Virtual power plant regulation and control method and device based on nonlinear operation and risk

Abstract

The invention provides a non-linear operation and risk-based virtual power plant regulation and control method and device, and the method comprises the steps: obtaining historical operation data which at least comprises a power plant regulation and control parameter, a power plant cost parameter and a risk interference parameter; constructing a power plant operation model according to the parameters, and simulating the change trend of the net profit of the virtual power plant along with the power plant regulation and control parameters and the risk interference parameters; constructing a first constraint group and a second constraint group, the first constraint group being used for performing nonlinear limitation on an association relationship between the power plant regulation and control parameters and the net profit of the power plant operation model, and the second constraint group being used for performing nonlinear limitation on an association relationship between the risk interference parameters and the net profit of the power plant operation model; and solving the power plant operation model by taking the maximum net profit as a target function based on the two constraint groups to obtain operation parameters, and controlling the operation of the virtual power plant. The method solves the problem that a virtual power plant regulation and control method integrating the volatility and uncertainty of a virtual power plant is lacked in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of integrated resource planning of virtual power plants, and in particular to a control method, a control device, a computer-readable storage medium and a control system for virtual power plants based on nonlinear operation and risk. Background Art

[0002] The volatility and uncertainty of renewable energy sources hinder their participation in the electricity market. This problem can be addressed to some extent by operating renewable generation units in conjunction with other technologies such as conventional generation units, storage facilities and flexible demand. The aggregation of these energy resources has given rise to the concept of virtual power plants (VPPs), which operate as a single entity with a single operating mode. The aggregation of assets provided by virtual power plants paves the way for smarter power systems by gaining economies of scale, thereby improving the efficiency of power plant regulation and minimizing costs. Identifying the most favorable investment options for strategic VPP owners is challenging due to the lack of a direct relationship between investment decisions and expected profits. This problem mainly stems from the presence of long-term and short-term sources of uncertainty, which makes the future operation of the expanded VPP highly uncertain, as well as the nonlinearity of the operation of VPP components, which makes the operation of VPPs very complex. In order to make wise investment decisions, future techno-economic conditions such as renewable energy generation availability, demand levels, production costs and market prices should be correctly described within the decision framework, while the operation of VPP components should be accurately modeled. In addition, the high volatility of VPP profit distribution requires the integration of risk management into the decision-making process. Summary of the invention

[0003] The main purpose of this application is to provide a control method, a control device, a computer-readable storage medium and a control system for a virtual power plant based on nonlinear operation and risk, so as to at least solve the problem in the prior art of the lack of a virtual power plant control method that takes into account the volatility and uncertainty of the virtual power plant.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a control method for a virtual power plant based on nonlinear operation and risk is provided, including: obtaining historical operation data, the historical operation data at least including power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least including electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the risk interference parameters at least including risk value data under each risk event; constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, the power plant operation model is used to simulate the net profit of the virtual power plant as the power plant control parameters and the risk interference parameters change. ; construct a first constraint group and a second constraint group, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints; based on the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit as the objective function to obtain the power plant operation parameters, and the operation of the virtual power plant is controlled according to the power plant operation parameters.

[0005] Optionally, a power plant operation model is constructed according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, including: determining a target price, a first electricity quantity, a first coefficient, a first variable, a first cost, a second electricity quantity, a second cost, a second variable, a third cost and a third variable according to the power plant control parameters, the target price is the price of the electricity sold by the virtual power plant, the first electricity quantity is the electricity sold by the virtual power plant, the first coefficient is a preset cost coefficient of a non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the first cost is the additional cost of the non-new energy unit that changes with the power generation, and the second electricity quantity is the power generation of the non-new energy unit , the second cost is the startup cost of the non-new energy unit, the second variable is a binary variable of whether the non-new energy unit is started, the third cost is the shutdown cost of the non-new energy unit, and the third variable is a binary variable of whether the non-new energy unit is shut down; the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable are determined according to the power plant cost parameters, the fourth cost is the construction cost of the non-new energy unit, the fourth variable is a binary variable of whether the non-new energy unit is built, the fifth cost is the construction cost of the new energy unit, the fourth variable is a binary variable of whether the new energy unit is built, and the sixth cost is the construction cost of the energy storage device , the sixth variable is a binary variable indicating whether the energy storage device is built; the risk value data and the preset variables are determined according to the risk interference parameters; a first target formula is constructed according to the target price and the first power quantity, and the first target formula is used to simulate the change trend of the income of the virtual power plant with the target price and the first power quantity; a second target formula is constructed according to the first coefficient, the first variable, the first cost, the second power quantity, the second cost, the second variable, the third cost and the third variable; a third target formula is constructed according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable, and the second target formula is used to simulate The operating cost of the virtual power plant is simulated to change with the first coefficient, the first variable, the first cost, the second electricity, the third cost and the third variable. The third target formula is used to simulate the construction cost of the virtual power plant to change with the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable. A fourth target formula is constructed according to the risk interference parameter and the preset variable. The fourth target formula is used to characterize the risk penalty changing with the risk interference parameter and the preset variable. The power plant operation model is obtained by combining the first target formula, the second target formula, the third target formula and the fourth target formula.

[0006] Optionally, constructing a first target formula according to the target price and the first power includes: constructing the first target formula based on a correlation between the target price and the first power and the income: Where A is the income, is the target price, is the first electrical quantity.

[0007] Optionally, constructing a second target formula according to the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable includes: constructing the second target formula based on the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and an association between the third variable and the operating cost: Wherein, B is the operating cost, C is the set of non-new energy units, is the first coefficient, is the first variable, For the first cost, is the second electric quantity, For the second cost, is the second variable, For the third cost, is the third variable; based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the association between the sixth variable and the construction cost, the third target formula is constructed: Wherein, C is the construction cost, It is the collection of non-new energy units that are expected to be built. For the fourth cost, is the fourth variable, The collection of the new energy units planned to be built, For the fifth cost, is the fifth variable, T is the set of energy storage devices expected to be constructed, For the sixth cost, is the sixth variable.

[0008] Optionally, constructing a fourth target formula according to the risk intervention parameter and the preset variable includes: constructing the fourth target formula based on an association relationship between the risk intervention parameter and the preset variable and the risk penalty: Where D is the risk penalty, β is the preset coefficient, α is the confidence level, θ is the risk value data, ξ ω is the preset variable, π ωis the probability of the scenario occurring, and Ω is the total number of the scenarios.

[0009] Optionally, combining the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model includes: combining the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model: Where ψ is the net profit, F is the set of representative days, N t is the number of days, and H is the set of hourly time periods.

