wind turbine at time t, v t is the real-time wind speed, v in is the first wind speed, v out is the second wind speed, is the second power generation, v ra is the third wind speed, P t PV is the actual output power of the photovoltaic unit at time t, is the sunshine intensity, η PV is the photovoltaic power generation efficiency, loss is the loss ratio, μ inv is the solar energy conversion efficiency, μ sor is the solar energy absorption rate, S PV is the photovoltaic panel area, and are the depreciation costs of the wind turbine and the photovoltaic unit at time t, and are the actual output power of the wind turbine and the photovoltaic unit at time t, respectively, WPP and p PV The second cost and the third cost per unit power of the wind turbine generator set and the photovoltaic generator set respectively; Obtain a target capacity, a first target power, a second target power, and a fourth cost, determine the first target power or the second target power as an input parameter, determine the third target cost as an output parameter, and construct a model corresponding to the energy storage device according to the target capacity, the first target power, the second target power, and the fourth cost to obtain a third component cost model, wherein the target capacity is the capacity of the energy storage device, the first target power is the charging power of the energy storage device, the second target power is the discharging power of the energy storage device, the fourth cost is the operating cost of the energy storage device, and the third target cost is the total cost of the energy storage device: C ESS =c4*(ES t+1 -IS t ); Among them, ES t+1 and ES t denote the target capacity at time t+1 and time t, respectively, δ chr and δ disc are the charging and discharging efficiency of the energy storage device, P chr and P disc are the first target power and the second target power of the energy storage device respectively, Δt is the time interval between time t+1 and time t, C ESS is the third target cost, c4 is the fourth cost; A target transfer amount and a target price are obtained, and a model corresponding to the flexible load is constructed with the target transfer amount as an input parameter and a fourth target cost as an output parameter to obtain a fourth component cost model. The target transfer amount is the load transfer amount or the load transfer amount of the flexible load, the target price is the load unit transfer price, and the fourth target cost is the total cost of the flexible load: in, is the target transfer amount of the nth load at time t, The initial load transfer of the nth load at time t, M is the price demand elasticity matrix, is a binary variable for load transfer in and out, represents the rate of change of load at time t corresponding to J hours, where p J (t) represents the load change at time t in J hours, is the initial load state corresponding to the beginning of hour J, is the load transfer amount of the nth load at time t, is the load transfer amount of the nth load at time t, is the target price, C DR is the fourth target cost.

3. The method according to claim 2, characterized in that Determining the value range of the operating parameter of each of the component elements based on the second target parameter to obtain a first constraint group includes: The load transfer constraint of the flexible load is constructed to obtain the first objective constraint: in, is the maximum transfer amount of the flexible load; The operation constraint of the energy storage device is constructed to obtain the second objective constraint: in, and are the minimum and maximum values ​​of the charging power of the energy storage device, respectively. and are the minimum and maximum discharge power of the energy storage device, ES min and ES max are the lower and upper limits of the capacity of the electric energy storage device, respectively; The virtual power plant system balance constraint is constructed to obtain the third objective constraint: Where D is the load demand value of the virtual power plant, Q CHP is the active power output of the thermal power unit, Q j,WPP is the active power output of wind turbine j, Q m,pv is the active power output of the photovoltaic unit m; The output constraint of the thermal power unit is constructed to obtain the fourth objective constraint: in, and are respectively the maximum dispatchable output and the minimum dispatchable output of the thermal power unit; The climbing constraint of the thermal power unit is constructed to obtain the fifth objective constraint: in, and They are respectively the lower limit and the upper limit of the ramp rate of the thermal power unit within the unit monitoring period; The output constraint of the wind turbine generator set is constructed to obtain the sixth objective constraint: in, and The maximum dispatchable output and the minimum dispatchable output of the wind turbine generator set are processed respectively; The output constraint of the photovoltaic unit is constructed to obtain the seventh objective constraint: in, and are respectively the maximum dispatchable output and the minimum dispatchable output of the photovoltaic unit; The backup capacity constraint of the virtual power plant is constructed to obtain the eighth objective constraint: Where G is the set of all generating units, D(t) is the load demand of the system at time t, R(t) is the reserve demand of the system at time t, l is the line loss rate of the virtual power plant, θ is the self-use rate of the unit, Q max (t) is the maximum output of the generator set at time t, and the generator set includes the thermal power unit and the renewable energy unit; The first objective constraint, the second objective constraint, the third objective constraint, the fourth objective constraint, the fifth objective constraint, the sixth objective constraint, the seventh objective constraint and the eighth objective constraint are combined to obtain the first constraint group.

