Method and device for regulating virtual power plant based on non-linear operation and risk

Abstract

The application provides a virtual power plant regulation method and device based on non-linear operation and risk, the method comprising: obtaining historical operation data, at least including power plant regulation parameters, power plant cost parameters and risk intervention parameters; constructing a power plant operation model according to the parameters, simulating the change trend of the net profit of the virtual power plant with the power plant regulation parameters and the risk intervention parameters; constructing a first constraint group and a second constraint group, the first constraint group being used for non-linearly limiting the correlation between the power plant regulation parameters and the net profit of the power plant operation model, and the second constraint group being used for non-linearly limiting the correlation between the risk intervention parameters and the net profit of the power plant operation model; solving the power plant operation model based on the two constraint groups with the maximum net profit as a target function, obtaining operation parameters, and controlling the virtual power plant operation. The method solves the problem that there is no virtual power plant regulation method that comprehensively considers the volatility and uncertainty of the virtual power plant in the prior art.

Description

Technical Field

[0001] This invention relates to the field of integrated resource planning for virtual power plants, and more specifically, to a control method, control device, computer-readable storage medium, and control system for virtual power plants based on nonlinear operation and risk. Background Technology

[0002] The volatility and uncertainty of renewable energy hinders its participation in the electricity market. This problem can be addressed to some extent by operating renewable generating units in conjunction with other technologies such as conventional generating units, storage facilities, and flexible demand. This aggregation of energy resources gives rise to the concept of a Virtual Power Plant (VPP), which operates as a single entity in a single operating mode. The asset aggregation offered by VPPs paves the way for smarter power systems by achieving economies of scale, thereby improving the efficiency of power plant regulation and minimizing costs. Determining the most advantageous investment options for strategic VPP owners is challenging due to the lack of a direct link between investment decisions and expected profits. This problem stems primarily from the presence of long-term and short-term sources of uncertainty, making the future operation of the expanded VPP highly uncertain, and the non-linearity of VPP component operation, which makes VPP operation extremely complex. To make informed investment decisions, future techno-economic conditions, such as renewable energy generation availability, demand levels, production costs, and market prices, should be accurately described within the decision-making framework, while the operation of VPP components should be accurately modeled. Furthermore, the high volatility of VPP profit distribution necessitates incorporating risk management into the decision-making process. Summary of the Invention

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

[0004] To achieve the above objectives, according to one aspect of this application, a method for regulating a virtual power plant based on nonlinear operation and risk is provided, comprising: acquiring historical operating data, wherein the historical operating data includes at least power plant regulation parameters, power plant cost parameters, and risk intervention parameters, wherein the power plant regulation parameters include at least electricity market prices, electricity interaction data, generator operating costs, variable generation costs, and start-up and shutdown costs, and the risk intervention parameters include at least risk value data under various risk events; constructing a power plant operation model based on the power plant regulation parameters, the power plant cost parameters, and the risk intervention parameters, wherein the power plant operation model is used to simulate the net profit of the virtual power plant as it varies with the power plant regulation parameters and the risk intervention parameters. The changing trend; construct a first constraint group and a second constraint group. The first constraint group is used to nonlinearly restrict the correlation between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to nonlinearly restrict the correlation between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. 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 by maximizing the net profit as the objective function to obtain the power plant operation parameters. The virtual power plant operation is controlled according to the power plant operation parameters.

[0005] Optionally, a power plant operation model is constructed based on the power plant control parameters, the power plant cost parameters, and the risk intervention parameters, including: determining a target price, a first power volume, a first coefficient, a first variable, a first cost, a second power volume, a second cost, a second variable, a third cost, and a third variable based on the power plant control parameters. The target price is the price of the power volume sold by the virtual power plant; the first power volume is the power volume sold by the virtual power plant; the first coefficient is a preset cost coefficient for non-renewable energy units; the first variable is a binary variable indicating whether the non-renewable energy units generate electricity; the first cost is the additional cost of the non-renewable energy units as their power generation changes; and the second power volume is the power generation of the non-renewable energy units. The second cost is the start-up cost of the non-new energy unit, and the second variable is a binary variable indicating 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 indicating whether the non-new energy unit is shut down. Based on the power plant cost parameters, a fourth cost, a fourth variable, a fifth cost, a fifth variable, a sixth cost, and a sixth variable are determined. The fourth cost is the construction cost of the non-new energy unit, and 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, and the fourth 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. The sixth variable is a binary variable indicating whether the energy storage device is constructed; risk value data and preset variables are determined based on the risk interference parameters; a first target formula is constructed based on the target price and the first electricity volume, which is used to simulate the revenue trend of the virtual power plant as the target price and the first electricity volume change; a second target formula is constructed based on the first coefficient, the first variable, the first cost, the second electricity volume, the second cost, the second variable, the third cost, and the third variable; a third target formula is constructed based on 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 operating cost of the virtual power plant is simulated as a function of the first coefficient, the first variable, the first cost, the second electricity consumption, the third cost, and the third variable. The third objective formula is used to simulate the construction cost of the virtual power plant as a function of the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost, and the sixth variable. A fourth objective formula is constructed based on the risk intervention parameter and the preset variable. The fourth objective formula is used to characterize the risk penalty as a function of the risk intervention parameter and the preset variable. The power plant operation model is obtained by combining the first objective formula, the second objective formula, the third objective formula, and the fourth objective formula.

[0006] Optionally, constructing a first target formula based on the target price and the first electricity consumption includes: constructing the first target formula based on the correlation between the target price, the first electricity consumption, and the revenue. Where A represents the revenue. The target price, This is the first electrical charge.

[0007] Optionally, constructing a second target formula based on the first coefficient, the first variable, the first cost, the second electricity consumption, the second cost, the second variable, the third cost, and the third variable includes: constructing the second target formula based on the correlation between the first coefficient, the first variable, the first cost, the second electricity consumption, the second cost, the second variable, the third cost, the third variable, and the operating cost. Wherein, B represents the operating cost, and C represents the set of non-new energy units. For the first coefficient, For the first variable, For the first cost, This is the second amount of electricity. For the second cost, For the second variable, For the third cost, The third variable is defined as follows: Based on the fourth cost, the fourth variable, the fifth cost, the sixth cost, and the relationship between the sixth variable and the construction cost, the third objective formula is constructed as follows: Where C represents the construction cost. This refers to the collection of non-new energy generating units that are planned for construction. For the fourth cost, For the fourth variable, This refers to the collection of the aforementioned new energy generating units that are expected to be constructed. For the fifth cost, Let T be the fifth variable, and T be the set of energy storage devices to be constructed. For the sixth cost, This is the sixth variable.

[0008] Optionally, constructing a fourth objective formula based on the risk intervention parameters and the preset variables includes: constructing the fourth objective formula based on the correlation between the risk intervention parameters, the preset variables, and the risk penalty. Where D is the risk penalty, β is a preset coefficient, α is the confidence level, θ is the value at risk data, and ξ is the risk penalty. ω For the preset variable, π ωLet Ω represent the probability of a scenario occurring, and let Ω represent the total number of such scenarios.