[0010] Optionally, constructing the first constraint group and the second constraint group includes: constructing the investment amount constraint of the virtual power plant corresponding to the power plant operation model to obtain a first target constraint: in, is the set of energy storage units to be built, TC is the total investment amount; construct the power balance constraint of the virtual power plant corresponding to the power plant operation model, and obtain the second objective constraint: Among them, R is the set of new energy units, S is the set of energy storage units, is the power generation of the new energy unit, are the amount of electricity charged and discharged by the energy storage device, is the power load; constructing the unit operation constraints of the virtual power plant corresponding to the power plant operation model, and obtaining the third target constraint:

[0011] in, is the power generation status of the unit at the last moment, is the power generation of the unit at the last moment, and are the upper and lower limits of the power generation of non-renewable energy units, and is the up and down climbing power of the non-new energy unit, and is the start-stop power of the non-new energy unit; constructing the demand response constraint of the virtual power plant corresponding to the power plant operation model to obtain the fourth target constraint: in, and are the upper and lower limits of demand response, is the daily minimum demand; the first target constraint, the second target constraint, the third target constraint and the fourth target constraint are combined to obtain the first constraint group; the event risk constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain the second constraint group:

[0012] According to another aspect of the present application, a control device for a virtual power plant based on nonlinear operation and risk is provided, the device comprising: an acquisition unit, used to acquire historical operation data, the historical operation data at least including power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least including electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, the risk interference parameters at least including risk value data under each risk event; a first construction unit, used to construct a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, the power plant operation model being used to simulate the net profit of the virtual power plant as the power plant control parameters and the risk interference parameters change. trend; a second construction unit, used to construct a first constraint group and a second constraint group, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints; a calculation unit, used to solve the power plant operation model based on the first constraint group and the second constraint group with the maximum net profit as the objective function, to obtain the power plant operation parameters, and control the operation of the virtual power plant according to the power plant operation parameters.

[0013] 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 device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] 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 a method for executing a method described.

[0015] Applying the technical solution of the present application, in the above-mentioned control method of the virtual power plant based on nonlinear operation and risk, first, historical operation data is obtained, and the above-mentioned historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, and the above-mentioned power plant control parameters at least include electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the above-mentioned risk interference parameters at least include risk value data under various risk events; then, a power plant operation model is constructed according to the above-mentioned power plant control parameters, the above-mentioned power plant cost parameters and the above-mentioned risk interference parameters, and the above-mentioned power plant operation model is used to simulate the net profit of the above-mentioned virtual power plant as the above-mentioned power plant control parameters and the above-mentioned risk interference parameters change trend; then Then, a first constraint group and a second constraint group are constructed, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints. Finally, based on the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit as the objective function to obtain the power plant operation parameters, and the operation of the virtual power plant is controlled according to the power plant operation parameters. This application is based on converting the operating parameters of the virtual power plant into continuous variables and relating them to the virtual power plant revenue. In addition, the operating status, power generation and investment decisions are combined for nonlinear modeling. In addition, risk values ​​under different scenarios and the correlation between scenario-related profits are introduced for nonlinear modeling. Simulation is performed based on a scenario random framework to characterize the long-term uncertainty of production costs, demand levels and market prices, as well as the gas outage uncertainty of market prices and renewable energy generation points, to achieve a decision-making model that integrates long-term and short-term uncertainties and risk interference during emergencies, and to regulate the virtual power plant based on the decision-making model. This method solves the problem in the prior art of the lack of a virtual power plant regulation method that integrates the volatility and uncertainty of virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal according to a control method of a virtual power plant based on nonlinear operation and risk provided in an embodiment of the present application is shown;

[0017] Figure 2 A flow chart of a control method for a virtual power plant based on nonlinear operation and risk provided in accordance with an embodiment of the present application is shown;

[0018] Figure 3A structural block diagram of a control device for a virtual power plant based on nonlinear operation and risk provided according to an embodiment of the present application is shown.

[0019] The above drawings include the following reference numerals:

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0025] CVaR model: Conditional Value at Risk model is a method used to measure the risk of financial assets or investment portfolios.

[0026] As introduced in the background technology, the high volatility of profit distribution of virtual power plants in the prior art requires that risk management be incorporated into the decision-making process. In order to solve the problem that the prior art lacks a virtual power plant control method that takes into account the volatility and uncertainty of virtual power plants, the embodiments of the present application provide a virtual power plant control method, control device, computer-readable storage medium, processor and control system based on nonlinear operation and risk.

[0027] 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.

[0028] 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 control method of a virtual power plant based on nonlinear operation and risk in 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 only for illustration 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.

[0029] 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.

[0030] In this embodiment, a control method for a virtual power plant based on nonlinear operation and risk 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.

[0031] Figure 2 FIG. 1 is a flow chart of a control method of a virtual power plant based on nonlinear operation and risk according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0032] Step S201, obtaining historical operation data, the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0033] Specifically, the present application considers regulation from three aspects: power plant control parameters, power plant cost parameters, and risk interference parameters of the virtual power plant operation, at least using electricity market prices, electricity interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs as power plant control parameters, and at least using risk value data under each risk event as risk interference parameters.

[0034] Step S202, constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the net profit of the virtual power plant as the power plant control parameters and the risk interference parameters change;

[0035] Specifically, a power plant operation model is constructed based on the historical operation data of the power plant. The net profit of the virtual power plant under different power plant control parameters and different risk interference parameters is obtained through the power plant operation model, so as to obtain the changing trend of the net profit of the virtual power plant with the above-mentioned power plant control parameters and the above-mentioned risk interference parameters.

[0036] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, wherein the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints;

[0037] Specifically, according to the total investment amount TC, the fourth cost The fourth variable Fifth cost The fifth variable Sixth Cost and the sixth variable Construct investment quota constraints based on the first electricity Second power Power generation of new energy units Energy storage device charge Discharge capacity of energy storage device and power load Construct a power balance constraint based on the first variable Second variable The third variable The power generation status of the unit at the last moment, the second power The fourth variable The power generation of the unit at the last moment, and the upper and lower limits of the power generation of non-new energy units and Up and down ramp power of non-new energy units and Start-stop power of non-new energy units and Construct unit operation constraints based on upper and lower limits of demand response and Minimum daily demand and power load Construct demand response constraints. The above investment limit constraints, power balance constraints, unit operation constraints and demand response constraints constitute the first constraint group. According to income A, operating cost B, construction cost C and number of days N t Construct an event risk constraint group, and the above event risk constraints constitute the second constraint group.