4. The method according to claim 2, characterized in that: Determining a second candidate parameter after the virtual power plant performs a peak-shaving operation based on the second target parameter and the fourth target parameter and a second correlation relationship between the deviation between the first candidate parameters and the peak-shaving cost, and constructing a model according to the second correlation relationship for simulation to obtain a peak-shaving cost model, including: Based on the second target parameter and the fourth target parameter, the peak load cost model is constructed: Among them, F t de is the peak load cost at time t, which is determined by the deviation between the quotation of the virtual power plant and the actual electricity price at time t, is the estimated sales volume of the virtual power plant at time t, P t VPP is the actual amount of electricity sold by the virtual power plant at time t, κ is the upper limit of the deviation, λ de is the penalty coefficient, is the clearing price at time t.

5. The method according to claim 4, characterized in that Before obtaining the first candidate parameters by minimizing the sum of the output parameters of the component cost model as the first objective function and calculating the input parameters of the component cost model when the first objective function is minimized under the first constraint group, the method further includes: According to the peak load cost model, a corresponding objective function is constructed to obtain the second objective function: in, and are the cost of adjusting the virtual power plant upward / downward at time h, are the upward / downward adjustment requirements of the virtual power plant at time h, and are the cost prices of adjusting the thermal power unit, the energy storage device and the flexible load at time h, and are the upward adjustment amounts of the thermal power unit, the energy storage device and the flexible load at time h, and The amounts by which the thermal power unit, the energy storage device and the flexible load are respectively adjusted downward at time h.

6. The method according to claim 5, characterized in that Determining the target candidate parameters after the virtual power plant performs frequency regulation operation based on the second target parameter and the fifth target parameter and a third correlation relationship between the deviation between the second candidate parameters and the frequency regulation capacity, and constructing a corresponding model according to the third correlation relationship to obtain a frequency regulation capacity model, including: Based on the second target parameter and the fifth target parameter, construct the frequency modulation capacity model: in, and are the virtual power plant at t n The capacity to adjust down and up during the period, The virtual power plant is n Ancillary service settlement price for the period, t n The starting time of the time period is time t.

7. The method according to claim 6, characterized in that Before obtaining the target operating parameters by maximizing the sum of the output parameters of the frequency regulation capacity model as a third objective function and calculating the input parameters of the frequency regulation capacity model when the third objective function is maximized under the constraints of the first constraint group, the method further includes: According to the frequency modulation capacity model, a corresponding objective function is constructed to obtain the third objective function: Among them, N X is the total frequency regulation times of the virtual power plant in one day, t n The time interval for frequency modulation of the virtual power plant.

8. A virtual power plant control device based on multi-time scale rolling adjustment, characterized in that: The virtual power plant at least includes a thermal power unit, a renewable energy unit, an energy storage device and a flexible load, and the device includes: a first acquisition unit, for acquiring a first target parameter, the first target parameter at least including a second target parameter, a third target parameter, a fourth target parameter and a fifth target parameter, the first target parameter being an operating parameter of the virtual power plant within a first preset time period, the end time of the first preset time period being the first preset time period and the start time being the second preset time period, the second target parameter including an operating parameter of each component element, the component elements including the thermal power unit, the renewable energy unit, the energy storage device and the flexible load, the second target parameter including an operating cost of each component element, the fourth target parameter including a peak regulation cost of each component element, and the fifth target parameter including a frequency regulation capacity of each component element; a first construction unit, configured to determine a first correlation between the operating parameter and the operating cost of each of the component elements according to the second target parameter and the third target parameter, and to construct a model according to the first correlation to perform simulation and obtain a component cost model; A first determination unit is used to determine the value range of the operating parameter of each of the component elements based on the second target parameter, obtain a first constraint group, minimize the sum of the output parameters of the component cost model as a first objective function, and under the restriction of the first constraint group, calculate the input parameters of the component cost model when the first objective function is minimized to obtain a first candidate parameter; a second construction unit, for determining, based on the second target parameter and the fourth target parameter, a second candidate parameter after the virtual power plant performs a peak-shaving operation and a second correlation between the deviation between the first candidate parameter and the peak-shaving cost, and constructing a model according to the second correlation to perform simulation to obtain a peak-shaving cost model, wherein an input parameter of the peak-shaving cost model is the first candidate parameter; A first calculation unit is configured to take the sum of the output parameters of the peak-shaving cost model as the second objective function, and under the constraints of the first constraint group, calculate the input parameters of the peak-shaving cost model when the second objective function is minimized to obtain second candidate parameters; a third construction unit, configured to determine, based on the second target parameter and the fifth target parameter, a third correlation relationship between a target candidate parameter after the virtual power plant performs frequency regulation operation and a deviation between the second candidate parameter and the frequency regulation capacity, and to construct a corresponding model according to the third correlation relationship to obtain a frequency regulation capacity model, wherein an input parameter of the frequency regulation capacity model is the second candidate parameter; The second calculation unit is used to maximize the sum of the output parameters of the frequency regulation capacity model as the third objective function, calculate the input parameters of the frequency regulation capacity model when the third objective function is maximum under the constraints of the first constraint group, obtain target operating parameters, generate target control instructions according to the target operating parameters, and control the operation of the virtual power plant according to the target control instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A virtual power plant control system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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