Stochastic scheduling methods and control systems for virtual power plants that reduce carbon emissions

Abstract

This application provides a stochastic scheduling method and control system for virtual power plants that consider carbon emission reduction. The method includes: acquiring historical operating parameters of the virtual power plant; simulating each component based on the historical operating parameters, and simultaneously establishing a first target model by combining the physical models of each component; constructing a numerical model to characterize the correlation between cost variation parameters of each component and historical operating parameters, obtaining a component cost model; simultaneously establishing a second target model by combining the cost models of each component based at least on energy flow relationships; maximizing the output parameters of the second target model and minimizing the output parameters of the first target model as the objective function, solving it under the constraints of a stochastic model and a preset set of constraints to obtain target operating parameters and generate target control commands to control the operation of the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant dispatching, and more specifically, to a random dispatching method, dispatching device, computer-readable storage medium, processor, and virtual power plant control system for virtual power plants that take carbon emission reduction into account. Background Technology

[0002] Driven by profound transformation and innovation in the energy sector, achieving complementary energy utilization and improving system flexibility have become important trends in promoting future energy development. As pilot projects for new energy models such as virtual power plants and integrated energy parks gradually enter the trial operation phase, we are moving towards a more efficient and intelligent energy system.

[0003] Against this backdrop, thermal power units gain greater flexibility and initiative through carbon trading mechanisms, providing a strong impetus for the low-carbon transformation of regional energy systems. Specifically, carbon capture devices are responsible for collecting and storing the carbon dioxide generated during the power generation process of thermal power units. This carbon dioxide is then transferred to power-to-gas (HPC) equipment, where excess electricity is converted into methane, which is then sold as a byproduct of natural gas, generating economic benefits for enterprises. However, since carbon capture and HPC equipment may not be able to guarantee the complete absorption and utilization of all carbon dioxide generated by virtual power plants in practical applications, achieving net-zero emissions remains challenging. To overcome this challenge, there is an urgent need to establish a low-carbon economic dispatch method for virtual power plants. By optimizing dispatch strategies, energy efficiency can be further improved, carbon emissions reduced, and the development of virtual power plants towards a low-carbon and environmentally friendly direction. Summary of the Invention

[0004] The main objective of this application is to provide a random dispatching method, dispatching device, computer-readable storage medium, processor, and virtual power plant control system for virtual power plants that take into account the reduction of carbon emissions, so as to at least solve the problem that the virtual power plant dispatching schemes in the prior art do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

[0005] To achieve the above objectives, according to one aspect of this application, a stochastic scheduling method for a virtual power plant considering carbon emission reduction is provided. The method includes: acquiring historical operating parameters of the virtual power plant, the historical operating parameters including multiple historical operating parameters and cost variation parameters, wherein the multiple historical operating parameters are operating parameters of different components of the virtual power plant, the components including at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices; the cost variation parameters being the operating costs of different components of the virtual power plant; the thermal power units supplying the electrical load of the carbon capture devices and the virtual power plant; the wind power units supplying the electrical load of the power-to-gas devices and the virtual power plant; simulating each component of the virtual power plant according to the historical operating parameters to obtain corresponding component physical models; and determining the energy relationships between each component based on the historical operating parameters. Based on the energy flow relationship, the physical models of each component are simultaneously established to obtain a first target model. A numerical model is constructed based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, resulting in a component cost model. At least based on the energy flow relationship, the cost models of each component are simultaneously established to obtain a second target model. The objective function is to maximize the output parameters of the second target model and minimize the output parameters of the first target model. Based on a robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and a preset constraint group, the objective function is solved to obtain target operating parameters. Target control commands are generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of each component's physical model.

[0006] Optionally, simulations are performed on each component of the virtual power plant based on the historical operating parameters to obtain corresponding component physical models. This includes: obtaining a first power generation capacity; expressing the operating state of the thermal power unit as a binary variable; using the operating state as an input parameter and a second power generation capacity as an output parameter; constructing the component physical model corresponding to the thermal power unit based on the first power generation capacity and the operating state to obtain a first physical model; where the first power generation capacity is the rated power generation capacity of the thermal power unit, and the second power generation capacity is the actual power generation capacity of the thermal power unit; obtaining a third power generation capacity, a first wind speed, and a second wind speed. Using the second wind speed, third wind speed, and real-time wind speed as the output parameter and the real-time wind speed as the input parameter, a physical model of the component corresponding to the wind turbine is constructed based on the second wind speed, the first wind speed, the second wind speed, the third wind speed, and the real-time wind speed, resulting in a second physical model. The third wind speed is the rated power of the wind turbine, the first wind speed is the cut-in wind speed of the wind turbine, the second wind speed is the cut-out wind speed of the wind turbine, and the third wind speed is the rated wind speed of the wind turbine. The first wind speed is less than the third wind speed, and the third wind speed is less than the second wind speed. The fourth power generation is the actual power generation of the wind turbine; the target solar radiation, target area, and first preset coefficient are obtained. Using the target solar radiation as the input parameter and the fifth power generation as the output parameter, a physical model of the element corresponding to the photovoltaic unit is constructed based on the target solar radiation, the target area, and the first preset coefficient to obtain a third physical model. The target solar radiation is the radiation intensity of the current solar radiation, and the fifth power generation is the actual power generation of the photovoltaic unit; the first power consumption and first preset power consumption are obtained. Using the first power consumption as the input parameter and the first content as the output parameter, a physical model of the element corresponding to the carbon capture device is constructed based on the first power consumption and the first preset power consumption to obtain a fourth physical model. The first power consumption is the real-time operating power consumption of the carbon capture device, and the first content is the content of carbon dioxide collected by the carbon capture device; the second power consumption and second preset coefficient are obtained. Using the second power consumption as the input parameter and the second content as the output parameter, a physical model of the element corresponding to the power-to-gas device is constructed based on the second power consumption and the second preset coefficient to obtain a fifth physical model. The second power consumption is the real-time operating power consumption of the power-to-gas device, and the second content is the content of methane generated by the power-to-gas device.

[0007] Optionally, the first target model is obtained by simultaneously establishing the physical models of each component based on the energy flow relationship, including: obtaining a first target load and a third power consumption; setting the second power generation equal to the sum of the first target load, the third power consumption, and the first power consumption to obtain a first target formula, where the first target load is the portion of the total electrical load borne by the thermal power unit, and the third power consumption is the fixed power consumption of the carbon capture device; obtaining a second target load; setting the fourth power generation equal to the sum of the second target load and the second power consumption to obtain a second target formula, where the second target load is the portion of the total load borne by the wind power unit; and simultaneously establishing the first physical model, the second physical model, the third physical model, the fourth physical model, the fifth physical model, the first target formula, and the second target formula to obtain the first target model.

[0008] Optionally, obtaining the target solar radiation includes: determining a first solar radiation and a second solar radiation based on the historical operating parameters, wherein the first solar radiation is the mean of the solar radiation and the second solar radiation is a normally distributed value of the solar radiation; substituting the first solar radiation and the second solar radiation into a first target formula to obtain a first shape parameter; and substituting the first shape parameter and the first solar radiation into a second target formula to obtain a second shape parameter. Where β is the first shape parameter, μ is the first solar radiation, δ is the second solar radiation, and α is the second shape parameter; obtain a third preset coefficient, and substitute the third preset coefficient, the first solar radiation, and the second solar radiation into the third target formula to obtain the target solar radiation: Where P(θ) is the target solar radiation, θ is the third preset coefficient, and θ d θ is the upper limit of solar radiation. c As the lower limit of solar load, f(θ) follows a beta distribution:

[0009]

[0010] Optionally, a numerical model is constructed based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, resulting in a component cost model. At least based on the energy flow relationship, the component cost models are simultaneously established to obtain a second target model, including: obtaining a first cost; constructing the component cost model corresponding to the thermal power unit based on the first cost and the first physical model, resulting in a first cost model, where the first cost is the start-up cost of the thermal power unit.