[0038] Step S204, solving the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function to obtain power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters;

[0039] Specifically, based on the above-mentioned first constraint group and the above-mentioned second constraint group, simulations are performed to characterize the long-term uncertainties of future production costs, demand levels and market prices, as well as the short-term changes in market prices and renewable energy power generation levels. The above-mentioned maximum net profit is taken as the objective function, and the power plant operating parameters are obtained by solving the above-mentioned power plant operation model and controlling the operation of the virtual power plant.

[0040] Through this embodiment, first, historical operation data is obtained, and the above historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, and the above power plant control parameters at least include electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the above risk interference parameters at least include risk value data under each risk event; then, a power plant operation model is constructed according to the above power plant control parameters, the above power plant cost parameters and the above risk interference parameters, and the above power plant operation model is used to simulate the net profit of the above virtual power plant with the change trend of the above power plant control parameters and the above risk interference parameters; then, a first constraint group and a second constraint group are constructed. The first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints. Finally, based on the first constraint group and the second constraint group, the power plant operation model is solved with the maximum net profit as the objective function to obtain the power plant operation parameters, and the operation of the virtual power plant is controlled according to the power plant operation parameters. This application is based on converting the operating parameters of the virtual power plant into continuous variables and relating them to the virtual power plant revenue. In addition, the operating status, power generation and investment decisions are combined for nonlinear modeling. In addition, risk values ​​under different scenarios and the correlation between scenario-related profits are introduced for nonlinear modeling. Simulation is performed based on a scenario random framework to characterize the long-term uncertainty of production costs, demand levels and market prices, as well as the gas outage uncertainty of market prices and renewable energy generation points, to achieve a decision-making model that integrates long-term and short-term uncertainties and risk interference during emergencies, and to regulate the virtual power plant based on the decision-making model. This method solves the problem in the prior art of the lack of a virtual power plant regulation method that integrates the volatility and uncertainty of virtual power plants.

[0041] In order to construct a power plant operation model, in an optional implementation, the above step S202 includes:

[0042] Step S2021, determining the target price, the first electricity, the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable according to the power plant control parameters, the target price is the price of the electricity sold by the virtual power plant, the first electricity is the electricity sold by the virtual power plant, the first coefficient is the preset cost coefficient of the non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the first cost is the additional cost of the non-new energy unit that changes with the power generation, the second electricity is the power generation of the non-new energy unit, the second cost is the startup cost of the non-new energy unit, the second variable is a binary variable of whether the non-new energy unit is started, the third cost is the shutdown cost of the non-new energy unit, and the third variable is a binary variable of whether the non-new energy unit is shut down;

[0043] Specifically, the price of electricity sold by the virtual power plant determined based on the above-mentioned power plant control parameters is used to obtain the above-mentioned target price, the electricity sold by the virtual power plant is used to obtain the above-mentioned first electricity, the preset cost coefficient of the non-new energy unit is used to obtain the above-mentioned first coefficient, the binary variable of whether the non-new energy unit generates electricity is used to obtain the above-mentioned first variable, the additional cost of the non-new energy unit that changes with the power generation is used to obtain the above-mentioned first cost, the power generation of the non-new energy unit is used to obtain the above-mentioned second electricity, the startup cost of the non-new energy unit is used to obtain the above-mentioned second cost, the binary variable of whether the non-new energy unit is started is used to obtain the above-mentioned second variable, the shutdown cost of the non-new energy unit is used to obtain the third cost, and the binary variable of whether the non-new energy unit is shut down is used to obtain the third variable.

[0044] Step S2022, determining the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable according to the power plant cost parameters, the fourth cost is the construction cost of the non-new energy unit, the fourth variable is a binary variable of whether the non-new energy unit is built, the fifth cost is the construction cost of the new energy unit, the fifth variable is a binary variable of whether the new energy unit is built, the sixth cost is the construction cost of the energy storage device, and the sixth variable is a binary variable of whether the energy storage device is built;

[0045] Specifically, the construction cost of the non-new energy unit determined based on the above cost parameters obtains the above fourth cost, the binary variable of whether the non-new energy unit is built obtains the above fourth variable, the construction cost of the new energy unit obtains the above fifth cost, the binary variable of whether the new energy unit is built obtains the above fifth variable, the construction cost of the energy storage device obtains the above sixth cost, the binary variable of whether the energy storage device is built obtains the above sixth variable.

[0046] Step S2023, determining risk value data and preset variables according to the above risk interference parameters;

[0047] Specifically, according to the above-mentioned risk intervention parameters, at least risk value data and preset variables under each risk event are obtained to establish the correlation relationship between the above-mentioned risk intervention parameters and the above-mentioned preset variables and the above-mentioned risk penalties based on the CVaR model (conditional risk value model).

[0048] Step S2024, constructing a first target formula according to the target price and the first quantity of electricity, wherein the first target formula is used to simulate a change trend of the income of the virtual power plant with the target price and the first quantity of electricity;

[0049] Specifically, based on the price of electricity sold by the virtual power plant determined by the above-mentioned power plant control parameters, i.e., the above-mentioned target price, and the electricity sold by the virtual power plant, i.e., the above-mentioned first electricity, a correlation relationship is established between the income of the above-mentioned virtual power plant and the above-mentioned target price and the above-mentioned first electricity, and then the income of the above-mentioned simulated power plant is determined according to the above-mentioned target price and the above-mentioned first electricity.

[0050] Step S2025, constructing a second target formula according to the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable, and constructing a third target formula according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable, the second target formula is used to simulate the changing trend of the operating cost of the virtual power plant with the first coefficient, the first variable, the first cost, the second electricity, the third cost and the third variable, and the third target formula is used to simulate the changing trend of the construction cost of the virtual power plant with the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable;

[0051] Specifically, based on the preset cost coefficient of the non-new energy unit determined by the above-mentioned power plant control parameters, that is, the above-mentioned first coefficient, the binary variable of whether the non-new energy unit generates electricity, that is, the above-mentioned first variable, the additional cost of the non-new energy unit that changes with the power generation, that is, the above-mentioned first cost, the power generation of the non-new energy unit, that is, the above-mentioned second amount of electricity, the startup cost of the non-new energy unit, that is, the above-mentioned second cost, the binary variable of whether the non-new energy unit is started, that is, the above-mentioned second variable, the shutdown cost of the non-new energy unit, that is, the third cost, and the binary variable of whether the non-new energy unit is shut down, that is, the third variable, establish the correlation between the operation and operation costs of the virtual power plant energy units, and then according to the above-mentioned first coefficient, the above-mentioned first variable, the above-mentioned first cost, the above-mentioned second amount of electricity, the above-mentioned third variable The second cost, the second variable, the third cost and the third variable determine the operating cost of the virtual power plant, the construction cost of the non-new energy unit determined based on the above cost parameters, that is, the fourth cost, the binary variable of whether the non-new energy unit is built, that is, the fourth variable, the construction cost of the new energy unit, that is, the fifth cost, the binary variable of whether the new energy unit is built, that is, the fifth variable, the construction cost of the energy storage device, that is, the sixth cost, the binary variable of whether the energy storage device is built, that is, the sixth variable, establish the correlation between the energy units and the construction cost of the virtual power plant, and then determine the construction cost of the virtual power plant according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable.