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

[0010] Optionally, constructing a first constraint group and a 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, Let TC be the set of energy storage units to be built, and TC be the total investment amount; construct the power balance constraints of the virtual power plant corresponding to the power plant operation model, and obtain the second objective constraint: Where R represents the set of new energy generating units, and S represents the set of energy storage units. For the power generation of new energy units, These represent 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, and obtain the third objective constraint:

[0011] in, This represents the generator's power generation status at the previous moment. This represents the amount of electricity generated by the generator unit at the previous moment. and These are the upper and lower limits for the power generation of non-new energy generating units. and The uphill and downhill ramp power of the aforementioned non-new energy unit. and Let the start-up and shutdown power of the non-new energy units be defined; construct the demand response constraints of the virtual power plant corresponding to the power plant operation model, and obtain the fourth objective constraint: in, and These are the upper and lower limits of the demand response. To determine the minimum daily demand; by combining the first, second, third, and fourth objective constraints, we obtain the first constraint group; by constructing the event risk constraints for the virtual power plant corresponding to the power plant operation model, we obtain the second constraint group:

[0012] According to another aspect of this application, a control device for a virtual power plant based on nonlinear operation and risk is provided. The device includes: an acquisition unit for acquiring historical operating data, the historical operating data including at least power plant control parameters, power plant cost parameters, and risk intervention parameters; the power plant control parameters including at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up / shutdown costs; and the risk intervention parameters including at least risk value data for each risk event; and a first construction unit for constructing a power plant operation model based on the power plant control parameters, the power plant cost parameters, and the risk intervention parameters, the power plant operation model simulating the net profit of the virtual power plant as a function of the power plant control parameters and the risk intervention parameters. The system includes: a trend; a second construction unit for constructing a first constraint group and a second constraint group, wherein the first constraint group is used to impose nonlinear restrictions on the correlation between the power plant control parameters and the net profit of the power plant operation model, and the second constraint group is used to impose nonlinear restrictions on the correlation between the risk intervention parameters and the net profit of the power plant operation model; 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; and a calculation unit for solving the power plant operation model based on the first constraint group and the second constraint group, using the maximization of net profit as the objective function, to obtain the power plant operation parameters, and controlling the operation of the virtual power plant according to the power plant operation parameters.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this 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 configured to be executed by the one or more processors, the one or more programs including methods for performing a method.

[0015] Applying the technical solution of this application, in the aforementioned control method for a virtual power plant based on nonlinear operation and risk, firstly, historical operating data is acquired. This historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event. Then, a power plant operation model is constructed based on the aforementioned power plant control parameters, power plant cost parameters, and risk intervention parameters. This power plant operation model is used to simulate the trend of the net profit of the virtual power plant as a function of the aforementioned power plant control parameters and risk intervention parameters. Then, a first constraint group and a second constraint group are constructed. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit in the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit in the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints. The second constraint group includes event risk constraints. Finally, based on the first and second constraint groups, the power plant operation model is solved by maximizing the net profit as the objective function to obtain the power plant operation parameters. The operation of the virtual power plant is then controlled according to the power plant operation parameters. This application converts the operating parameters of a virtual power plant into continuous variables and correlates them with the virtual power plant's revenue. Furthermore, it combines operating status, power generation, and investment decisions for nonlinear modeling. Additionally, it introduces risk values ​​under different scenarios and the correlation of scenario-related profits for further nonlinear modeling. Simulation is performed based on a scenario stochastic framework to characterize the long-term uncertainties of production costs, demand levels, and market prices, as well as the uncertainties of market prices and renewable energy generation levels. This results in a decision-making model that integrates long-term and short-term uncertainties with the risk intervention of sudden events. Based on this decision-making model, virtual power plant regulation is implemented. This method addresses the lack of a comprehensive virtual power plant regulation method that addresses the volatility and uncertainty of virtual power plants in existing technologies. Attached Figure Description

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for a control method of a virtual power plant based on nonlinear operation and risk, provided in an embodiment of this application, is shown.

[0017] Figure 2 A schematic flowchart of a control method for a virtual power plant based on nonlinear operation and risk, according to an embodiment of this application, is shown.

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

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

[0020] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort 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, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

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

[0026] As described in the background section, the high volatility of profit distribution in existing virtual power plants requires risk management to be incorporated into the decision-making process. To address the lack of a comprehensive virtual power plant control method that addresses the volatility and uncertainty of virtual power plants, embodiments of this application provide a control method, control device, computer-readable storage medium, processor, and control system for virtual power plants based on nonlinear operation and risk.

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0028] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a control method of a virtual power plant based on nonlinear operation and risk, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station 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] This embodiment provides a method for controlling a virtual power plant based on nonlinear operation and risk, which runs on a mobile terminal, computer terminal or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases the steps shown or described can be executed in a different order than that shown here.

[0031] Figure 2 This is a flowchart of a control method for a virtual power plant based on nonlinear operation and risk, according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0032] Step S201: Obtain historical operating data. The historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, electricity interaction data, generator unit operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event.

[0033] Specifically, this application considers regulation from three aspects: power plant control parameters, power plant cost parameters, and risk intervention parameters for the operation of virtual power plants. At least the electricity market price, power interaction data, generator operating costs, variable generation costs, and start-up and shutdown costs are used as power plant control parameters, and at least the risk value data under each risk event is used as risk intervention parameters.

[0034] Step S202: Construct a power plant operation model based on the above power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant operation model is used to simulate the net profit of the virtual power plant as the above power plant control parameters and risk intervention parameters change.

[0035] Specifically, a power plant operation model is constructed based on the power plant's historical operating data. The net profit of the virtual power plant under different power plant control parameters and different risk intervention parameters is obtained through the power plant operation model. In this way, the trend of the virtual power plant's net profit with the changes of the aforementioned power plant control parameters and risk intervention parameters is obtained.

[0036] Step S203: Construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0037] Specifically, based on the total investment amount TC and the fourth cost Fourth variable Fifth Cost Fifth variable Sixth cost and the sixth variable Establish investment amount constraints based on the first electricity consumption. Second battery Power generation of new energy units Energy storage device charging capacity Discharge of energy storage device and power load Construct power balance constraints based on the first variable. Second variable Third variable The generating status of the unit at the previous moment, and the second power quantity. Fourth variable The power generation of the generating unit at the previous moment, and the upper and lower limits of the power generation of non-new energy generating units. and Uphill and downhill power of non-new energy units and Start-stop power of non-new energy units and Establish unit operation constraints based on the upper and lower limits of demand response. and Minimum daily demand and power load Demand response constraints are constructed. The above-mentioned investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints constitute the first constraint group, based on revenue A, operating costs B, construction costs C, and the number of days N. t Construct an event risk constraint group, and the above event risk constraints form a second constraint group.

[0038] Step S204: Based on the first constraint group and the second constraint group, the above-mentioned net profit maximization is used as the objective function to solve the above-mentioned power plant operation model to obtain the power plant operation parameters, and the operation of the above-mentioned virtual power plant is controlled according to the above-mentioned power plant operation parameters;

[0039] Specifically, based on the above first constraint group and the above second constraint group, the simulation characterizes the long-term uncertainty 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. Taking the maximization of net profit as the objective function, the power plant operation parameters are obtained by solving the above power plant operation model and the operation of the virtual power plant is controlled.