[0011] Among them, D Th,t For the first cost, QTh,t For the second power generation, a Th b Th and c Th Let u be the energy consumption coefficient of the thermal power unit. Th,t Let t represent the operating state of the thermal power unit. The fuel cost of the thermal power unit. r is the start-up and shutdown cost of the thermal power unit. Th The total revenue of the thermal power unit. The real-time unit electricity price is used; a second cost and a third cost are obtained; based on the second cost, the third cost, the second physical model, and the third physical model, the component cost models corresponding to the wind turbine and the photovoltaic unit are constructed to obtain the second cost model, where the second cost is the operating cost of the wind turbine and the third cost is the operating cost of the photovoltaic unit. in, and Let be the total cost of the wind turbine and the photovoltaic unit at time t, respectively. and C z PV Let be the depreciation costs of the wind turbine and the photovoltaic unit at time t, respectively. and p represents the fourth power generation capacity and the fifth power generation capacity, respectively. WPP and p PV The depreciation cost per unit power of the wind turbine and the photovoltaic unit, respectively, r wpv r represents the total revenue of the wind turbine and the photovoltaic unit. WPP and r PV The electricity sales revenue of the wind turbine and the photovoltaic unit are respectively; based on the fourth physical model, the component cost model corresponding to the carbon capture device is constructed to obtain the third cost model: C CC =a(P t CC ) 2 +bP t CC +c; where C CC P represents the total cost of the carbon capture device. t CC The first power consumption is given, and a, b, and c are the operating cost coefficients of the carbon capture device; based on the fifth physical model, the component cost model corresponding to the electro-gas conversion device is constructed to obtain the fourth cost model: r t pg =Q pg,t *p gas ; where r t pgp represents the total revenue of the electro-gas conversion device. gas For natural gas prices, Q pg,t Let e ​​be the second content at time t; obtain the first emission amount; construct a third target formula based on the second power generation, the first emission amount, and the second emission amount; set the second emission amount to be greater than or equal to the first content, and the first content to be greater than or equal to the second content, to obtain a fourth target formula; the first emission amount is the carbon dioxide emission per unit power generation of the thermal power unit, and the second emission amount is the total emission of the thermal power unit: e Th,t =Q Th，t ·e Th ; where e Th,t For the second emission amount, e Th Let the first emission amount be denoted by the third target formula, the fourth target formula, the first cost model, the second cost model, the third cost model, and the fourth cost model. Then, the second target model is obtained by combining these three cost models.

[0012] Optionally, the method further includes constructing a first objective function: F1 = maxπ, where the second objective model output parameters are maximized and the first objective model output parameters are minimized. VPP =RC=(r Th +r wpv +r pg )-(C Th +C WPP +C PV +C CC ); where π VPP Given the total revenue of the virtual power plant; construct a second objective function: in, For the second content, QCC t This refers to the first content.

[0013] Optionally, based on the robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model, including: modeling the wind turbine using the envelope constraint model to obtain a first stochastic model: Among them, P WPP This represents the actual power generation of the wind turbine. For the historical output data of wind turbines within the scheduling cycle, α WPP The uncertainty of the output of the wind turbine; by modeling the photovoltaic unit using the envelope constraint model, a second stochastic model is obtained: Among them, P WPP This represents the actual power generation of the photovoltaic unit. For the historical output data of photovoltaic units within the scheduling cycle, αWPP The uncertainty of the output of the photovoltaic unit; based on the first uncertainty, the second uncertainty, and the target uncertainty, a fifth target formula is constructed, where the first uncertainty is the uncertainty of the output of the wind turbine unit, the second uncertainty is the uncertainty of the output of the photovoltaic unit, and the target uncertainty is the total uncertainty of the virtual power plant: α = α WPP *λ WPP +α PV *λ PV Where α is the total uncertainty, and λ is the total uncertainty. WPP and λ PV The weight parameters corresponding to the first uncertainty and the second uncertainty are respectively; by combining the fifth objective formula, the first stochastic model and the second stochastic model, the stochastic model is obtained.

[0014] According to another aspect of this application, a random scheduling device for a virtual power plant considering carbon emission reduction is provided. The device includes: a first acquisition unit, configured to acquire historical operating parameters of the virtual power plant, the historical operating parameters including multiple historical operating parameters and cost variation parameters, wherein the multiple historical operating parameters are operating parameters of different components of the virtual power plant, the components including at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices, and the cost variation parameters being the operating costs of different components of the virtual power plant; wherein the thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant, and the wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant; and a simulation unit, configured to simulate each component of the virtual power plant according to the historical operating parameters, obtain corresponding component physical models, and determine the energy flow between each component based on the historical operating parameters. The system comprises: a first target model, which is obtained by simultaneously establishing the physical models of each component based on the energy flow relationship; a construction unit, which is used to construct a numerical model based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, thereby obtaining a component cost model; and a second target model, which is obtained by simultaneously establishing the cost models of each component based on the energy flow relationship; and a calculation unit, which is used to maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function; to model the wind turbine and the thermal power unit based on the robustness factor through an envelope constraint model, thereby obtaining a stochastic model; to solve the objective function under the constraints of the stochastic model and a preset constraint group, thereby obtaining target operating parameters; and to generate target control commands based on the target operating parameters to control the operation of the virtual power plant, wherein the preset constraint group is used to limit the parameter values ​​of each component physical model.

[0015] 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 any of the methods described in the device where the computer-readable storage medium is located.

[0016] 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 any one of the methods described.

[0017] Applying the technical solution of this application, in the above-mentioned random dispatch method for a virtual power plant considering carbon emission reduction, the virtual power plant includes a power generation module and a carbon conversion module. The power generation module includes thermal power units, wind power units, and photovoltaic units. The carbon conversion module includes the thermal power units, wind power units, a carbon capture device, and an electricity-to-gas conversion device. The thermal power units are used to supply the electrical load of the carbon capture device and the virtual power plant, and the wind power units are used to supply the electrical load of the electricity-to-gas conversion device and the virtual power plant. In the above method, firstly, the historical operation data of the virtual power plant is obtained. The parameters, the aforementioned historical operating parameters, include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. These components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas conversion devices. The cost variation parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture device and the virtual power plant, and the wind power units are used to supply the electrical load of the power-to-gas conversion device and the virtual power plant. Then, based on the aforementioned historical... The historical operating parameters are used to simulate each of the aforementioned components of the virtual power plant, resulting in corresponding component physical models. Based on the historical operating parameters, the energy flow relationships between the aforementioned components are determined. Based on the energy flow relationships, the physical models of the aforementioned components are simultaneously established to obtain a first target model. Subsequently, based on the historical operating parameters, a numerical model is constructed to characterize the correlation between the cost change parameters of the aforementioned components and the historical operating parameters, resulting in a component cost model. At least based on the energy flow relationships, the cost models of the aforementioned components are simultaneously established to obtain a second target model. Finally, the objective function is to maximize the output parameters of the second target model and minimize the output parameters of the first target model. Based on the robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and a preset constraint set, the objective function is solved to obtain target operating parameters. Target control commands are generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint set is used to limit the parameter values ​​of the physical models of the aforementioned components. This application sets up simulation models for thermal power, wind power, and photovoltaic units in a virtual power plant. Based on this, it integrates the simulation of carbon dioxide generation from thermal power units through carbon capture devices and power-to-gas conversion devices in existing virtual power plants. Then, based on the energy flow between each unit and the carbon capture device and the power-to-gas conversion device, the models are linked together to construct a corresponding cost calculation model. Considering that zero emissions cannot be achieved, the solution is based on optimal economics and minimum carbon emissions to obtain the corresponding operating parameters for controlling the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy. Attached Figure Description

[0018] Figure 1 A hardware block diagram of a mobile terminal for a random scheduling method of a virtual power plant that takes into account carbon emission reduction, provided in an embodiment of this application, is shown.