[0052] Step S2026, constructing a fourth target formula according to the risk interference parameter and the preset variable, wherein the fourth target formula is used to characterize the change trend of the risk penalty with the risk interference parameter and the preset variable;

[0053] Specifically, based on the correlation between the risk interference parameter and the preset variable and the risk penalty, a relationship between the risk penalty and the preset variable is constructed, and the risk penalty is determined according to the preset variable.

[0054] Step S2027, combining the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model;

[0055] Specifically, the power plant operating model is obtained by combining the revenue of the virtual power plant calculated by the first target formula, the operating cost of the virtual power plant calculated by the second target formula, the construction cost of the virtual power plant calculated by the third target formula, and the risk penalty of the virtual power plant calculated by the fourth target formula, and then the net profit of the virtual power plant under different operating parameters is calculated based on the power plant operating model.

[0056] In order to construct the first target formula, in an optional implementation, the above step S2024 includes:

[0057] Step S20241, based on the target price and the correlation between the first electricity quantity and the income, construct the first target formula:

[0058]

[0059] Among them, A is the above income, For the above target price, is the first electrical quantity mentioned above.

[0060] In order to construct the second target formula, in an optional implementation, the above step S2025 includes:

[0061] Step S20251, constructing the second target formula based on the first coefficient, the first variable, the first cost, the second power, the second cost, the second variable, the third cost and the relationship between the third variable and the operating cost:

[0062]

[0063] Among them, B is the above operating cost, C is the collection of the above non-new energy units, is the first coefficient mentioned above, is the first variable mentioned above, is the first cost mentioned above, is the second electrical quantity mentioned above, is the second cost mentioned above, is the second variable mentioned above, For the third cost mentioned above, is the third variable mentioned above;

[0064] Step S20252, constructing the third target formula based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost, and the relationship between the sixth variable and the construction cost:

[0065]

[0066] Among them, C is the above construction cost, For the collection of the above non-new energy units planned to be built, For the fourth cost mentioned above, is the fourth variable mentioned above, For the collection of the above-mentioned new energy units to be built, For the fifth cost mentioned above, is the fifth variable, T is the set of energy storage devices planned to be constructed, For the sixth cost mentioned above, is the sixth variable mentioned above.

[0067] In order to construct the fourth target formula, in an optional implementation, the above step S2026 includes:

[0068] Step S20261, based on the risk intervention parameter and the correlation between the preset variable and the risk penalty, construct the fourth target formula:

[0069]

[0070] Where D is the above risk penalty, β is the preset coefficient, α is the confidence level, θ is the risk value data, ξ ω is the above preset variable, π ω is the probability of the scenario occurring, and Ω is the total number of the above scenarios.

[0071] In order to construct a power plant operation model, in an optional implementation, the above step S2027 includes:

[0072] Step S20271, combining the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model:

[0073]

[0074] Among them, ψ is the above net profit, π ω is the probability of the scenario occurring, F is the set of representative days, N t is the number of days, and H is the set of hourly time periods.

[0075] In order to construct the constraint group, in an optional implementation, the above step S203 includes:

[0076] Step S2031, construct the investment limit constraint of the virtual power plant corresponding to the power plant operation model to obtain the first target constraint:

[0077]

[0078] in, is the collection of energy storage units to be built, and TC is the total investment amount.

[0079] Step S2032: construct the power balance constraint of the virtual power plant corresponding to the power plant operation model to obtain the second target constraint:

[0080]

[0081] Among them, R is the set of new energy units, S is the set of energy storage units, is the power generation of the new energy unit, are the amount of electricity charged and discharged by the energy storage device, For the power load;

[0082] Step S2033, construct the unit operation constraints of the virtual power plant corresponding to the power plant operation model to obtain the third target constraint:

[0083]

[0084] in, is the power generation status of the unit at the last moment, is the power generation of the unit at the last moment, and are the upper and lower limits of the power generation of non-renewable energy units, and is the up and down ramp power of the above non-new energy units, and It is the start-stop power of the above non-new energy units.

[0085] Step S2034, constructing the demand response constraint of the virtual power plant corresponding to the power plant operation model to obtain a fourth target constraint:

[0086]

[0087] in, and are the upper and lower limits of demand response, Minimum daily demand;

[0088] Specifically, the fourth objective constraint described above describes the boundaries of flexible demand and the daily energy consumption of flexible demand for each time period, representative day and scenario, which can be used to describe the willingness of flexibility demand consumers to adjust their consumption status without compromising their electricity utility.

[0089] Step S2035, combining the first objective constraint, the second objective constraint, the third objective constraint and the fourth objective constraint to obtain the first constraint group;

[0090] Step S2036, constructing the event risk constraints of the virtual power plant corresponding to the power plant operation model to obtain the second constraint group:

[0091]

[0092] Specifically, the second constraint group describes the relationship between each scenario and risk through the variable θ, the profit associated with the scenario, and the non-negative auxiliary variable ξ ω Modeling.

[0093] In the above embodiments, the present application uses a scenario-based stochastic programming framework to simulate the long-term uncertainty of future production costs, demand levels and market prices, as well as short-term changes in market prices and renewable energy generation levels. The proposed stochastic model helps to show that investment decisions are related to improving the expected economic performance of VPPs under uncertainty, and can be widely used in the field of virtual power plant integrated resource planning.

[0094] 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.

[0095] The embodiment of the present application also provides a control device for a virtual power plant based on nonlinear operation and risk. It should be noted that the control device for a virtual power plant based on nonlinear operation and risk in the embodiment of the present application can be used to execute the control method for a virtual power plant based on nonlinear operation and risk provided in 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 explained 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.

[0096] The following introduces the control device of the virtual power plant based on nonlinear operation and risk provided in the embodiment of the present application.

[0097] Figure 3 1 is a structural block diagram of a control device for a virtual power plant based on nonlinear operation and risk according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0098] An acquisition unit 10 is used to acquire historical operation data, wherein the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, wherein the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0099] Specifically, the present application considers regulation from three aspects: power plant control parameters, power plant cost parameters, and risk interference parameters of the virtual power plant operation, at least using electricity market prices, electricity interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs as power plant control parameters, and at least using risk value data under each risk event as risk interference parameters.