[0040] In this embodiment, firstly, historical operating data is acquired. This historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event. Then, a power plant operation model is constructed based on the power plant control parameters, power plant cost parameters, and risk intervention parameters. This power plant operation model is used to simulate the net profit trend of the virtual power plant as the power plant control parameters and risk intervention parameters change. Afterward, a first constraint group and a second constraint group are constructed. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit in the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit in the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints. The second constraint group includes event risk constraints. Finally, based on the first and second constraint groups, the power plant operation model is solved by maximizing the net profit as the objective function to obtain the power plant operation parameters. The operation of the virtual power plant is then controlled according to the power plant operation parameters. This application converts the operating parameters of a virtual power plant into continuous variables and correlates them with the virtual power plant's revenue. Furthermore, it combines operating status, power generation, and investment decisions for nonlinear modeling. Additionally, it introduces risk values ​​under different scenarios and the correlation of scenario-related profits for further nonlinear modeling. Simulation is performed based on a scenario stochastic framework to characterize the long-term uncertainties of production costs, demand levels, and market prices, as well as the uncertainties of market prices and renewable energy generation levels. This results in a decision-making model that integrates long-term and short-term uncertainties with the risk intervention of sudden events. Based on this decision-making model, virtual power plant regulation is implemented. This method addresses the lack of a comprehensive virtual power plant regulation method that addresses the volatility and uncertainty of virtual power plants in existing technologies.

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

[0042] Step S2021: Determine the target price, first power volume, first coefficient, first variable, first cost, second power volume, second cost, second variable, third cost, and third variable based on the above power plant control parameters. The target price is the price of the power volume sold by the virtual power plant. The first power volume is the power volume 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 indicating whether the non-new energy unit generates electricity. The first cost is the additional cost of the non-new energy unit as the power generation changes. The second power volume is the power generation of the non-new energy unit. The second cost is the start-up cost of the non-new energy unit. The second variable is a binary variable indicating whether the non-new energy unit is started. The third cost is the shutdown cost of the non-new energy unit. The third variable is a binary variable indicating whether the non-new energy unit is shut down.

[0043] Specifically, based on the price of the virtual power plant's sold electricity determined by the aforementioned power plant control parameters, the target price is obtained; the electricity sold by the virtual power plant is obtained as the first electricity volume; the preset cost coefficient of the non-new energy unit is obtained as the first coefficient; the binary variable of whether the non-new energy unit generates electricity is obtained as the first variable; the additional cost of the non-new energy unit as its electricity generation changes is obtained as the first cost; the electricity generation of the non-new energy unit is obtained as the second electricity volume; the start-up cost of the non-new energy unit is obtained as the second cost; the binary variable of whether the non-new energy unit starts is obtained as the second variable; the shutdown cost of the non-new energy unit is obtained as the third cost; and the binary variable of whether the non-new energy unit stops is obtained as the third variable.

[0044] Step S2022: Determine the fourth cost, fourth variable, fifth cost, fifth variable, sixth cost, and sixth variable based on the above power plant cost parameters. The fourth cost is the construction cost of the non-new energy unit, and 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, and 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.

[0045] Specifically, based on the construction cost of the non-new energy unit determined by the above cost parameters, the fourth cost is obtained; the binary variable of whether or not the non-new energy unit is constructed is obtained; the construction cost of the new energy unit is obtained; the binary variable of whether or not the new energy unit is constructed is obtained; the construction cost of the energy storage device is obtained; and the binary variable of whether or not the energy storage device is constructed is obtained.

[0046] Step S2023: Determine the value-at-risk data and preset variables based on the aforementioned risk intervention parameters;

[0047] Specifically, based on the aforementioned risk intervention parameters, at least the risk value data and preset variables for each risk event are obtained, which are used to establish the correlation between the aforementioned risk intervention parameters and preset variables and the aforementioned risk penalties based on the CVaR model (Conditional Value at Risk model).

[0048] Step S2024: Construct a first target formula based on the target price and the first electricity volume. The first target formula is used to simulate the revenue trend of the virtual power plant as the target price and the first electricity volume change.

[0049] Specifically, based on the price of the virtual power plant's sold electricity determined by the aforementioned power plant control parameters, i.e., the aforementioned target price, and the virtual power plant's sold electricity, i.e., the aforementioned first electricity volume, a correlation is established between the virtual power plant's revenue, the aforementioned target price, and the aforementioned first electricity volume. Then, the revenue of the simulated power plant is determined based on the aforementioned target price and the aforementioned first electricity volume.

[0050] Step S2025: Construct a second target formula based on the first coefficient, the first variable, the first cost, the second electricity consumption, the second cost, the second variable, the third cost, and the third variable; construct a third target formula based on 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 consumption, 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.

[0051] Specifically, based on the preset cost coefficient of non-new energy units determined by the aforementioned power plant control parameters (i.e., the first coefficient), the binary variable of whether non-new energy units generate electricity (i.e., the first variable), the additional cost of non-new energy units as their power generation changes (i.e., the first cost), the power generation of non-new energy units (i.e., the second power generation), the start-up cost of non-new energy units (i.e., the second cost), the binary variable of whether non-new energy units start up (i.e., the second variable), the shutdown cost of non-new energy units (i.e., the third cost), and the binary variable of whether non-new energy units shut down (i.e., the third variable), a correlation is established between the operation and operating costs of virtual power plant energy units. Then, based on the aforementioned first coefficient, the aforementioned first variable, the aforementioned first cost, the aforementioned second power generation, and the aforementioned third cost, a correlation is established between the operation and operating costs of virtual power plant energy units. The operating cost of the virtual power plant is determined by the second cost, the second variable, the third cost, and the third variable mentioned above. The construction cost of the non-new energy unit, determined based on the cost parameters mentioned above, is the fourth cost mentioned above. The binary variable indicating whether the non-new energy unit is constructed is the fourth variable mentioned above. The construction cost of the new energy unit is the fifth cost mentioned above. The construction cost of the energy storage device is the sixth cost mentioned above. The relationship between the construction of energy units and the construction cost of the virtual power plant is established. Then, the construction cost of the virtual power plant is determined based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost, and the sixth variable mentioned above.

[0052] Step S2026: Construct a fourth objective formula based on the above-mentioned risk intervention parameters and the above-mentioned preset variables. The fourth objective formula is used to characterize the trend of risk penalty as a function of the above-mentioned risk intervention parameters and the above-mentioned preset variables.

[0053] Specifically, based on the correlation between risk intervention parameters and preset variables and risk penalties, the relationship between the aforementioned risk penalties and the aforementioned preset variables is constructed, and the aforementioned risk penalties are determined according to the aforementioned preset variables.

[0054] Step S2027: Combine the above-mentioned first objective formula, the above-mentioned second objective formula, the above-mentioned third objective formula and the above-mentioned fourth objective formula to obtain the above-mentioned power plant operation model;

[0055] Specifically, the virtual power plant revenue calculated using the first objective formula, the virtual power plant operating cost calculated using the second objective formula, the virtual power plant construction cost calculated using the third objective formula, and the virtual power plant risk penalty calculated using the fourth objective formula are combined to obtain the power plant operation model. Then, the net profit of the virtual power plant under different operating parameters is calculated based on the power plant operation model.

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

[0057] Step S20241, based on the above target price and the relationship between the first electricity volume and the above revenue, construct the above first target formula:

[0058]

[0059] Where A represents the aforementioned income, The target price is as described above. This refers to the first electrical charge mentioned above.