[0019] Figure 2 A flowchart illustrating a stochastic scheduling method for a virtual power plant that takes into account carbon emission reduction, according to an embodiment of this application, is shown.

[0020] Figure 3 A structural block diagram of a random scheduling device for a virtual power plant that takes into account carbon emission reduction, according to an embodiment of this application, is shown.

[0021] The above figures include the following reference numerals:

[0022] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] 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.

[0026] As described in the background section, existing carbon capture and power-to-gas (HPC) equipment cannot guarantee that all carbon dioxide generated by the virtual power plant will be absorbed and utilized, thus failing to achieve net-zero emissions. To address the issue that virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy, embodiments of this application provide a random scheduling method, scheduling device, computer-readable storage medium, processor, and virtual power plant control system that considers reducing carbon emissions.

[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 random scheduling method of a virtual power plant considering carbon emission reduction, 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 random scheduling method for a virtual power plant that considers reducing carbon emissions, 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 a 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 random scheduling method for virtual power plants considering carbon emission reduction, according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0032] Step S201: Obtain historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0033] Specifically, this application considers scheduling based on multiple historical operating parameters and cost change parameters of the virtual power plant. It obtains at least the operating status of the virtual power plant's thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices as historical operating parameters, and the operating costs of the aforementioned components as cost change parameters.

[0034] Step S202: Simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters to obtain the corresponding component physical model, and determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0035] Specifically, for the components of the virtual power plant, such as thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas equipment, simulations are performed based on historical operating parameters. The performance of each component under different working environments is calculated, and the energy flow relationship between electricity and carbon dioxide in the processes of power generation, transmission, and consumption among the components is analyzed and determined. Based on the energy flow relationship, the physical models of each component are established to obtain the first target model, which simulates the actual operation of the power plant.

[0036] Step S203: Construct a numerical model based on the above historical operating parameters to characterize the correlation between the cost change parameters of each of the above components and the above historical operating parameters, thereby obtaining a component cost model. At least based on the above energy flow relationship, combine the cost models of each of the above components to obtain a second target model.

[0037] Specifically, a numerical model is constructed using historical operating parameters to measure the cost variation parameters as they change. This model is used to quantify the costs of each component in the virtual power plant under different operating conditions. Furthermore, a second objective model is constructed based on the energy flow relationships between the components in the virtual power plant, which is used to evaluate the overall cost-effectiveness of the virtual power plant under different scheduling strategies.

[0038] Step S204: Maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function. Based on the robustness factor, model the wind turbine and the thermal power unit through the envelope constraint model to obtain a stochastic model. Solve the objective function under the constraints of the stochastic model and the preset constraint group to obtain the target operating parameters. Generate target control commands based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0039] Specifically, considering the uncertainties in the actual operation of the virtual power plant, a robustness factor is introduced to enhance the model's resilience, ensuring that the virtual power plant can maintain stable performance in the face of fluctuations and uncertainties, under the premise of maximizing the output parameters of the second objective model and minimizing the output parameters of the first objective model. An envelope constraint model is used to construct a model that reflects the stochastic output of the power generation unit, which is used to simulate and predict the performance of wind turbines and thermal power units under various possible conditions. A preset constraint group is set to limit the parameter value range of the physical model of each component of the virtual power plant, ensuring that all components operate within a safe and feasible range. Under the above constraints, the objective function is solved to obtain the target operating parameters, and corresponding control commands are generated to precisely control the operating status of the thermal power units, wind turbines, photovoltaic units and other components of the virtual power plant.

[0040] In this embodiment, firstly, historical operating parameters of the virtual power plant are obtained. These historical operating parameters include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. These components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas conversion devices. The cost variation parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant, and the wind power units are used to supply the electrical load of the power-to-gas conversion devices and the virtual power plant. Then, each component of the virtual power plant is simulated according to the historical operating parameters to obtain the corresponding component physical model. Based on the historical operating parameters, the energy flow relationship between each component is determined, and based on the energy flow relationship, each component is... The physical models are combined to obtain the first target model. Then, based on the historical operating parameters, a numerical model is constructed to characterize the correlation between the cost change parameters of each component and the historical operating parameters, resulting in a component cost model. At least based on the energy flow relationship, the cost models of each component are combined to obtain the second target model. Finally, the objective function is to maximize the output parameters of the second target model and minimize the output parameters of the first target model. Based on the robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and a preset constraint set, the objective function is solved to obtain the target operating parameters. Target control commands are generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint set is used to limit the parameter values ​​of each component's physical model. This application sets up simulation models for thermal power, wind power, and photovoltaic units in a virtual power plant. Based on this, it integrates the simulation of carbon dioxide generation from thermal power units through carbon capture devices and power-to-gas conversion devices in existing virtual power plants. Then, based on the energy flow between each unit and the carbon capture device and the power-to-gas conversion device, the models are linked together to construct a corresponding cost calculation model. Considering that zero emissions cannot be achieved, the solution is based on optimal economics and minimum carbon emissions to obtain the corresponding operating parameters for controlling the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

[0041] In order to construct a physical model of the component, in one optional implementation, step S202 above includes:

[0042] Step S20201: Obtain the first power generation, express the operating state of the thermal power unit in the form of binary variables, take the operating state as input parameter and the second power generation as output parameter, construct the physical model of the component corresponding to the thermal power unit based on the first power generation and the operating state, and obtain the first physical model. The first power generation is the rated power generation of the thermal power unit, and the second power generation is the actual power generation of the thermal power unit.

[0043] Specifically, the maximum power generation capacity of the thermal power unit is first determined, i.e., the rated power generation capacity. A binary variable is used to represent the operating state of the thermal power unit, indicating whether the thermal power unit is in a shutdown state or an operating state. Based on the rated power generation capacity and the actual operating state of the thermal power unit, its physical model is constructed to obtain the first physical model. The actual power generation capacity of the thermal power unit is predicted through the model to obtain the second power generation capacity.

[0044] Step S20202: Obtain the third power generation, first wind speed, second wind speed, third wind speed and real-time wind speed. Use the fourth power generation as the output parameter and the real-time wind speed as the input parameter. Construct the physical model of the components corresponding to the wind turbine based on the second power generation, first wind speed, second wind speed, third wind speed and real-time wind speed to obtain the second physical model. The third power generation is the rated power of the wind turbine. The first wind speed is the cut-in wind speed of the wind turbine. The second wind speed is the cut-out wind speed of the wind turbine. The third wind speed is the rated wind speed of the wind turbine. The first wind speed is less than the third wind speed. The third wind speed is less than the second wind speed. The fourth power generation is the actual power generation of the wind turbine.