[0100] A first construction unit 20 is used to construct a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the change trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters;

[0101] Specifically, a power plant operation model is constructed based on the historical operation data of the power plant. The net profit of the virtual power plant under different power plant control parameters and different risk interference parameters is obtained through the power plant operation model, so as to obtain the changing trend of the net profit of the virtual power plant with the above-mentioned power plant control parameters and the above-mentioned risk interference parameters.

[0102] The second construction unit 30 is used to construct a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameter of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameter of the power plant operation model and the net profit, wherein the first constraint group includes an investment amount constraint, a power balance constraint, a unit operation constraint and a demand response constraint, and the second constraint group includes an event risk constraint;

[0103] Specifically, according to the total investment amount TC, the fourth cost The fourth variable Fifth cost The fifth variable Sixth Cost and the sixth variable Construct investment quota constraints based on the first electricity Second power Power generation of new energy units Energy storage device charge Discharge capacity of energy storage device and power load Construct a power balance constraint based on the first variable Second variable The third variable The power generation status of the unit at the last moment, the second power The fourth variable The power generation of the unit at the last moment, and the upper and lower limits of the power generation of non-new energy units and Up and down ramp power of non-new energy units and Start-stop power of non-new energy units and Construct unit operation constraints based on upper and lower limits of demand response and Minimum daily demand and power load Construct demand response constraints. The above investment limit constraints, power balance constraints, unit operation constraints and demand response constraints constitute the first constraint group. According to income A, operating cost B, construction cost C and number of days N t Construct an event risk constraint group, and the above event risk constraints constitute the second constraint group.

[0104] A calculation unit 40 is used to solve the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as an objective function to obtain power plant operation parameters, and control the operation of the virtual power plant according to the power plant operation parameters;

[0105] Specifically, based on the above-mentioned first constraint group and the above-mentioned second constraint group, simulations are performed to characterize the long-term uncertainties of future production costs, demand levels and market prices, as well as the short-term changes in market prices and renewable energy power generation levels. The above-mentioned maximum net profit is taken as the objective function, and the power plant operating parameters are obtained by solving the above-mentioned power plant operation model and controlling the operation of the virtual power plant.

[0106] Through this embodiment, the acquisition unit acquires historical operation data, the above historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the above power plant control parameters at least include electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the above risk interference parameters at least include risk value data under each risk event; the first construction unit constructs a power plant operation model according to the above power plant control parameters, the above power plant cost parameters and the above risk interference parameters, and the above power plant operation model is used to simulate the net profit of the above virtual power plant with the change trend of the above power plant control parameters and the above risk interference parameters; the second construction unit constructs the first constraint group and the second Constraint group, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints; the calculation unit solves the power plant operation model based on the first constraint group and the second constraint group, taking the maximum net profit as the objective function, obtains the power plant operation parameters, and controls the operation of the virtual power plant according to the power plant operation parameters. This application is based on converting the operating parameters of the virtual power plant into continuous variables and relating them to the virtual power plant revenue. In addition, the operating status, power generation and investment decisions are combined for nonlinear modeling. In addition, risk values ​​under different scenarios and the correlation between scenario-related profits are introduced for nonlinear modeling. Simulation is performed based on a scenario random framework to characterize the long-term uncertainty of production costs, demand levels and market prices, as well as the gas outage uncertainty of market prices and renewable energy generation points, to achieve a decision-making model that integrates long-term and short-term uncertainties and risk interference during emergencies, and to regulate the virtual power plant based on the decision-making model. This method solves the problem in the prior art of the lack of a virtual power plant regulation method that integrates the volatility and uncertainty of virtual power plants.

[0107] In order to construct a power plant operation model, in an optional implementation, the first construction unit includes:

[0108] A first determination subunit is used to determine a target price, a first quantity, a first coefficient, a first variable, a first cost, a second quantity, a second cost, a second variable, a third cost and a third variable according to the power plant control parameters, wherein the target price is the price of the quantity of electricity sold by the virtual power plant, the first quantity of electricity is the quantity of electricity sold by the virtual power plant, the first coefficient is a preset cost coefficient of the non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the first cost is an additional cost of the non-new energy unit that changes with the power generation, the second quantity of electricity is the power generation of the non-new energy unit, the second cost is the startup cost of the non-new energy unit, the second variable is a binary variable of whether the non-new energy unit is started, the third cost is the shutdown cost of the non-new energy unit, and the third variable is a binary variable of whether the non-new energy unit is shut down;

[0109] Specifically, the price of electricity sold by the virtual power plant determined based on the above-mentioned power plant control parameters is used to obtain the above-mentioned target price, the electricity sold by the virtual power plant is used to obtain the above-mentioned first electricity, the preset cost coefficient of the non-new energy unit is used to obtain the above-mentioned first coefficient, the binary variable of whether the non-new energy unit generates electricity is used to obtain the above-mentioned first variable, the additional cost of the non-new energy unit that changes with the power generation is used to obtain the above-mentioned first cost, the power generation of the non-new energy unit is used to obtain the above-mentioned second electricity, the startup cost of the non-new energy unit is used to obtain the above-mentioned second cost, the binary variable of whether the non-new energy unit is started is used to obtain the above-mentioned second variable, the shutdown cost of the non-new energy unit is used to obtain the third cost, and the binary variable of whether the non-new energy unit is shut down is used to obtain the third variable.

[0110] The second determination subunit is used to determine the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable according to the power plant cost parameter, the fourth cost is the construction cost of the non-new energy unit, the fourth variable is a binary variable of whether the non-new energy unit is built, the fifth cost is the construction cost of the new energy unit, the fifth variable is a binary variable of whether the new energy unit is built, the sixth cost is the construction cost of the energy storage device, and the sixth variable is a binary variable of whether the energy storage device is built;

[0111] Specifically, the construction cost of the non-new energy unit determined based on the above cost parameters obtains the above fourth cost, the binary variable of whether the non-new energy unit is built obtains the above fourth variable, the construction cost of the new energy unit obtains the above fifth cost, the binary variable of whether the new energy unit is built obtains the above fifth variable, the construction cost of the energy storage device obtains the above sixth cost, the binary variable of whether the energy storage device is built obtains the above sixth variable.

[0112] A third determination subunit is used to determine risk value data and preset variables according to the above risk intervention parameters;

[0113] Specifically, at least risk value data and preset variables under each risk event are obtained according to the above risk interference parameters, so as to establish an association relationship between the risk interference parameters, the preset variables and the risk penalty.