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

[0061] Step S20251: Based on the aforementioned first coefficient, first variable, first cost, second electricity consumption, second cost, second variable, third cost, and the relationship between the third variable and operating cost, construct the aforementioned second objective formula:

[0062]

[0063] Where B represents the aforementioned operating costs, and C represents the collection of the aforementioned non-new energy generating units. The first coefficient mentioned above, For the first variable mentioned above, For the aforementioned first cost, This refers to the second electrical quantity mentioned above. For the aforementioned second cost, For the second variable mentioned above, For the aforementioned third cost, The third variable mentioned above;

[0064] Step S20252, based on the above-mentioned fourth cost, fourth variable, fifth cost, fifth variable, sixth cost, and the relationship between the sixth variable and the construction cost, construct the above-mentioned third objective formula:

[0065]

[0066] Where C represents the aforementioned construction cost. This refers to the collection of the aforementioned non-new energy generating units that are expected to be constructed. For the aforementioned fourth cost, For the fourth variable mentioned above, This refers to the collection of the aforementioned new energy generating units that are expected to be constructed. For the fifth cost mentioned above, The fifth variable mentioned above, where T represents the set of the aforementioned energy storage devices that are expected to be constructed. For the sixth cost mentioned above, This is the sixth variable mentioned above.

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

[0068] Step S20261: Based on the above-mentioned risk intervention parameters and the correlation between the above-mentioned preset variables and the above-mentioned risk penalties, construct the above-mentioned fourth objective formula:

[0069]

[0070] Where D represents the aforementioned risk penalty, β is a preset coefficient, α is the confidence level, θ is the value at risk data, and ξ... ω For the above-mentioned preset variables, π ω Let Ω represent the probability of the scenario occurring, and let Ω represent the total number of the aforementioned scenarios.

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

[0072] Step S20271: By simultaneously applying the above-mentioned first objective formula, second objective formula, third objective formula, and fourth objective formula, the above-mentioned power plant operation model is obtained:

[0073]

[0074] Where ψ represents the aforementioned net profit, and π ω Let F be the probability of the scenario occurring, F be the set of representative days, and N be the number of days. t Let H be the number of days, and H be the set of hourly time periods.

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

[0076] Step S2031: Construct the investment amount constraint of the virtual power plant corresponding to the above power plant operation model, and obtain the first objective constraint:

[0077]

[0078] in, This refers to the collection of energy storage units to be built, with TC representing the total investment amount.

[0079] Step S2032: Construct the power balance constraints of the virtual power plant corresponding to the above power plant operation model to obtain the second objective constraint:

[0080]

[0081] Where R represents the set of new energy generating units, and S represents the set of energy storage units. For the power generation of new energy units, These represent the amount of electricity charged and discharged by the energy storage device. For electrical load;

[0082] Step S2033: Construct the unit operation constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the third objective constraint:

[0083]

[0084] in, This represents the generator's power generation status at the previous moment. This represents the amount of electricity generated by the generator unit at the previous moment. and These are the upper and lower limits for the power generation of non-new energy generating units. and This refers to the uphill and downhill ramp power of the aforementioned non-new energy generating units. and This refers to the start-stop power of the aforementioned non-new energy generating units.

[0085] Step S2034: Construct the demand response constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the fourth objective constraint:

[0086]

[0087] in, and These are the upper and lower limits of the demand response. This is the minimum daily demand;

[0088] Specifically, the fourth objective constraint outlines the limits of flexible demand for each time period, representative day, and scenario, as well as the daily energy consumption of flexible demand. This can be used to describe the willingness of flexible demand consumers to adjust their consumption patterns without compromising their electricity utility.

[0089] Step S2035: Combine the above-mentioned first objective constraint, the above-mentioned second objective constraint, the above-mentioned third objective constraint and the above-mentioned fourth objective constraint to obtain the above-mentioned first constraint group;

[0090] Step S2036: Construct the event risk constraints for the virtual power plant corresponding to the above power plant operation model, and obtain the second set of constraints:

[0091]

[0092] Specifically, the second set of constraints described above defines the relationship between each scenario and risk through the variable θ, the profit associated with that scenario, and the non-negative auxiliary variable ξ. ω Modeling is performed.

[0093] In the above embodiments, this application employs a scenario-based stochastic programming framework to simulate and 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 generation levels. The proposed stochastic model helps demonstrate the correlation between investment decisions and the expected economic performance of a Virtual Power Plant (VPP) under increased uncertainty, and can be widely applied in the field of integrated resource planning for virtual power plants.

[0094] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0095] This application also provides a control device for a virtual power plant based on nonlinear operation and risk. It should be noted that this control device for a virtual power plant based on nonlinear operation and risk can be used to execute the control method for a virtual power plant based on nonlinear operation and risk provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0096] The following describes the control device for a virtual power plant based on nonlinear operation and risk, as provided in the embodiments of this application.

[0097] Figure 3 This 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 this application. Figure 3 As shown, the device includes:

[0098] The acquisition unit 10 is used to acquire historical operating data, which includes at least power plant control parameters, power plant cost parameters and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs and start-up and shutdown costs. The risk intervention parameters include at least risk value data under each risk event.

[0099] Specifically, this application considers regulation from three aspects: power plant control parameters, power plant cost parameters, and risk intervention parameters for the operation of virtual power plants. At least the electricity market price, power interaction data, generator operating costs, variable generation costs, and start-up and shutdown costs are used as power plant control parameters, and at least the risk value data under each risk event is used as risk intervention parameters.

[0100] The first construction unit 20 is used to construct a power plant operation model based on the power plant control parameters, the power plant cost parameters and the risk intervention parameters. The power plant operation model is used to simulate the trend of the net profit of the virtual power plant as the power plant control parameters and the risk intervention parameters change.

[0101] Specifically, a power plant operation model is constructed based on the power plant's historical operating data. The net profit of the virtual power plant under different power plant control parameters and different risk intervention parameters is obtained through the power plant operation model. In this way, the trend of the virtual power plant's net profit with the changes of the aforementioned power plant control parameters and risk intervention parameters is obtained.

[0102] The second construction unit 30 is used to construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0103] Specifically, based on the total investment amount TC and the fourth cost Fourth variable Fifth Cost Fifth variable Sixth cost and the sixth variable Establish investment amount constraints based on the first electricity consumption. Second battery Power generation of new energy units Energy storage device charging capacity Discharge of energy storage device and power load Construct power balance constraints based on the first variable. Second variable Third variable The generating status of the unit at the previous moment, and the second power quantity. Fourth variable The power generation of the generating unit at the previous moment, and the upper and lower limits of the power generation of non-new energy generating units. and Uphill and downhill power of non-new energy units and Start-stop power of non-new energy units and Establish unit operation constraints based on the upper and lower limits of demand response. and Minimum daily demand and power load Demand response constraints are constructed. The above-mentioned investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints constitute the first constraint group, based on revenue A, operating costs B, construction costs C, and the number of days N. t Construct an event risk constraint group, and the above event risk constraints form a second constraint group.

[0104] The calculation unit 40 is used to solve 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, to obtain the power plant operation parameters, and to control the operation of the virtual power plant according to the power plant operation parameters.

[0105] Specifically, based on the above first constraint group and the above second constraint group, the simulation characterizes the long-term uncertainty 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. Taking the maximization of net profit as the objective function, the power plant operation parameters are obtained by solving the above power plant operation model and the operation of the virtual power plant is controlled.