[0045] Specifically, the aforementioned second physical model is Among them, Q j,w (t) represents the available output of wind turbine j at time t; g r This refers to the rated output power of the wind turbine generator; v i,w v o,w The cut-in and cut-out wind speeds are respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

[0046] Step S20203: Obtain the target solar radiation, target area and first preset coefficient. Using the target solar radiation as input parameter and the fifth power generation as output parameter, construct the physical model of the components corresponding to the photovoltaic unit based on the target solar radiation, target area and first preset coefficient to obtain the third physical model. The target solar radiation is the radiation intensity of the current solar radiation, and the fifth power generation is the actual power generation of the photovoltaic unit.

[0047] Specifically, the third physical model mentioned above is g m,pv (t)=η PV ×S PV ×θ t , where g m,pv (t) represents the fifth power generation capacity mentioned above, η PV S is the first preset coefficient mentioned above. PV For the target area mentioned above, θ t The solar radiation for the aforementioned target.

[0048] Step S20204: Obtain the first power consumption and the first preset power consumption. Using the first power consumption as the input parameter and the first content as the output parameter, construct the physical model of the component corresponding to the carbon capture device based on the first power consumption and the first preset power consumption to obtain the fourth physical model. The first power consumption is the real-time operating power consumption of the carbon capture device, and the first content is the content of carbon dioxide collected by the carbon capture device.

[0049] Specifically, the aforementioned fourth physical model is Among them, P t op Let QCC be the energy consumption of the carbon capture device at time t, i.e., the power consumption mentioned above. t The amount of CO2 captured by the carbon capture device, i.e., the first content mentioned above; The operating energy consumption of the carbon capture device for processing a unit of CO2, i.e., the first preset power consumption mentioned above.

[0050] Step S20205: Obtain the second power consumption and the second preset coefficient. Using the second power consumption as the input parameter and the second content as the output parameter, construct the component physical model corresponding to the electro-gas conversion device based on the second power consumption and the second preset coefficient to obtain the fifth physical model. The second power consumption is the real-time operating power consumption of the electro-gas conversion device, and the second content is the content of methane generated by the electro-gas conversion device.

[0051] Specifically, the fifth physical model mentioned above is Q. pg,t =P pg,t η pg , where Q pg,t This refers to the amount of gas generated through electro-gas conversion, i.e., the second content mentioned above. P pg,t This refers to the electricity consumed in the electro-gas conversion, i.e., the second power consumption mentioned above. η pg This represents the electrical conversion efficiency, which is the second preset coefficient mentioned above.

[0052] In order to construct the first target model, in one optional implementation, step S202 above includes:

[0053] Step S20211: Obtain the first target load and the third power consumption, set the second power generation power equal to the sum of the first target load, the third power consumption and the first power consumption, and obtain the first target formula. The first target load is the part of the total electrical load borne by the thermal power unit, and the third power consumption is the fixed power consumption of the carbon capture device.

[0054] Specifically, by determining the target load and related energy consumption of thermal power units, a target formula representing the power generation capacity and energy consumption of thermal power units is constructed. First, the portion of the total grid load allocated to thermal power units is obtained, which represents the first target load that the thermal power units need to meet, and the fixed energy consumption required for the normal operation of the carbon capture device, which is the third power consumption. The power drive of the carbon capture device mainly comes from the thermal power units. The actual power generation of the thermal power units is the sum of the first target load, the third power consumption, and the first power consumption, which is used to calculate the power generation that the thermal power units need to achieve in order to meet the load and power consumption requirements of the virtual power plant.

[0055] Step S20212: Obtain the second target load, set the fourth power generation to the sum of the second target load and the second power consumption, and obtain the second target formula. The second target load is the portion of the total load borne by the wind turbine.

[0056] Specifically, by determining the target load and related energy consumption of the wind turbine, a target formula representing the power generation capacity and energy consumption of the wind turbine is constructed. First, the power generation capacity that the wind turbine needs to achieve is obtained to meet its target load and its own energy consumption, which is the second target load mentioned above. The power consumption of the power-to-gas conversion device mainly comes from the curtailed wind power. Therefore, the sum of the second power consumption and the second target load is used as the second target formula to calculate the power generation capacity that the wind turbine needs to achieve to meet its target load and its own energy consumption.

[0057] Step S20213: Combine the above-mentioned first physical model, the above-mentioned second physical model, the above-mentioned third physical model, the above-mentioned fourth physical model, the above-mentioned fifth physical model, the above-mentioned first objective formula and the above-mentioned second objective formula to obtain the above-mentioned first objective model.

[0058] Specifically, by combining the operational characteristics of multiple models such as thermal power, wind power, photovoltaic power, and carbon capture, a unified primary target model is formed. The inputs, outputs, and internal parameters of different models are coordinated and integrated to achieve comprehensive optimization scheduling of all components within the virtual power plant.

[0059] In order to calculate the power generation capacity of the photovoltaic unit, in one optional embodiment, step S20203 above includes:

[0060] Step S202031: Determine the first solar radiation and the second solar radiation based on the above historical operating parameters. The first solar radiation is the mean value of solar radiation, and the second solar radiation is the normal distribution value of solar radiation.

[0061] Specifically, we analyze solar radiation data collected during the past operation of the photovoltaic power generation system, calculate the mean solar radiation of these historical data, which reflects the average level of solar radiation that the photovoltaic power generation system can expect under normal operating conditions, and determine the normal distribution value of solar radiation of these historical data in order to predict and simulate possible changes in solar radiation.

[0062] Step S202032: Substitute the first solar radiation and the second solar radiation into the first target formula to obtain the first shape parameter; substitute the first shape parameter and the first solar radiation into the second target formula to obtain the second shape parameter.

[0063]

[0064] Wherein, β is the first shape parameter, μ is the first solar radiation, δ is the second solar radiation, and α is the second shape parameter;

[0065] Specifically, the mean and standard deviation of solar radiation are introduced to calculate two shape parameters, which are used as parameters for calculating the Beta distribution of solar radiation.

[0066] Step S202033: Obtain the third preset coefficient, and substitute the third preset coefficient, the first solar radiation, and the second solar radiation into the third target formula to obtain the target solar radiation:

[0067]

[0068] Where P(θ) is the target solar radiation, θ is the third preset coefficient, and θ d θ is the upper limit of solar radiation. c As the lower limit of solar load, f(θ) follows a beta distribution:

[0069]

[0070] Specifically, based on the beta distribution and the third objective formula, the changes in solar radiation are predicted according to the upper and lower limits of solar radiation, so as to better reflect the performance of photovoltaic power generation systems under different solar radiation conditions.

[0071] In order to obtain the second target model, in one optional implementation, step S203 above includes:

[0072] Step S2031: Obtain the first cost. Based on the first cost and the first physical model, construct the component cost model corresponding to the thermal power unit to obtain the first cost model. The first cost is the start-up cost of the thermal power unit.

[0073]

[0074] Among them, D Th,t For the aforementioned first cost, Q Th,t For the aforementioned second power generation, a Th b Th and c Th Let u be the energy consumption coefficient of the aforementioned thermal power units. Th,t Let t represent the operating status of the aforementioned thermal power unit. The fuel cost of the aforementioned thermal power units, For the start-up and shutdown costs of the aforementioned thermal power units, r Th The total revenue of the aforementioned thermal power units, This refers to the real-time unit electricity price;

[0075] Step S2032: Obtain the second cost and the third cost. Based on the second cost, the third cost, the second physical model, and the third physical model, construct the component cost models corresponding to the wind turbine and the photovoltaic unit to obtain the second cost model. The second cost is the operating cost of the wind turbine, and the third cost is the operating cost of the photovoltaic unit.