[0114] A first construction subunit is used to construct a first target formula according to the target price and the first quantity of electricity, wherein the first target formula is used to simulate the change trend of the income of the virtual power plant with the target price and the first quantity of electricity;

[0115] Specifically, based on the price of electricity sold by the virtual power plant determined by the above-mentioned power plant control parameters, i.e., the above-mentioned target price, and the electricity sold by the virtual power plant, i.e., the above-mentioned first electricity, a correlation relationship is established between the income of the above-mentioned virtual power plant and the above-mentioned target price and the above-mentioned first electricity, and then the income of the above-mentioned simulated power plant is determined according to the above-mentioned target price and the above-mentioned first electricity.

[0116] A second construction subunit is used to construct a second target formula according to the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable, and to construct a third target formula according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable. The second target formula is used to simulate the changing trend of the operating cost of the virtual power plant with the first coefficient, the first variable, the first cost, the second electricity, the third cost and the third variable. The third target formula is used to simulate the changing trend of the construction cost of the virtual power plant with the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable.

[0117] Specifically, based on the preset cost coefficient of the non-new energy unit determined by the above-mentioned power plant control parameters, that is, the above-mentioned first coefficient, the binary variable of whether the non-new energy unit generates electricity, that is, the above-mentioned first variable, the additional cost of the non-new energy unit that changes with the power generation, that is, the above-mentioned first cost, the power generation of the non-new energy unit, that is, the above-mentioned second amount of electricity, the startup cost of the non-new energy unit, that is, the above-mentioned second cost, the binary variable of whether the non-new energy unit is started, that is, the above-mentioned second variable, the shutdown cost of the non-new energy unit, that is, the third cost, and the binary variable of whether the non-new energy unit is shut down, that is, the third variable, establish the correlation between the operation and operation costs of the virtual power plant energy units, and then according to the above-mentioned first coefficient, the above-mentioned first variable, the above-mentioned first cost, the above-mentioned second amount of electricity, the above-mentioned third variable The second cost, the second variable, the third cost and the third variable determine the operating cost of the virtual power plant, the construction cost of the non-new energy unit determined based on the above cost parameters, that is, the fourth cost, the binary variable of whether the non-new energy unit is built, that is, the fourth variable, the construction cost of the new energy unit, that is, the fifth cost, the binary variable of whether the new energy unit is built, that is, the fifth variable, the construction cost of the energy storage device, that is, the sixth cost, the binary variable of whether the energy storage device is built, that is, the sixth variable, establish the correlation between the energy units and the construction cost of the virtual power plant, and then determine the construction cost of the virtual power plant according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable.

[0118] A third construction subunit is used to construct a fourth target formula according to the risk interference parameter and the preset variable, wherein the fourth target formula is used to characterize the change trend of the risk penalty with the risk interference parameter and the preset variable;

[0119] Specifically, based on the risk interference parameter and the correlation between the preset variable and the risk penalty, a relationship between the risk penalty and the preset variable is constructed, and the risk penalty is determined according to the preset variable.

[0120] A first combined sub-unit is used to combine the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model;

[0121] Specifically, the power plant operating model is obtained by combining the revenue of the virtual power plant calculated by the first target formula, the operating cost of the virtual power plant calculated by the second target formula, the construction cost of the virtual power plant calculated by the third target formula, and the risk penalty of the virtual power plant calculated by the fourth target formula, and then the net profit of the virtual power plant under different operating parameters is calculated based on the power plant operating model.

[0122] In order to construct the first target formula, in an optional implementation, the first construction subunit includes:

[0123] The first construction module is used to construct the first target formula based on the target price and the correlation between the first quantity and the income:

[0124]

[0125] Among them, A is the above income, For the above target price, is the first electrical quantity mentioned above.

[0126] In order to construct the second target formula, in an optional implementation, the second construction subunit includes:

[0127] The second construction module is used to construct the second target formula based on the first coefficient, the first variable, the first cost, the second power, the second cost, the second variable, the third cost and the relationship between the third variable and the operating cost:

[0128]

[0129] Among them, B is the above operating cost, C is the collection of the above non-new energy units, is the first coefficient mentioned above, is the first variable mentioned above, is the first cost mentioned above, is the second electrical quantity mentioned above, is the second cost mentioned above, is the second variable mentioned above, For the third cost mentioned above, is the third variable mentioned above.

[0130] The third construction module is used to construct the third target formula based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the association between the sixth variable and the construction cost:

[0131]

[0132] Where C is the above construction cost, It is the collection of non-new energy units that are expected to be built. For the fourth cost mentioned above, is the fourth variable mentioned above, For the collection of the above-mentioned new energy units to be built, For the fifth cost mentioned above, is the fifth variable, T is the set of energy storage devices planned to be constructed, For the sixth cost mentioned above, is the sixth variable mentioned above.

[0133] In order to construct the fourth target formula, in an optional implementation, the third construction subunit includes:

[0134] The fourth construction module is used to construct the fourth target formula based on the risk intervention parameter and the correlation between the preset variable and the risk penalty:

[0135]

[0136] Where D is the above risk penalty, β is the preset coefficient, α is the confidence level, θ is the risk value data, ξ ω is the above preset variable, π ω is the probability of the scenario occurring, and Ω is the total number of the above scenarios.

[0137] In order to construct a power plant operation model, in an optional implementation, the first combined sub-unit includes:

[0138] A combined module is used to combine the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model:

[0139]

[0140] Among them, ψ is the above net profit, F is the set of representative days, N t is the number of days, H is the set of hourly time periods;

[0141] In order to construct the constraint group, in an optional implementation, the second construction unit includes:

[0142] The fourth construction subunit is used to construct the investment limit constraint of the virtual power plant corresponding to the power plant operation model to obtain the first target constraint:

[0143]

[0144] in, is the collection of energy storage units to be built, and TC is the total investment amount.

[0145] The fifth construction subunit is used to construct the power balance constraint of the virtual power plant corresponding to the power plant operation model to obtain the second target constraint:

[0146]

[0147] Among them, R is the set of new energy units, S is the set of energy storage units, is the power generation of the new energy unit, are the amount of electricity charged and discharged by the energy storage device, For electrical load.

[0148] The sixth construction subunit is used to construct the unit operation constraints of the virtual power plant corresponding to the power plant operation model to obtain the third target constraint:

[0149]

[0150] in, is the power generation status of the unit at the last moment, is the power generation of the unit at the last moment, and are the upper and lower limits of the power generation of non-renewable energy units, and is the up and down ramp power of the above non-new energy units, and It is the start-stop power of the above non-new energy units.