[0106] In this embodiment, the acquisition unit acquires historical operating data, which includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up / shutdown costs. The risk intervention parameters include at least risk value data for each risk event. The first construction unit constructs a power plant operation model based on the power plant control parameters, power plant cost parameters, and risk intervention parameters. This power plant operation model simulates the net profit trend of the virtual power plant as a function of the power plant control parameters and risk intervention parameters. The second construction unit constructs a first constraint group and a second constraint group. The constraint group consists of two sets: a first constraint group, which imposes nonlinear constraints on the relationship between the power plant control parameters and net profit in the power plant operation model; and a second constraint group, which imposes nonlinear constraints on the relationship between the risk intervention parameters and net profit in the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints. The second constraint group includes event risk constraints. The calculation unit solves the power plant operation model based on the first and second constraint groups, using the maximization of net profit as the objective function, to obtain the power plant operation parameters. The virtual power plant is then controlled according to these power plant operation parameters. This application converts the operating parameters of a virtual power plant into continuous variables and correlates them with the virtual power plant's revenue. Furthermore, it combines operating status, power generation, and investment decisions for nonlinear modeling. Additionally, it introduces risk values ​​under different scenarios and the correlation of scenario-related profits for further nonlinear modeling. Simulation is performed based on a scenario stochastic framework to characterize the long-term uncertainties of production costs, demand levels, and market prices, as well as the uncertainties of market prices and renewable energy generation levels. This results in a decision-making model that integrates long-term and short-term uncertainties with the risk intervention of sudden events. Based on this decision-making model, virtual power plant regulation is implemented. This method addresses the lack of a comprehensive virtual power plant regulation method that addresses the volatility and uncertainty of virtual power plants in existing technologies.

[0107] To construct a power plant operation model, in one optional implementation, the first construction unit mentioned above includes:

[0108] The first determining subunit is used to determine the target price, first power volume, first coefficient, first variable, first cost, second power volume, second cost, second variable, third cost, and third variable based on the power plant control parameters mentioned above. The target price is the price of the power volume sold by the virtual power plant. The first power volume is the power volume 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 indicating whether the non-new energy unit generates electricity. The first cost is the additional cost of the non-new energy unit as the power generation changes. The second power volume is the power generation of the non-new energy unit. The second cost is the start-up cost of the non-new energy unit. The second variable is a binary variable indicating whether the non-new energy unit is started. The third cost is the shutdown cost of the non-new energy unit. The third variable is a binary variable indicating whether the non-new energy unit is shut down.

[0109] Specifically, based on the price of the virtual power plant's sold electricity determined by the aforementioned power plant control parameters, the target price is obtained; the electricity sold by the virtual power plant is obtained as the first electricity volume; the preset cost coefficient of the non-new energy unit is obtained as the first coefficient; the binary variable of whether the non-new energy unit generates electricity is obtained as the first variable; the additional cost of the non-new energy unit as its electricity generation changes is obtained as the first cost; the electricity generation of the non-new energy unit is obtained as the second electricity volume; the start-up cost of the non-new energy unit is obtained as the second cost; the binary variable of whether the non-new energy unit starts is obtained as the second variable; the shutdown cost of the non-new energy unit is obtained as the third cost; and the binary variable of whether the non-new energy unit stops is obtained as the third variable.

[0110] The second determining subunit is used to determine the fourth cost, fourth variable, fifth cost, fifth variable, sixth cost, and sixth variable based on the above power plant cost parameters. The fourth cost is the construction cost of the above non-new energy unit, the fourth variable is a binary variable indicating whether the above 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 above new energy unit is constructed, and the sixth cost is the construction cost of the energy storage device, the sixth variable is a binary variable indicating whether the above energy storage device is constructed.

[0111] Specifically, based on the construction cost of the non-new energy unit determined by the above cost parameters, the fourth cost is obtained; the binary variable of whether or not the non-new energy unit is constructed is obtained; the construction cost of the new energy unit is obtained; the binary variable of whether or not the new energy unit is constructed is obtained; the construction cost of the energy storage device is obtained; and the binary variable of whether or not the energy storage device is constructed is obtained.

[0112] The third determining subunit is used to determine risk value data and preset variables based on the aforementioned risk intervention parameters;

[0113] Specifically, based on the aforementioned risk intervention parameters, at least the risk value data and preset variables for each risk event are obtained, which are used to establish the correlation between the risk intervention parameters and preset variables and the risk penalty.

[0114] The first construction subunit is used to construct a first target formula based on the target price and the first electricity volume, and the first target formula is used to simulate the revenue trend of the virtual power plant as the target price and the first electricity volume change.

[0115] Specifically, based on the price of the virtual power plant's sold electricity determined by the aforementioned power plant control parameters, i.e., the aforementioned target price, and the virtual power plant's sold electricity, i.e., the aforementioned first electricity volume, a correlation is established between the virtual power plant's revenue, the aforementioned target price, and the aforementioned first electricity volume. Then, the revenue of the simulated power plant is determined based on the aforementioned target price and the aforementioned first electricity volume.

[0116] The second construction subunit is used to construct a second target formula based on the first coefficient, the first variable, the first cost, the second electricity consumption, the second cost, the second variable, the third cost, and the third variable; and to construct a third target formula based on 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 consumption, 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.

[0117] Specifically, based on the preset cost coefficient of non-new energy units determined by the aforementioned power plant control parameters (i.e., the first coefficient), the binary variable of whether non-new energy units generate electricity (i.e., the first variable), the additional cost of non-new energy units as their power generation changes (i.e., the first cost), the power generation of non-new energy units (i.e., the second power generation), the start-up cost of non-new energy units (i.e., the second cost), the binary variable of whether non-new energy units start up (i.e., the second variable), the shutdown cost of non-new energy units (i.e., the third cost), and the binary variable of whether non-new energy units shut down (i.e., the third variable), a correlation is established between the operation and operating costs of virtual power plant energy units. Then, based on the aforementioned first coefficient, the aforementioned first variable, the aforementioned first cost, the aforementioned second power generation, and the aforementioned third cost, a correlation is established between the operation and operating costs of virtual power plant energy units. The operating cost of the virtual power plant is determined by the second cost, the second variable, the third cost, and the third variable mentioned above. The construction cost of the non-new energy unit, determined based on the cost parameters mentioned above, is the fourth cost mentioned above. The binary variable indicating whether the non-new energy unit is constructed is the fourth variable mentioned above. The construction cost of the new energy unit is the fifth cost mentioned above. The construction cost of the energy storage device is the sixth cost mentioned above. The relationship between the construction of energy units and the construction cost of the virtual power plant is established. Then, the construction cost of the virtual power plant is determined based on the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost, and the sixth variable mentioned above.

[0118] The third construction subunit is used to construct a fourth objective formula based on the above-mentioned risk intervention parameters and the above-mentioned preset variables. The fourth objective formula is used to characterize the trend of risk penalty as a function of the above-mentioned risk intervention parameters and the above-mentioned preset variables.

[0119] Specifically, based on the relationship between the aforementioned risk intervention parameters and the aforementioned preset variables and the aforementioned risk penalties, a relationship is constructed between the aforementioned risk penalties and the aforementioned preset variables, and the aforementioned risk penalties are determined according to the aforementioned preset variables.

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

[0121] Specifically, the virtual power plant revenue calculated using the first objective formula, the virtual power plant operating cost calculated using the second objective formula, the virtual power plant construction cost calculated using the third objective formula, and the virtual power plant risk penalty calculated using the fourth objective formula are combined to obtain the power plant operation model. Then, the net profit of the virtual power plant under different operating parameters is calculated based on the power plant operation model.

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

[0123] The first construction module is used to construct the first target formula based on the aforementioned target price and the relationship between the aforementioned first electricity volume and the aforementioned revenue:

[0124]

[0125] Where A represents the aforementioned income, The target price is as described above. This refers to the first electrical charge mentioned above.