[0076]

[0077] in, and Let be the total cost of the aforementioned wind turbine and photovoltaic unit at time t, respectively. and The values ​​are the depreciation costs of the aforementioned wind turbine and photovoltaic unit at time t, respectively. and These are the fourth and fifth power generation capacities mentioned above, respectively, p WPP and p PV The depreciation costs per unit power of the aforementioned wind turbine and photovoltaic unit are respectively, r wpv For the total revenue of the aforementioned wind turbines and photovoltaic units, r WPP and r PV The revenue from the sale of electricity from the aforementioned wind turbine units and the aforementioned photovoltaic units, respectively;

[0078] Step S2033: Based on the fourth physical model described above, construct the cost model of the components corresponding to the carbon capture device to obtain the third cost model:

[0079] CCC =a(P t CC ) 2 +bP t CC +c;

[0080] Among them, C CC For the total cost of the aforementioned carbon capture device, P t CC The first power consumption is given above, and a, b, and c are the operating cost coefficients of the carbon capture device mentioned above;

[0081] Step S2034: Based on the fifth physical model, construct the cost model of the components corresponding to the electro-gas conversion device to obtain the fourth cost model:

[0082] r t pg =Q pg,t *p gas ;

[0083] Where, r t pg For the total revenue of the aforementioned electro-gas conversion device, p gas For natural gas prices, Q pg,t The second content mentioned above at time t;

[0084] Step S2035: Obtain the first emission amount; construct a third target formula based on the second power generation, the first emission amount, and the second emission amount; set the second emission amount to be greater than or equal to the first content, and the first content to be greater than or equal to the second content, to obtain a fourth target formula; the first emission amount is the carbon dioxide emission per unit power generation of the thermal power unit, and the second emission amount is the total emission of the thermal power unit.

[0085] e Th,t =Q Th，t ·e Th ;

[0086] Among them, e Th,t For the second emission mentioned above, e Th This refers to the first emission level mentioned above;

[0087] Step S2036: Combine the above-mentioned third objective formula, fourth objective formula, first cost model, second cost model, third cost model and fourth cost model to obtain the above-mentioned second objective model.

[0088] Specifically, by combining the above objective formula and cost model, we can analyze the contribution and interaction of different energy technologies and optimize the cost-effectiveness and performance of the entire energy system.

[0089] In an optional implementation, to construct the objective function, step S204 above includes:

[0090] Step S20401, construct the first objective function:

[0091] F1 = maxπ VPP =RC=(r Th +r wpv +r pg )-(C Th +C WPP +C PV +C CC );

[0092] Where, π VPP The total revenue of the aforementioned virtual power plant;

[0093] Step S20402, construct the second objective function:

[0094]

[0095] in, For the second content mentioned above, QCC t This refers to the first content mentioned above.

[0096] To obtain the stochastic model, in one optional implementation, step S204 above includes:

[0097] Step S20411: Model the wind turbine using the envelope constraint model to obtain the first stochastic model:

[0098]

[0099] Among them, P WPP This represents the actual power generation of the wind turbine. For the historical output data of wind turbines within the scheduling cycle, α WPP The uncertainty of the output of the wind turbine;

[0100] Step S20412: Model the photovoltaic unit using the envelope constraint model to obtain the second stochastic model:

[0101]

[0102] Among them, P WPP This represents the actual power generation of the photovoltaic unit. For the historical output data of photovoltaic units within the scheduling cycle, α WPP The uncertainty of the output of the photovoltaic unit;

[0103] Step S20413: Based on the first uncertainty, the second uncertainty, and the target uncertainty, construct a fifth target formula, where the first uncertainty is the uncertainty of the output of the wind turbine, the second uncertainty is the uncertainty of the output of the photovoltaic unit, and the target uncertainty is the total uncertainty of the virtual power plant.

[0104] α=α WPP *λ WPP +α PV *λ PV ;

[0105] Where α is the total uncertainty, λ WPP and λ PV These are the weighting parameters corresponding to the first uncertainty and the second uncertainty, respectively;

[0106] Step S20414: Combine the fifth objective formula, the first randomness model, and the second randomness model to obtain the randomness model.

[0107] Furthermore, a robustness coefficient is introduced for optimization, and a worst-case target is set to ensure that the system target value is always higher than the worst-case expectation, thereby obtaining the maximum range of uncertainty.

[0108] Assuming the system uncertainty is α1 and the robustness deviation factor is β1, the overall system objective can be expressed as:

[0109]

[0110] Wherein, β1 is the robustness bias factor, the larger β1 is, the stronger the robustness and risk resistance of the system; F is the objective function, F0 is the optimization target benchmark value, which is the cost minimization, that is, the cost obtained by the model when both wind and solar data are historical data; (1+β1)F0 is the expected cost after introducing the robustness bias factor; The uncertainty of wind and light under risk-averse decision-making.

[0111] If, in actual operation, the actual output of wind and solar power is lower than historical data, that is... The system's scheduling cost will inevitably increase. To achieve the worst-case scenario under the robust model, minimizing the output of wind and solar power will maximize F, which can then be considered as maxF. Therefore, the overall system expression above can be rewritten as:

[0112]

[0113] Taking the robust uncertainty of wind power as its lower bound for fluctuation, we have:

[0114] Similarly, Substitute the incomplete rating expression into the above objective function to solve.

[0115] 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.

[0116] This application also provides a random scheduling device for a virtual power plant considering carbon emission reduction. It should be noted that this random scheduling device for a virtual power plant considering carbon emission reduction can be used to execute the random scheduling method for a virtual power plant considering carbon emission reduction 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 implements 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.

[0117] The following describes the random scheduling device for a virtual power plant that takes into account the reduction of carbon emissions, provided in the embodiments of this application.

[0118] Figure 3 This is a structural block diagram of a random scheduling device for a virtual power plant considering carbon emission reduction, according to an embodiment of this application. Figure 3 As shown, the device includes:

[0119] The first acquisition unit 10 is used to acquire historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0120] Specifically, this application considers scheduling based on multiple historical operating parameters and cost change parameters of the virtual power plant. It obtains at least the operating status of the virtual power plant's thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices as historical operating parameters, and the operating costs of the aforementioned components as cost change parameters.

[0121] The simulation unit 20 is used to simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters, obtain the corresponding component physical models, determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0122] Specifically, for the components of the virtual power plant, such as thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas equipment, simulations are performed based on historical operating parameters. The performance of each component under different working environments is calculated, and the energy flow relationship between electricity and carbon dioxide in the processes of power generation, transmission, and consumption among the components is analyzed and determined. Based on the energy flow relationship, the physical models of each component are established to obtain the first target model, which simulates the actual operation of the power plant.

[0123] The construction unit 30 is used to construct a numerical model based on the above-mentioned historical operating parameters to characterize the correlation between the cost change parameters of each of the above-mentioned components and the above-mentioned historical operating parameters, thereby obtaining a component cost model. At least based on the above-mentioned energy flow relationship, the cost models of each of the above-mentioned components are combined to obtain a second target model.

[0124] Specifically, a numerical model is constructed using historical operating parameters to measure the cost variation parameters as they change. This model is used to quantify the costs of each component in the virtual power plant under different operating conditions. Furthermore, a second objective model is constructed based on the energy flow relationships between the components in the virtual power plant, which is used to evaluate the overall cost-effectiveness of the virtual power plant under different scheduling strategies.