[0151] The seventh construction subunit is used to construct the demand response constraint of the virtual power plant corresponding to the power plant operation model to obtain the fourth target constraint:

[0152]

[0153] in, and are the upper and lower limits of demand response, Minimum daily demand;

[0154] Specifically, the fourth objective constraint described above describes the boundaries of flexible demand and the daily energy consumption of flexible demand for each time period, representative day and scenario, which can be used to describe the willingness of flexibility demand consumers to adjust their consumption status without compromising their electricity utility.

[0155] The second joint sub-unit jointly combines the first objective constraint, the second objective constraint, the third objective constraint and the fourth objective constraint to obtain the first constraint group.

[0156] The eighth construction subunit constructs the event risk constraints of the virtual power plant corresponding to the power plant operation model to obtain the second constraint group:

[0157]

[0158] Specifically, the second constraint group describes the relationship between each scenario and risk through the variable θ, the profit associated with the scenario, and the non-negative auxiliary variable ξω Modeling.

[0159] The control device of the virtual power plant based on nonlinear operation and risk includes a processor and a memory. The acquisition unit, the first construction unit, the second construction unit and the calculation unit are stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0160] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the efficiency of power plant regulation under volatility and uncertainty can be improved by adjusting kernel parameters.

[0161] 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.

[0162] 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 control method of the virtual power plant based on nonlinear operation and risk.

[0163] Specifically, the control methods of virtual power plants based on nonlinear operation and risk include:

[0164] Step S201, obtaining historical operation data, the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0165] Step S202, constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the net profit of the virtual power plant as the power plant control parameters and the risk interference parameters change;

[0166] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, wherein the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints;

[0167] Step S204, solving the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function to obtain power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters;

[0168] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program, when running, executes the control method of the virtual power plant based on nonlinear operation and risk.

[0169] Specifically, the control methods of virtual power plants based on nonlinear operation and risk include:

[0170] Step S201, obtaining historical operation data, the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0171] Step S202, constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the change trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters;

[0172] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, wherein the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints;

[0173] Step S204, solving the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function to obtain power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters;

[0174] An embodiment of the present invention provides a virtual power plant control system, the control system comprising a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0175] Step S201, obtaining historical operation data, the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0176] Step S202, constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the change trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters;

[0177] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, wherein the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints;

[0178] Step S204, solving the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function to obtain power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters;

[0179] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0180] Step S201, obtaining historical operation data, the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events;

[0181] Step S202, constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the change trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters;

[0182] Step S203, constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, wherein the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints;

[0183] Step S204, solving the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function to obtain power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters;

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0189] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0194] 1) The control method of the virtual power plant based on nonlinear operation and risk of the present application first obtains historical operation data, the above historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the above power plant control parameters at least include electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the above risk interference parameters at least include risk value data under various risk events; then, a power plant operation model is constructed according to the above power plant control parameters, the above power plant cost parameters and the above risk interference parameters, and the above power plant operation model is used to simulate the net profit of the above virtual power plant with the change trend of the above power plant control parameters and the above risk interference parameters; then, a The first constraint group and the second constraint group, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit, the first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints; finally, based on the first constraint group and the second constraint group, the power plant operation model is solved by taking the maximum net profit as the objective function to obtain the power plant operation parameters, and the operation of the virtual power plant is controlled according to the power plant operation parameters. This application is based on converting the operating parameters of the virtual power plant into continuous variables and relating them to the virtual power plant revenue. In addition, the operating status, power generation and investment decisions are combined for nonlinear modeling. In addition, risk values ​​under different scenarios and the correlation between scenario-related profits are introduced for nonlinear modeling. Simulation is performed based on a scenario random framework to characterize the long-term uncertainty of production costs, demand levels and market prices, as well as the gas outage uncertainty of market prices and renewable energy generation points, to achieve a decision-making model that integrates long-term and short-term uncertainties and risk interference during emergencies, and to regulate the virtual power plant based on the decision-making model. This method solves the problem in the prior art of the lack of a virtual power plant regulation method that integrates the volatility and uncertainty of virtual power plants.

[0195] 2) The control device of the virtual power plant based on nonlinear operation and risk of the present application, the acquisition unit acquires historical operation data, the above-mentioned historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, the above-mentioned power plant control parameters at least include electricity market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-up and shutdown costs, and the above-mentioned risk interference parameters at least include risk value data under various risk events; the first construction unit constructs a power plant operation model according to the above-mentioned power plant control parameters, the above-mentioned power plant cost parameters and the above-mentioned risk interference parameters, and the above-mentioned power plant operation model is used to simulate the net profit of the above-mentioned virtual power plant with the change trend of the above-mentioned power plant control parameters and the above-mentioned risk interference parameters; the second construction unit The first constraint group and the second constraint group are constructed, the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameters of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameters of the power plant operation model and the net profit. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints, and the second constraint group includes event risk constraints. The calculation unit solves the power plant operation model based on the first constraint group and the second constraint group, takes the maximum net profit as the objective function, obtains the power plant operation parameters, and controls the operation of the virtual power plant according to the power plant operation parameters. This application is based on converting the operating parameters of the virtual power plant into continuous variables and relating them to the virtual power plant revenue. In addition, the operating status, power generation and investment decisions are combined for nonlinear modeling. In addition, risk values ​​under different scenarios and the correlation between scenario-related profits are introduced for nonlinear modeling. Simulation is performed based on a scenario random framework to characterize the long-term uncertainty of production costs, demand levels and market prices, as well as the gas outage uncertainty of market prices and renewable energy generation points, to achieve a decision-making model that integrates long-term and short-term uncertainties and risk interference during sudden events, and to regulate the virtual power plant based on the decision-making model. This method solves the problem in the prior art of the lack of a virtual power plant regulation method that integrates the volatility and uncertainty of virtual power plants.

[0196] 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 control method for a virtual power plant based on nonlinear operation and risk, characterized in that: include: Acquire historical operation data, wherein the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, wherein the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events; Constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the changing trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters; Constructing a first constraint group and a second constraint group, wherein the first constraint group is used to perform nonlinear restrictions on the correlation between the power plant control parameter of the power plant operation model and the net profit, and the second constraint group is used to perform nonlinear restrictions on the correlation between the risk interference parameter of the power plant operation model and the net profit, the first constraint group includes an investment amount constraint, a power balance constraint, a unit operation constraint, and a demand response constraint, and the second constraint group includes an event risk constraint; Based on the first constraint group and the second constraint group, the power plant operation model is solved by taking the maximum net profit as the objective function to obtain power plant operation parameters, and the operation of the virtual power plant is controlled according to the power plant operation parameters.