[0126] In order to construct the second objective formula, in one optional implementation, the second constructing subunit includes:

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

[0128]

[0129] Where B represents the aforementioned operating costs, and C represents the collection of the aforementioned non-new energy generating units. The first coefficient mentioned above, For the first variable mentioned above, For the aforementioned first cost, This refers to the second electrical quantity mentioned above. For the aforementioned second cost, For the second variable mentioned above, For the aforementioned third cost, This is the third variable mentioned above.

[0130] The third construction module is used to construct the third objective formula based on the aforementioned fourth cost, fourth variable, fifth cost, fifth variable, sixth cost, and the relationship between the sixth variable and the construction cost.

[0131]

[0132] Where C represents the aforementioned construction cost. This refers to the collection of non-new energy generating units that are planned for construction. For the aforementioned fourth cost, For the fourth variable mentioned above, This refers to the collection of the aforementioned new energy generating units that are expected to be constructed. For the fifth cost mentioned above, The fifth variable mentioned above, where T represents the set of the aforementioned energy storage devices that are expected to be constructed. For the sixth cost mentioned above, This is the sixth variable mentioned above.

[0133] In order to construct the fourth objective formula, in one optional implementation, the third constructing subunit mentioned above includes:

[0134] The fourth construction module is used to construct the fourth objective formula based on the correlation between the aforementioned risk intervention parameters, the aforementioned preset variables, and the aforementioned risk penalties:

[0135]

[0136] Where D represents the aforementioned risk penalty, β is a preset coefficient, α is the confidence level, θ is the value at risk data, and ξ... ω For the above-mentioned preset variables, π ω Let Ω represent the probability of the scenario occurring, and let Ω represent the total number of the aforementioned scenarios.

[0137] To construct a power plant operation model, in one optional implementation, the aforementioned first combined sub-unit includes:

[0138] The simultaneous equation module is used to simultaneously establish the first objective equation, the second objective equation, the third objective equation, and the fourth objective equation to obtain the power plant operation model described above.

[0139]

[0140] Where ψ represents the aforementioned net profit, F is the set of representative days, and N... t Let H be the number of days, and H be the set of hourly time periods;

[0141] In one alternative implementation, to construct the constraint group, the second construction unit includes:

[0142] The fourth construction subunit is used to construct the investment amount constraint of the virtual power plant corresponding to the above power plant operation model, thus obtaining the first objective constraint:

[0143]

[0144] in, This refers to the collection of energy storage units to be built, with TC representing the total investment amount.

[0145] The fifth construction subunit is used to construct the power balance constraints of the virtual power plant corresponding to the above power plant operation model, thereby obtaining the second objective constraint:

[0146]

[0147] Where R represents the set of new energy generating units, and S represents the set of energy storage units. For the power generation of new energy units, These represent 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 above power plant operation model, thus obtaining the third objective constraint:

[0149]

[0150] in, This represents the generator's power generation status at the previous moment. This represents the amount of electricity generated by the generator unit at the previous moment. and These are the upper and lower limits for the power generation of non-new energy generating units. and This refers to the uphill and downhill ramp power of the aforementioned non-new energy generating units. and This refers to the start-stop power of the aforementioned non-new energy generating units.

[0151] The seventh construction subunit is used to construct the aforementioned demand response constraints of the virtual power plant corresponding to the aforementioned power plant operation model, resulting in the fourth objective constraint:

[0152]

[0153] in, and These are the upper and lower limits of the demand response. This is the minimum daily demand;

[0154] Specifically, the fourth objective constraint outlines the limits of flexible demand for each time period, representative day, and scenario, as well as the daily energy consumption of flexible demand. This can be used to describe the willingness of flexible demand consumers to adjust their consumption patterns without compromising their electricity utility.

[0155] The second combined subunit combines the aforementioned first objective constraint, the aforementioned second objective constraint, the aforementioned third objective constraint, and the aforementioned fourth objective constraint to obtain the aforementioned first constraint group.

[0156] The eighth construction subunit constructs the aforementioned event risk constraints for the virtual power plant corresponding to the aforementioned power plant operation model, resulting in the aforementioned second set of constraints:

[0157]

[0158] Specifically, the second set of constraints described above defines the relationship between each scenario and risk through the variable θ, the profit associated with that scenario, and the non-negative auxiliary variable ξ.ω Modeling is performed.

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

[0160] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, 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 computer-readable media, such as 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] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the aforementioned control method for a virtual power plant based on nonlinear operation and risk.

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

[0164] Step S201: Obtain historical operating data. The historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, electricity interaction data, generator unit operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event.

[0165] Step S202: Construct a power plant operation model based on the above power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant operation model is used to simulate the net profit of the virtual power plant as the above power plant control parameters and risk intervention parameters change.

[0166] Step S203: Construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0167] Step S204: Based on the first constraint group and the second constraint group, the above-mentioned net profit maximization is used as the objective function to solve the above-mentioned power plant operation model to obtain the power plant operation parameters, and the operation of the above-mentioned virtual power plant is controlled according to the above-mentioned power plant operation parameters;

[0168] This invention provides a processor for running a program, wherein the program executes the aforementioned control method for a virtual power plant based on nonlinear operation and risk.

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

[0170] Step S201: Obtain historical operating data. The historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, electricity interaction data, generator unit operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event.

[0171] Step S202: Construct a power plant operation model based on the above power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant operation model is used to simulate the net profit of the virtual power plant as the above power plant control parameters and risk intervention parameters change.

[0172] Step S203: Construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0173] Step S204: Based on the first constraint group and the second constraint group, the above-mentioned net profit maximization is used as the objective function to solve the above-mentioned power plant operation model to obtain the power plant operation parameters, and the operation of the above-mentioned virtual power plant is controlled according to the above-mentioned power plant operation parameters;

[0174] This invention provides a virtual power plant control system. The control system includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0175] Step S201: Obtain historical operating data. The historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, electricity interaction data, generator unit operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event.

[0176] Step S202: Construct a power plant operation model based on the above power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant operation model is used to simulate the net profit of the virtual power plant as the above power plant control parameters and risk intervention parameters change.

[0177] Step S203: Construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0178] Step S204: Based on the first constraint group and the second constraint group, the above-mentioned net profit maximization is used as the objective function to solve the above-mentioned power plant operation model to obtain the power plant operation parameters, and the operation of the above-mentioned virtual power plant is controlled according to the above-mentioned power plant operation parameters;

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

[0180] Step S201: Obtain historical operating data. The historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, electricity interaction data, generator unit operating costs, variable generation costs, and start-up and shutdown costs. The risk intervention parameters include at least risk value data for each risk event.

[0181] Step S202: Construct a power plant operation model based on the above power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant operation model is used to simulate the net profit of the virtual power plant as the above power plant control parameters and risk intervention parameters change.

[0182] Step S203: Construct a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit of the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit of the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints and demand response constraints. The second constraint group includes event risk constraints.