[0125] The calculation unit 40 is used to maximize the output parameters of the second objective model and minimize the output parameters of the first objective model as the objective function. Based on the robustness factor, the wind turbine and the thermal power unit are modeled through the envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and the preset constraint group, the objective function is solved to obtain the target operating parameters. The target operating parameters are used to generate target control commands to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0126] Specifically, considering the uncertainties in the actual operation of the virtual power plant, a robustness factor is introduced to enhance the model's resilience, ensuring that the virtual power plant can maintain stable performance in the face of fluctuations and uncertainties, under the premise of maximizing the output parameters of the second objective model and minimizing the output parameters of the first objective model. An envelope constraint model is used to construct a model that reflects the stochastic output of the power generation unit, which is used to simulate and predict the performance of wind turbines and thermal power units under various possible conditions. A preset constraint group is set to limit the parameter value range of the physical model of each component of the virtual power plant, ensuring that all components operate within a safe and feasible range. Under the above constraints, the objective function is solved to obtain the target operating parameters, and corresponding control commands are generated to precisely control the operating status of the thermal power units, wind turbines, photovoltaic units and other components of the virtual power plant.

[0127] In this embodiment, the first acquisition unit acquires historical operating parameters of the virtual power plant. These historical operating parameters include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters represent the operating parameters of different components of the virtual power plant. These components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas conversion devices. The cost variation parameters represent the operating costs of different components of the virtual power plant. The thermal power units supply the electrical load to the carbon capture devices and the virtual power plant, and the wind power units supply the electrical load to the power-to-gas conversion devices and the virtual power plant. The simulation unit simulates each component of the virtual power plant according to the historical operating parameters, obtaining corresponding component physical models. Based on the historical operating parameters, it determines the energy flow relationships between the components and, based on these energy flow relationships, analyzes each component... The physical models of the components are combined to obtain the first target model. The construction unit constructs a numerical model based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, thus obtaining the component cost model. The cost models of each component are combined at least based on the energy flow relationship to obtain the second target model. The calculation unit maximizes the output parameters of the second target model and minimizes the output parameters of the first target model as the objective function. Based on the robustness factor, the wind turbine and the thermal power unit are modeled through an envelope constraint model to obtain a stochastic model. The objective function is solved under the constraints of the stochastic model and a preset constraint group to obtain the target operating parameters. The target control command is generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of each component physical model. This application sets up simulation models for thermal power, wind power, and photovoltaic units in a virtual power plant. Based on this, it integrates the simulation of carbon dioxide generation from thermal power units through carbon capture devices and power-to-gas conversion devices in existing virtual power plants. Then, based on the energy flow between each unit and the carbon capture device and the power-to-gas conversion device, the models are linked together to construct a corresponding cost calculation model. Considering that zero emissions cannot be achieved, the solution is based on optimal economics and minimum carbon emissions to obtain the corresponding operating parameters for controlling the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

[0128] The aforementioned random scheduling device for a virtual power plant designed to reduce carbon emissions includes a processor and a memory. The first acquisition unit, simulation unit, construction unit, and calculation unit are all stored as program units in the memory, and the processor executes these program units 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.

[0129] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and communication efficiency can be improved by adjusting kernel parameters.

[0130] 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.

[0131] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the random scheduling method for a virtual power plant that takes into account carbon emission reduction.

[0132] Specifically, stochastic scheduling methods for virtual power plants that aim to reduce carbon emissions include:

[0133] Step S201: Obtain historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0134] Step S202: Simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters to obtain the corresponding component physical model, and determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0135] Step S203: Construct a numerical model based on the above historical operating parameters to characterize the correlation between the cost change parameters of each of the above components and the above historical operating parameters, thereby obtaining a component cost model. At least based on the above energy flow relationship, combine the cost models of each of the above components to obtain a second target model.

[0136] Step S204: Maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function. Based on the robustness factor, model the wind turbine and the thermal power unit through the envelope constraint model to obtain a stochastic model. Solve the objective function under the constraints of the stochastic model and the preset constraint group to obtain the target operating parameters. Generate target control commands based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0137] This invention provides a processor for running a program, wherein the program executes the aforementioned random scheduling method for virtual power plants that takes into account carbon emission reduction.

[0138] Specifically, stochastic scheduling methods for virtual power plants that aim to reduce carbon emissions include:

[0139] Step S201: Obtain historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0140] Step S202: Simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters to obtain the corresponding component physical model, and determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0141] Step S203: Construct a numerical model based on the above historical operating parameters to characterize the correlation between the cost change parameters of each of the above components and the above historical operating parameters, thereby obtaining a component cost model. At least based on the above energy flow relationship, combine the cost models of each of the above components to obtain a second target model.

[0142] Step S204: Maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function. Based on the robustness factor, model the wind turbine and the thermal power unit through the envelope constraint model to obtain a stochastic model. Solve the objective function under the constraints of the stochastic model and the preset constraint group to obtain the target operating parameters. Generate target control commands based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0143] This invention provides a virtual power plant control system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0144] Step S201: Obtain historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0145] Step S202: Simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters to obtain the corresponding component physical model, and determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0146] Step S203: Construct a numerical model based on the above historical operating parameters to characterize the correlation between the cost change parameters of each of the above components and the above historical operating parameters, thereby obtaining a component cost model. At least based on the above energy flow relationship, combine the cost models of each of the above components to obtain a second target model.

[0147] Step S204: Maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function. Based on the robustness factor, model the wind turbine and the thermal power unit through the envelope constraint model to obtain a stochastic model. Solve the objective function under the constraints of the stochastic model and the preset constraint group to obtain the target operating parameters. Generate target control commands based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0148] 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:

[0149] Step S201: Obtain historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost change parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant. The wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant.

[0150] Step S202: Simulate each of the above-mentioned components of the virtual power plant according to the above-mentioned historical operating parameters to obtain the corresponding component physical model, and determine the energy flow relationship between each of the above-mentioned components based on the above-mentioned historical operating parameters, and combine the above-mentioned component physical models based on the above-mentioned energy flow relationship to obtain the first target model.

[0151] Step S203: Construct a numerical model based on the above historical operating parameters to characterize the correlation between the cost change parameters of each of the above components and the above historical operating parameters, thereby obtaining a component cost model. At least based on the above energy flow relationship, combine the cost models of each of the above components to obtain a second target model.

[0152] Step S204: Maximize the output parameters of the second target model and minimize the output parameters of the first target model as the objective function. Based on the robustness factor, model the wind turbine and the thermal power unit through the envelope constraint model to obtain a stochastic model. Solve the objective function under the constraints of the stochastic model and the preset constraint group to obtain the target operating parameters. Generate target control commands based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each of the above components.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0163] 1) The stochastic scheduling method for a virtual power plant considering carbon emission reduction in this application firstly obtains historical operating parameters of the virtual power plant. These historical operating parameters include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. These components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas conversion devices. The cost variation parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant, and the wind power units are used to supply the electrical load of the power-to-gas conversion devices and the virtual power plant. Then, based on the historical operating parameters, simulations are performed on each of the aforementioned components of the virtual power plant to obtain corresponding component physical models. Based on the historical operating parameters, the energy flow relationships between the aforementioned components are determined. The flow relationship is used to simultaneously establish the physical models of each of the aforementioned components to obtain the first objective model. Then, based on the historical operating parameters, a numerical model is constructed to characterize the correlation between the cost change parameters of each of the aforementioned components and the historical operating parameters, resulting in a component cost model. At least based on the energy flow relationship, the cost models of each of the aforementioned components are simultaneously established to obtain the second objective model. Finally, the objective function is to maximize the output parameters of the second objective model and minimize the output parameters of the first objective model. Based on the robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and a preset constraint set, the objective function is solved to obtain the target operating parameters. Target control commands are generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint set is used to limit the parameter values ​​of each of the aforementioned component physical models. This application sets up simulation models for thermal power, wind power, and photovoltaic units in a virtual power plant. Based on this, it integrates the simulation of carbon dioxide generation from thermal power units through carbon capture devices and power-to-gas conversion devices in existing virtual power plants. Then, based on the energy flow between each unit and the carbon capture device and the power-to-gas conversion device, the models are linked together to construct a corresponding cost calculation model. Considering that zero emissions cannot be achieved, the solution is based on optimal economics and minimum carbon emissions to obtain the corresponding operating parameters for controlling the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