2. The method according to claim 1, characterized in that Constructing a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk intervention parameters, including: Determine the target price, the first electricity, the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable according to the power plant control parameters, the target price is the price of the electricity sold by the virtual power plant, the first electricity is the electricity sold by the virtual power plant, the first coefficient is the preset cost coefficient of the non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the first cost is the additional cost of the non-new energy unit that changes with the power generation, the second electricity is the power generation of the non-new energy unit, the second cost is the startup cost of the non-new energy unit, the second variable is a binary variable of whether the non-new energy unit is started, the third cost is the shutdown cost of the non-new energy unit, and the third variable is a binary variable of whether the non-new energy unit is shut down; Determine the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable according to the power plant cost parameter, wherein the fourth cost is the construction cost of the non-new energy unit, the fourth variable is a binary variable indicating whether the non-new energy unit is constructed, the fifth cost is the construction cost of the new energy unit, the fifth variable is a binary variable indicating whether the new energy unit is constructed, the sixth cost is the construction cost of the energy storage device, and the sixth variable is a binary variable indicating whether the energy storage device is constructed; Determine risk value data and preset variables according to the risk intervention parameters; Constructing a first target formula according to the target price and the first quantity of electricity, wherein the first target formula is used to simulate a change trend of the income of the virtual power plant with the target price and the first quantity of electricity; A second target formula is constructed according to the first coefficient, the first variable, the first cost, the second electricity, the second cost, the second variable, the third cost and the third variable; a third target formula is constructed according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable; the second target formula is used to simulate the changing trend of the operating cost of the virtual power plant with the first coefficient, the first variable, the first cost, the second electricity, the third cost and the third variable; the third target formula is used to simulate the changing trend of the construction cost of the virtual power plant with the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable; Constructing a fourth target formula according to the risk intervention parameter and the preset variable, wherein the fourth target formula is used to characterize the change trend of the risk penalty with the risk intervention parameter and the preset variable; The power plant operation model is obtained by combining the first target formula, the second target formula, the third target formula and the fourth target formula.

3. The method according to claim 2, characterized in that Constructing a first target formula according to the target price and the first quantity of electricity includes: Based on the target price and the correlation between the first power and the income, the first target formula is constructed: Where A is the income, is the target price, is the first electrical quantity.

4. The method according to claim 2, characterized in that: A second target formula is constructed according to the first coefficient, the first variable, the first cost, the second power, the second cost, the second variable, the third cost and the third variable, including: The second target formula is constructed based on the first coefficient, the first variable, the first cost, the second power, the second cost, the second variable, the third cost, and the association between the third variable and the operating cost: Wherein, B is the operating cost, C is the set of non-new energy units, is the first coefficient, is the first variable, For the first cost, is the second electrical quantity, For the second cost, is the second variable, For the third cost, is the third variable; Based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost, and the association between the sixth variable and the construction cost, the third target formula is constructed: Wherein, C is the construction cost, It is the collection of non-new energy units that are expected to be built. For the fourth cost, is the fourth variable, The collection of the new energy units planned to be built, For the fifth cost, is the fifth variable, T is the set of energy storage devices expected to be constructed, For the sixth cost, is the sixth variable.

5. The method according to claim 2, characterized in that: Constructing a fourth target formula according to the risk intervention parameter and the preset variable includes: Based on the risk intervention parameter and the association relationship between the preset variable and the risk penalty, the fourth target formula is constructed: Where D is the risk penalty, β is the preset coefficient, α is the confidence level, θ is the risk value data, ξ ω is the preset variable, π ω is the probability of the scenario occurring, and Ω is the total number of the scenarios.

6. The method according to claim 2, characterized in that Combining the first target formula, the second target formula, the third target formula and the fourth target formula to obtain the power plant operation model includes: The first target formula, the second target formula, the third target formula and the fourth target formula are combined to obtain the power plant operation model: Among them, ψ is the net profit, π ω is the probability of the scenario occurring, F is the set of representative days, N t is the number of days, and H is the set of hourly time periods.

7. The method according to claim 1, characterized in that Construct the first constraint group and the second constraint group, including: The investment amount constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain a first target constraint: in, is the set of energy storage units to be built, TC is the total investment amount; The power balance constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain a second objective constraint: Among them, R is the set of new energy units, S is the set of energy storage units, is the power generation of the new energy unit, are the amount of electricity charged and discharged by the energy storage device, For the power load; Construct the unit operation constraints of the virtual power plant corresponding to the power plant operation model to obtain the third objective constraint: in, is the power generation status of the unit at the last moment, is the power generation of the unit at the last moment, and are the upper and lower limits of the power generation of non-renewable energy units, and is the up and down climbing power of the non-new energy unit, and is the start-stop power of the non-new energy unit; The demand response constraint of the virtual power plant corresponding to the power plant operation model is constructed to obtain a fourth target constraint: in, and are the upper and lower limits of demand response, The minimum daily requirement; Combining the first objective constraint, the second objective constraint, the third objective constraint and the fourth objective constraint to obtain the first constraint group; The event risk constraints of the virtual power plant corresponding to the power plant operation model are constructed to obtain the second constraint group:

8. A control device for a virtual power plant based on nonlinear operation and risk, characterized in that: The device comprises: an acquisition unit, configured to acquire historical operation data, wherein the historical operation data at least includes power plant control parameters, power plant cost parameters and risk interference parameters, wherein the power plant control parameters at least include power market prices, electric energy interaction data, operating costs of generator sets, variable power generation costs and start-stop costs, and the risk interference parameters at least include risk value data under various risk events; A first construction unit is used to construct a power plant operation model according to the power plant control parameters, the power plant cost parameters and the risk interference parameters, wherein the power plant operation model is used to simulate the change trend of the net profit of the virtual power plant with the power plant control parameters and the risk interference parameters; a second construction unit, configured to construct a first constraint group and a second constraint group, wherein the first constraint group is configured to perform nonlinear restrictions on the correlation between the power plant control parameter of the power plant operation model and the net profit, and the second constraint group is configured to perform nonlinear restrictions on the correlation between the risk interference parameter of the power plant operation model and the net profit, wherein the first constraint group includes an investment amount constraint, a power balance constraint, a unit operation constraint, and a demand response constraint, and the second constraint group includes an event risk constraint; A calculation unit is used to solve the power plant operation model based on the first constraint group and the second constraint group by taking the maximum net profit as the objective function, obtain the power plant operation parameters, and control the operation of the virtual power plant according to the power plant operation parameters.

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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