[0183] Step S204: Based on the first constraint group and the second constraint group, the above-mentioned net profit maximization is used as the objective function to solve the above-mentioned power plant operation model to obtain the power plant operation parameters, and the operation of the above-mentioned virtual power plant is controlled according to the above-mentioned power plant operation parameters;

[0184] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0185] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0187] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0188] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function 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] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0191] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0192] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0193] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0194] 1) The control method for a virtual power plant based on nonlinear operation and risk in this application firstly acquires historical operating data, which includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up / shutdown costs. The risk intervention parameters include at least risk value data for each risk event. Then, a power plant operation model is constructed based on the power plant control parameters, power plant cost parameters, and risk intervention parameters. This power plant operation model is used to simulate the net profit trend of the virtual power plant as a function of the power plant control parameters and the risk intervention parameters. Afterwards, a... The first constraint group and the second constraint group are used to impose nonlinear constraints on the relationship between the power plant control parameters and the net profit in the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints. The second constraint group includes event risk constraints. Finally, based on the first and second constraint groups, the power plant operation model is solved by maximizing the net profit as the objective function to obtain the power plant operation parameters. The operation of the virtual power plant is then controlled according to the power plant operation parameters. This application converts the operating parameters of a virtual power plant into continuous variables and correlates them with the virtual power plant's revenue. Furthermore, it combines operating status, power generation, and investment decisions for nonlinear modeling. Additionally, it introduces risk values ​​under different scenarios and the correlation of scenario-related profits for further nonlinear modeling. Simulation is performed based on a scenario stochastic framework to characterize the long-term uncertainties of production costs, demand levels, and market prices, as well as the uncertainties of market prices and renewable energy generation levels. This results in a decision-making model that integrates long-term and short-term uncertainties with the risk intervention of sudden events. Based on this decision-making model, virtual power plant regulation is implemented. This method addresses the lack of a comprehensive virtual power plant regulation method that addresses the volatility and uncertainty of virtual power plants in existing technologies.

[0195] 2) The control device for a virtual power plant based on nonlinear operation and risk in this application acquires historical operating data. This historical operating data includes at least power plant control parameters, power plant cost parameters, and risk intervention parameters. The power plant control parameters include at least electricity market prices, power interaction data, generator operating costs, variable generation costs, and start-up / shutdown costs. The risk intervention parameters include at least risk value data for each risk event. The first construction unit constructs a power plant operation model based on the power plant control parameters, power plant cost parameters, and risk intervention parameters. This power plant operation model is used to simulate the net profit trend of the virtual power plant as a function of the power plant control parameters and risk intervention parameters. The second construction unit… The system constructs a first constraint group and a second constraint group. The first constraint group is used to impose nonlinear restrictions on the relationship between the power plant control parameters and the net profit in the power plant operation model. The second constraint group is used to impose nonlinear restrictions on the relationship between the risk intervention parameters and the net profit in the power plant operation model. The first constraint group includes investment amount constraints, power balance constraints, unit operation constraints, and demand response constraints. The second constraint group includes event risk constraints. Based on the first and second constraint groups, the calculation unit solves the power plant operation model by using the maximization of net profit as the objective function to obtain the power plant operation parameters. The virtual power plant is then controlled according to the power plant operation parameters. This application converts the operating parameters of a virtual power plant into continuous variables and correlates them with the virtual power plant's revenue. Furthermore, it combines operating status, power generation, and investment decisions for nonlinear modeling. Additionally, it introduces risk values ​​under different scenarios and the correlation of scenario-related profits for further nonlinear modeling. Simulation is performed based on a scenario stochastic framework to characterize the long-term uncertainties of production costs, demand levels, and market prices, as well as the uncertainties of market prices and renewable energy generation levels. This results in a decision-making model that integrates long-term and short-term uncertainties with the risk intervention of sudden events. Based on this decision-making model, virtual power plant regulation is implemented. This method addresses the lack of a comprehensive virtual power plant regulation method that addresses the volatility and uncertainty of virtual power plants in existing technologies.

[0196] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for regulating a virtual power plant based on non-linear operation and risk, characterized in that, The method comprises the following steps: acquiring historical operation data, wherein the historical operation data at least comprises power plant regulation parameters, power plant cost parameters and risk intervention parameters, the power plant regulation parameters at least comprise power market price, power exchange data, operation cost of a generator unit, variable power generation cost and start-stop cost, and the risk intervention parameters at least comprise risk value data under each risk event; constructing a power plant operation model according to the power plant regulation parameters, the power plant cost parameters and the risk intervention parameters, wherein the power plant operation model is used for simulating the change trend of net profit of the virtual power plant with the power plant regulation parameters and the risk intervention parameters; constructing a first constraint group and a second constraint group, wherein the first constraint group is used for nonlinearly limiting the correlation between the power plant regulation parameters and the net profit of the power plant operation model, the second constraint group is used for nonlinearly limiting the correlation between the risk intervention parameters and the net profit of the power plant operation model, the first constraint group comprises investment amount constraint, power balance constraint, unit operation constraint and demand response constraint, and the second constraint group comprises event risk constraint; solving the power plant operation model based on the first constraint group and the second constraint group, taking the maximum net profit as a target function, obtaining power plant operation parameters, and controlling the virtual power plant to operate according to the power plant operation parameters, constructing a first constraint group and a second constraint group, comprising: constructing the investment amount constraint of the virtual power plant corresponding to the power plant operation model, obtaining a first target constraint: ； wherein, is a set of to-be-built energy storage units, represents any one of the to-be-built energy storage units, TC is the total investment amount, is a set of to-be-built non-new energy units, represents any one of the to-be-built non-new energy units, is a fourth cost, is a fourth variable, is a set of to-be-built new energy units, represents any one of the to-be-built new energy units, is a fifth cost, is a fifth variable, is a sixth cost, is a sixth variable, 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 constructed, 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 constructed, 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 constructed. constructing the power balance constraint of the virtual power plant corresponding to the power plant operation model, obtaining a second target constraint: ； wherein, is the set of non-new energy units, R is the set of new energy units, S is the set of energy storage units, is the power generation of new energy units, is the power of charging and discharging of energy storage devices, respectively, is the power load, is the first power, which is the power sold by the virtual power plant, is the second power, which is the power generation of non-new energy units, h represents a representative day, and t represents an hourly time period, represents a scenario; constructing the unit operation constraint of the virtual power plant corresponding to the power plant operation model, obtaining a third target constraint: ； wherein, is a second variable, is a third variable, is a first variable, is a generation state of a unit at a previous time, is a generation capacity of the unit at the previous time, and are an upper limit and a lower limit of a generation capacity of a non-new energy unit, and are up and down ramping powers of the non-new energy unit, and are start and stop powers of the non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the second variable is a binary variable of whether the non-new energy unit starts, and the third variable is a binary variable of whether the non-new energy unit stops. constructing the demand response constraint of the virtual power plant corresponding to the power plant operation model, obtaining a fourth target constraint: ； wherein, and are upper and lower limits for demand response, is the daily minimum demand, H is a set of hourly time periods; obtaining the first constraint group by simultaneously solving the first target constraint, the second target constraint, the third target constraint and the fourth target constraint; constructing the event risk constraint of the virtual power plant corresponding to the power plant operation model, obtaining the second constraint group: ， is a risk value data, A is a revenue, B is an operation cost, Q is a construction cost, is a preset variable, is a day, F is a set of representative days, wherein, , is a target price, the target price is a price of an electricity quantity sold by the virtual power plant, , is a first coefficient, is a first cost, is a second cost, is a third cost, the first coefficient is a preset cost coefficient of a non-new energy unit, the first cost is an additional cost of the non-new energy unit varying with an electricity generation quantity, the second cost is a start-up cost of the non-new energy unit, and the third cost is a shutdown cost of the non-new energy unit.