[0164] 2) The random dispatching device for a virtual power plant considering carbon emission reduction in this application includes a first acquisition unit acquiring historical operating parameters of the virtual power plant. These historical operating parameters include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters are operating parameters for different components of the virtual power plant. These components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas conversion devices. The cost variation parameters represent the operating costs of different components of the virtual power plant. The thermal power units supply the electrical load to the carbon capture devices and the virtual power plant, and the wind power units supply the electrical load to the power-to-gas conversion devices and the virtual power plant. The simulation unit simulates each component of the virtual power plant according to the historical operating parameters, obtaining corresponding component physical models. Based on the historical operating parameters, it determines the energy flow relationship between each component. The energy flow relationship is used to simultaneously establish the physical models of each of the aforementioned components to obtain the first target model. The construction unit constructs a numerical model based on the aforementioned historical operating parameters to characterize the correlation between the aforementioned cost change parameters of each of the aforementioned components and the aforementioned historical operating parameters, thus obtaining the component cost model. At least based on the aforementioned energy flow relationship, the cost models of each of the aforementioned components are simultaneously established to obtain the second target model. The calculation unit maximizes the output parameters of the aforementioned second target model and minimizes the output parameters of the aforementioned first target model as the objective function. Based on the robustness factor, the aforementioned wind turbine and the aforementioned thermal power unit are modeled through an envelope constraint model to obtain a stochastic model. Under the constraints of the aforementioned stochastic model and a preset constraint group, the aforementioned objective function is solved to obtain the target operating parameters. The target operating parameters are used to generate target control commands to control the operation of the aforementioned virtual power plant. The aforementioned preset constraint group is used to limit the parameter values ​​of each of the aforementioned component physical models. This application sets up simulation models for thermal power, wind power, and photovoltaic units in a virtual power plant. Based on this, it integrates the simulation of carbon dioxide generation from thermal power units through carbon capture devices and power-to-gas conversion devices in existing virtual power plants. Then, based on the energy flow between each unit and the carbon capture device and the power-to-gas conversion device, the models are linked together to construct a corresponding cost calculation model. Considering that zero emissions cannot be achieved, the solution is based on optimal economics and minimum carbon emissions to obtain the corresponding operating parameters for controlling the virtual power plant. This method solves the problem that existing virtual power plant scheduling schemes do not consider the impact of carbon circulation and cannot meet the needs of a low-carbon economy.

[0165] 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 stochastic scheduling method for virtual power plants considering carbon emission reduction, characterized in that, The method includes: Historical operating parameters of the virtual power plant are obtained. These historical operating parameters include multiple historical operating parameters and cost variation parameters. The multiple historical operating parameters are operating parameters of different components of the virtual power plant. The components include at least thermal power units, wind power units, photovoltaic units, carbon capture devices, and power-to-gas devices. The cost variation parameters are the operating costs of different components of the virtual power plant. The thermal power units are used to supply the electrical load of the carbon capture devices and the virtual power plant, and the wind power units are used to supply the electrical load of the power-to-gas devices and the virtual power plant. Simulations are performed on each component of the virtual power plant based on the historical operating parameters to obtain corresponding component physical models. The energy flow relationships between each component are determined based on the historical operating parameters. The physical models of each component are then combined based on the energy flow relationships to obtain the first target model. A numerical model is constructed based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, thereby obtaining a component cost model. At least based on the energy flow relationship, the component cost models are combined to obtain a second target model. The objective function is to maximize the output parameters of the second objective model and minimize the output parameters of the first objective model. Based on the robustness factor, the wind turbine and the thermal power unit are modeled through an envelope constraint model to obtain a stochastic model. The objective function is solved under the constraints of the stochastic model and a preset constraint group to obtain the target operating parameters. Target control commands are generated based on the target operating parameters to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each component.

2. The method according to claim 1, characterized in that, Based on the historical operating parameters, simulations are performed on each of the components of the virtual power plant to obtain the corresponding physical models of the components, including: The first power generation is obtained, and the operating state of the thermal power unit is expressed in the form of a binary variable. The operating state is used as the input parameter, and the second power generation is used as the output parameter. Based on the first power generation and the operating state, the physical model of the component corresponding to the thermal power unit is constructed to obtain the first physical model. The first power generation is the rated power generation of the thermal power unit, and the second power generation is the actual power generation of the thermal power unit. The system acquires the third power generation, first wind speed, second wind speed, third wind speed, and real-time wind speed. Using the fourth power generation as the output parameter and the real-time wind speed as the input parameter, it constructs a physical model of the component corresponding to the wind turbine based on the second power generation, first wind speed, second wind speed, third wind speed, and real-time wind speed, thus obtaining a second physical model. The third power generation is the rated power of the wind turbine, the first wind speed is the cut-in wind speed of the wind turbine, the second wind speed is the cut-out wind speed of the wind turbine, the third wind speed is the rated wind speed of the wind turbine, the first wind speed is less than the third wind speed, the third wind speed is less than the second wind speed, and the fourth power generation is the actual power generation of the wind turbine. The target solar radiation, target area, and first preset coefficient are obtained. The target solar radiation is used as the input parameter and the fifth power generation is used as the output parameter. The physical model of the component corresponding to the photovoltaic unit is constructed according to the target solar radiation, the target area, and the first preset coefficient to obtain the third physical model. The target solar radiation is the radiation intensity of the current solar radiation, and the fifth power generation is the actual power generation of the photovoltaic unit. Obtain the first power consumption and the first preset power consumption. Using the first power consumption as the input parameter and the first content as the output parameter, construct the physical model of the component corresponding to the carbon capture device based on the first power consumption and the first preset power consumption to obtain the fourth physical model. The first power consumption is the real-time operating power consumption of the carbon capture device, and the first content is the carbon dioxide content collected by the carbon capture device. Obtain the second power consumption and the second preset coefficient. Using the second power consumption as the input parameter and the second content as the output parameter, construct the component physical model corresponding to the electro-gas conversion device based on the second power consumption and the second preset coefficient to obtain the fifth physical model. The second power consumption is the real-time operating power consumption of the electro-gas conversion device, and the second content is the content of methane generated by the electro-gas conversion device.

3. The method according to claim 2, characterized in that, Based on the energy flow relationship, the physical models of each component are combined to obtain the first target model, which includes: Obtain the first target load and the third power consumption, set the second power generation power equal to the sum of the first target load, the third power consumption and the first power consumption, and obtain the first target formula. The first target load is the part of the total electrical load borne by the thermal power unit, and the third power consumption is the fixed power consumption of the carbon capture device. Obtain the second target load, set the fourth power generation to be the sum of the second target load and the second power consumption, and obtain the second target formula, where the second target load is the portion of the total load borne by the wind turbine; The first target model is obtained by combining the first physical model, the second physical model, the third physical model, the fourth physical model, the fifth physical model, the first target formula, and the second target formula.

4. The method according to claim 2, characterized in that, Obtaining target solar radiation includes: The first solar radiation and the second solar radiation are determined based on the historical operating parameters, wherein the first solar radiation is the mean value of solar radiation and the second solar radiation is the normal distribution value of solar radiation. Substituting the first solar radiation and the second solar radiation into the first target formula yields the first shape parameter. Substituting the first shape parameter and the first solar radiation into the second target formula yields the second shape parameter. Wherein, β is the first shape parameter, μ is the first solar radiation, δ is the second solar radiation, and α is the second shape parameter; Obtain the third preset coefficient, and substitute the third preset coefficient, the first solar radiation, and the second solar radiation into the third target formula to obtain the target solar radiation: Where P(θ) is the target solar radiation, θ is the third preset coefficient, and θ d θ is the upper limit of solar radiation. c As the lower limit of solar load, f(θ) follows a beta distribution:

5. The method according to claim 2, characterized in that, Based on the historical operating parameters, a numerical model is constructed to characterize the correlation between the cost variation parameters of each component and the historical operating parameters, thus obtaining a component cost model. At least based on the energy flow relationship, the component cost models are simultaneously established to obtain a second target model, including: Obtain the first cost, and construct the component cost model corresponding to the thermal power unit based on the first cost and the first physical model to obtain the first cost model, where the first cost is the start-up cost of the thermal power unit: Among them, D Th,t For the first cost, Q Th,t For the second power generation, a Th b Th and c Th Let u be the energy consumption coefficient of the thermal power unit. Th,t Let t represent the operating state of the thermal power unit. The fuel cost of the thermal power unit. r is the start-up and shutdown cost of the thermal power unit. Th The total revenue of the thermal power unit. This refers to the real-time unit electricity price; Obtain the second cost and the third cost. Based on the second cost, the third cost, the second physical model, and the third physical model, construct the component cost models corresponding to the wind turbine and the photovoltaic unit to obtain the second cost model. The second cost is the operating cost of the wind turbine, and the third cost is the operating cost of the photovoltaic unit. in, and Let be the total cost of the wind turbine and the photovoltaic unit at time t, respectively. and Let be the depreciation costs of the wind turbine and the photovoltaic unit at time t, respectively. and p represents the fourth power generation capacity and the fifth power generation capacity, respectively. WPP and p PV The depreciation cost per unit power of the wind turbine and the photovoltaic unit, respectively, r wpv r represents the total revenue of the wind turbine and the photovoltaic unit. WPP and r PV The electricity sales revenue of the wind turbine and the photovoltaic unit are respectively. Based on the fourth physical model, the component cost model corresponding to the carbon capture device is constructed, resulting in the third cost model: C CC =a(P t CC ) 2 +bP t CC +c; Among them, C CC P represents the total cost of the carbon capture device. t CC The first power consumption is denoted as a, b, and c, which are the operating cost coefficients of the carbon capture device. Based on the fifth physical model, the component cost model corresponding to the electro-gas conversion device is constructed, resulting in the fourth cost model: r t pg =Q pg,t *p gas ; Where, r t pg p represents the total revenue of the electro-gas conversion device. gas For natural gas prices, Q pg,t The second content at time t; A first emission level is obtained. A third target formula is constructed based on the second power generation, the first emission level, and the second emission level. The second emission level is set to be greater than or equal to the first emission level, and the first emission level is set to be greater than or equal to the second emission level, resulting in a fourth target formula. The first emission level is the carbon dioxide emission per unit power generation of the thermal power unit, and the second emission level is the total emission of the thermal power unit. And Th,t =Q Th，t ·And Th Among them, e Th,t For the second emission, e Th This is the first emission amount; By combining the third objective formula, the fourth objective formula, the first cost model, the second cost model, the third cost model, and the fourth cost model, the second objective model is obtained.

6. The method according to claim 5, characterized in that, The method further includes maximizing the output parameters of the second target model and minimizing the output parameters of the first target model to form a target function: Construct the first objective function; F1=maxπ VPP =R-C=(r Th +r wpv +r pg )-(C Th +C WPP +C PV +C CC ); Where, π VPP The total revenue of the virtual power plant; Construct the second objective function: in, For the second content, QCC t This refers to the first content.

7. The method according to claim 1, characterized in that, Based on the robustness factor, the wind turbine and the thermal power unit are modeled using an envelope constraint model to obtain a stochastic model, including: The wind turbine is modeled using the envelope constraint model to obtain the first stochastic model: Among them, P WPP This represents the actual power generation of the wind turbine. For the historical output data of wind turbines within the scheduling cycle, α WPP The uncertainty of the output of the wind turbine; The photovoltaic unit is modeled using the envelope constraint model to obtain the second stochastic model: Among them, P WPP This represents the actual power generation of the photovoltaic unit. For the historical output data of photovoltaic units within the scheduling cycle, α WPP The uncertainty of the output of the photovoltaic unit; Based on the first uncertainty, the second uncertainty, and the target uncertainty, a fifth target formula is constructed, where the first uncertainty is the uncertainty of the output of the wind turbine, the second uncertainty is the uncertainty of the output of the photovoltaic unit, and the target uncertainty is the total uncertainty of the virtual power plant. a = a WPP *l WPP +a PV *l PV ; Where α is the total uncertainty, λ WPP and λ PV These are the weighting parameters corresponding to the first uncertainty and the second uncertainty, respectively; By combining the fifth objective formula, the first stochastic model, and the second stochastic model, the stochastic model is obtained.

8. A random dispatching device for a virtual power plant considering carbon emission reduction, characterized in that, The virtual power plant includes a power generation module and a carbon conversion module. The power generation module includes thermal power units, wind power units, and photovoltaic units. The carbon conversion module includes the thermal power units, wind power units, a carbon capture device, and an electricity-to-gas conversion device. The thermal power units supply the electrical load to the carbon capture device and the virtual power plant. The wind power units supply the electrical load to the electricity-to-gas conversion device and the virtual power plant. The device includes: The first acquisition unit is used to acquire the historical operating parameters of the virtual power plant. The historical operating parameters include multiple historical operating parameters and cost change parameters. The multiple historical operating parameters are the operating parameters of different components of the virtual power plant. The components include at least the thermal power unit, the wind power unit, the photovoltaic unit, the carbon capture device, and the power-to-gas device. The cost change parameters are the operating costs of different components of the virtual power plant. The simulation unit is used to simulate each component of the virtual power plant according to the historical operating parameters to obtain the corresponding component physical model, and to determine the energy flow relationship between each component based on the historical operating parameters, and to combine the physical models of each component based on the energy flow relationship to obtain the first target model. The construction unit is used to construct a numerical model based on the historical operating parameters to characterize the correlation between the cost change parameters of each component and the historical operating parameters, thereby obtaining a component cost model. At least based on the energy flow relationship, the component cost models are combined to obtain a second target model. The calculation unit is used to maximize the output parameters of the second target model and minimize the output parameters of the first target model as an objective function. Based on the robustness factor, it models the wind turbine and the thermal power unit through an envelope constraint model to obtain a stochastic model. Under the constraints of the stochastic model and a preset constraint group, it solves the objective function to obtain target operating parameters. Based on the target operating parameters, it generates target control commands to control the operation of the virtual power plant. The preset constraint group is used to limit the parameter values ​​of the physical models of each component.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

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

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