2. The method of claim 1, wherein, constructing a power plant operation model according to the power plant regulation parameters, the power plant cost parameters and the risk intervention parameters, comprising: determining the target price, the first power, 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 according to the power plant regulation parameters; 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; determining risk value data and a preset variable according to the risk intervention parameters; constructing a first target formula according to the target price and the first power, wherein the first target formula is used for simulating the change trend of income of the virtual power plant with the target price and the first power; constructing a second objective formula according to the first coefficient, the first variable, the first cost, the second electric quantity, the second cost, the second variable, the third cost and the third variable, and constructing a third objective formula according to the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable, the second objective formula being used for simulating a variation trend of the operation cost of the virtual power plant with the first coefficient, the first variable, the first cost, the second electric quantity, the third cost and the third variable, and the third objective formula being used for simulating a variation 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 objective formula according to the risk intervention parameter and the preset variable, the fourth objective formula being used for representing a variation trend of the risk penalty with the risk intervention parameter and the preset variable; the power plant operation model is obtained by simultaneously solving the first objective formula, the second objective formula, the third objective formula and the fourth objective formula.

3. The method of claim 2, wherein, constructing a first objective formula according to the target price and the first electric quantity, including: constructing the first objective formula based on a correlation relationship between the target price and the first electric quantity and the income: ； wherein A is the income.

4. The method of claim 2, wherein, constructing a second objective formula according to the first coefficient, the first variable, the first cost, the second electric quantity, the second cost, the second variable, the third cost and the third variable, including: constructing the second objective formula based on a correlation relationship between the first coefficient, the first variable, the first cost, the second electric quantity, the second cost, the second variable, the third cost and the third variable and the operation cost: ； wherein B is the operation cost; constructing the third objective formula based on a correlation relationship between the fourth cost, the fourth variable, the fifth cost, the fifth variable, the sixth cost and the sixth variable and the construction cost: ； wherein Q is the construction cost.

5. The method of claim 2, wherein, constructing a fourth objective formula according to the risk intervention parameter and the preset variable, including: constructing the fourth objective formula based on a correlation relationship between the risk intervention parameter and the preset variable and the risk penalty: ； wherein, is the risk penalty, is a preset coefficient, is a confidence level, is a probability of the scenario occurring, is the total number of scenarios.

6. The method of claim 2, wherein, the power plant operation model is obtained by simultaneously solving the first objective formula, the second objective formula, the third objective formula and the fourth objective formula, including: the power plant operation model is obtained by simultaneously solving the first objective formula, the second objective formula, the third objective formula and the fourth objective formula: ； wherein, is the net profit, is the probability of the scenario occurring, is the risk penalty.

7. A virtual power plant regulating device based on non-linear operation and risk, characterized by, the device includes: an acquisition unit configured to acquire historical operation data, the historical operation data at least including power plant regulation parameters, power plant cost parameters and risk intervention parameters, the power plant regulation parameters at least including power market prices, electric energy interaction data, operation costs of generator units, variable power generation costs and start-stop costs, and the risk intervention parameters at least including risk value data under each risk event; the acquisition unit is configured to acquire historical operation data, the historical operation data at least including power plant regulation parameters, power plant cost parameters and risk intervention parameters, the power plant regulation parameters at least including power market prices, electric energy interaction data, operation costs of generator units, variable power generation costs and start-stop costs, and the risk intervention parameters at least including risk value data under each risk event; The first constructing unit is configured to construct a power plant operation model according to the power plant regulation parameter, the power plant cost parameter and the risk intervention parameter, the power plant operation model being used to simulate a change trend of net profit of the virtual power plant with the power plant regulation parameter and the risk intervention parameter; The second constructing unit is configured to construct a first constraint group and a second constraint group, the first constraint group being used to nonlinearly limit an association between the power plant regulation parameter and the net profit of the power plant operation model, the second constraint group being used to nonlinearly limit an association between the risk intervention parameter and the net profit of the power plant operation model, the first constraint group including an investment quota constraint, a power balance constraint, a unit operation constraint and a demand response constraint, and the second constraint group including an event risk constraint; The calculating unit is configured to solve the power plant operation model based on the first constraint group and the second constraint group, so as to obtain a power plant operation parameter, and control the virtual power plant to operate according to the power plant operation parameter. The second constructing unit includes: The fourth constructing sub-unit is configured to construct the investment quota constraint of the virtual power plant corresponding to the power plant operation model, so as to obtain a first target constraint: ； wherein, is a set of to-be-built energy storage units, represents any one of the to-be-built energy storage units, and TC is a total investment amount, is a set of to-be-built non-new energy units, represents any one of the to-be-built non-new energy units, is a fourth cost, is a fourth variable, is a set of to-be-built new energy units, represents any one of the to-be-built new energy units, is a fifth cost, is a fifth variable, is a sixth cost, is a sixth variable, the fourth cost is a construction cost of the non-new energy unit, the fourth variable is a binary variable of whether the non-new energy unit is constructed, the fifth cost is a construction cost of the new energy unit, the fifth variable is a binary variable of whether the new energy unit is constructed, the sixth cost is a construction cost of the energy storage device, and the sixth variable is a binary variable of whether the energy storage device is constructed. The fifth constructing sub-unit is configured to construct the power balance constraint of the virtual power plant corresponding to the power plant operation model, so as to obtain a second target constraint: ； wherein, is the set of non-new energy units, R is the set of new energy units, S is the set of energy storage units, is the power generation of new energy units, respectively, the power of charging and discharging of energy storage devices, is the power load, is the first power, which is the power sold by the virtual power plant, is the second power, which is the power generation of non-new energy units, h represents a representative day, and t represents an hourly time period, represents a scenario; The sixth constructing sub-unit is configured to construct the unit operation constraint of the virtual power plant corresponding to the power plant operation model, so as to obtain a third target constraint: ； wherein, is a second variable, is a third variable, is a first variable, is a generation state of the unit at a previous time, is a generation capacity of the unit at the previous time, and are an upper limit and a lower limit of the generation capacity of the non-new energy unit, and are up and down ramping powers of the non-new energy unit, and are start and stop powers of the non-new energy unit, the first variable is a binary variable of whether the non-new energy unit generates electricity, the second variable is a binary variable of whether the non-new energy unit starts, and the third variable is a binary variable of whether the non-new energy unit stops. The seventh constructing sub-unit is configured to construct the demand response constraint of the virtual power plant corresponding to the power plant operation model, so as to obtain a fourth target constraint: ； wherein, and are upper and lower limits of demand response, is the daily minimum demand, H is a set of hourly time periods; The second simultaneous sub-unit is configured to simultaneously solve the first target constraint, the second target constraint, the third target constraint and the fourth target constraint, so as to obtain the first constraint group. The eighth constructing sub-unit is configured to construct the event risk constraint of the virtual power plant corresponding to the power plant operation model, so as to obtain the second constraint group. , is a risk value data, A is a revenue, B is an operation cost, Q is a construction cost, is a preset variable, is a day, F is a set of representative days, wherein, , is a target price, the target price is a price of an electricity quantity sold by the virtual power plant, , is a first coefficient, is a first cost, is a second cost, is a third cost, the first coefficient is a preset cost coefficient of a non-new energy unit, the first cost is an additional cost of the non-new energy unit varying with an electricity generation quantity, the second cost is a start-up cost of the non-new energy unit, and the third cost is a shutdown cost of the non-new energy unit.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored program, wherein the program controls the device where the computer readable storage medium is located to execute the method in any one of claims 1 to 6 when the program is running.

9. A virtual power plant regulation system, characterized by, The one or more processors, the memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a program for executing the method in any one of claims 1 to 6. ​

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