Cost Optimization Method for Electro-Thermal Coupled Virtual Power Plant Based on Stochastic Optimization

Abstract

This application provides a cost optimization method for an electric-thermal coupled virtual power plant based on stochastic optimization. The method includes: obtaining historical operating parameters; constructing physical simulation models of each component based on the historical operating parameters to obtain a power plant operation model; constructing operating cost models of each component based on the historical operating parameters to obtain a power plant cost model; determining the uncertainty parameters of the virtual power plant based on the historical operating parameters and constructing a stochastic model; constructing a set of objective constraints based on the power plant operation model; obtaining the first and second uncertainties based on the system uncertainties using the IGDT method for risk avoidance and opportunity seeking; further solving for the power plant control parameters; and controlling the operation of the virtual power plant based on the power plant control parameters. This method addresses the problem in the prior art of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture, and electrical conversion equipment to meet low-cost and low-carbon requirements.

Description

Technical Field

[0001] This invention relates to the field of scheduling of thermal virtual power plants, and more specifically, to a cost optimization method, apparatus, computer-readable storage medium, processor, and planning system for an electric-thermal coupled virtual power plant based on stochastic optimization. Background Technology

[0002] Virtual power plants (VPPs), as aggregates of distributed resources, have enormous potential in improving the utilization rate of distributed renewable energy. With the continuous implementation of pilot projects in various regions in recent years, the forms of virtual power plants have gradually diversified, and their economic benefits have continued to expand.

[0003] As virtual power plants are implemented in practice, the combination of their distributed resources is constantly innovating. Beyond power source-type and hybrid virtual power plants, multi-energy resources are increasingly being integrated. Combined with cogeneration units, heat supply is also incorporated into the real-time scheduling of virtual power plants. Current virtual power plant scheduling schemes still rely on traditional virtual power plants, neglecting the uncertainties brought about by the introduction of new energy sources. Therefore, existing technologies lack scheduling methods that consider the energy conversion relationships in virtual power plants incorporating multiple energy sources. Summary of the Invention

[0004] The main objective of this application is to provide a cost optimization method, optimization device, computer-readable storage medium, processor, and planning system for an electro-thermal coupled virtual power plant based on stochastic optimization methods, so as to at least solve the problem in the prior art of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet the requirements of low cost and low carbon emissions.

[0005] To achieve the above objectives, according to one aspect of this application, a cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method is provided, comprising: acquiring historical operating parameters, wherein the historical operating parameters include input and output data of each component of the virtual power plant and the operating cost of each component, wherein the components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device, wherein the CHP unit receives natural gas and outputs electrical and thermal energy, the PV unit receives solar energy and outputs electrical energy, the WPP unit receives wind energy and outputs electrical energy, the carbon capture device receives electrical energy from the CHP unit to collect carbon dioxide output by the CHP unit and outputs it to the electrical conversion device, and the electrical conversion device receives electrical energy from the WPP unit to convert the carbon dioxide of the carbon capture device into methane; constructing a physical simulation model of each component based on the historical operating parameters to obtain a power plant operating model, wherein the power plant operating model is used to simulate the changing trends of the output data and energy flow of each component with the input data; and constructing an operating cost model of each component based on the historical operating parameters. This model yields a power plant cost model, which simulates the changing trend of the operating costs of each component with input data. Based on historical operating parameters, uncertainty parameters for the output load of the PV unit, WPP unit, and virtual power plant are determined, resulting in an uncertainty factor set. A stochastic model is constructed based on this uncertainty factor set to simulate the changing trend of system uncertainty with the uncertainty factor set. A target constraint set is constructed based on the power plant operation model, limiting the parameter values ​​of the power plant operation model. This target constraint set includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints. Based on the system uncertainty, risk avoidance and opportunity seeking solutions are performed using the IGDT method, yielding a first uncertainty and a second uncertainty. Based on the first and second uncertainties, the power plant operation model is solved with the goal of minimizing the output of the operating cost model to obtain power plant control parameters. The virtual power plant is then controlled according to these power plant control parameters.

[0006] Optionally, a physical simulation model of each component is constructed based on the historical operating parameters to obtain a power plant operation model, including: determining the power generation, heat production, and gas input power of the CHP unit based on the historical operating parameters; determining the power conversion efficiency based on the power generation and gas input power; determining the heat conversion efficiency based on the heat production and gas input power; constructing a power output formula based on the power generation, gas input power, and power conversion efficiency; constructing a heat output formula based on the heat production, gas input power, and heat conversion efficiency; and constructing a power output formula based on the CHP unit. The power output formula and heat output formula of the unit are used to construct a physical simulation model of the CHP unit, resulting in a first simulation model. Based on the historical operating parameters, the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the WPP unit are determined. Based on the correlation between the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the WPP unit, a physical simulation model of the WPP unit is constructed, resulting in a second simulation model. Based on the historical operating parameters, the power generation, photovoltaic panel area, average efficiency, and irradiance of the PV unit are determined. Based on the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the WPP unit, a physical simulation model of the WPP unit is constructed, resulting in a second simulation model. The solar absorptivity and solar conversion efficiency are determined by the photovoltaic panel area, the average efficiency, and the irradiance. A power output formula is constructed using the power generation capacity, photovoltaic panel area, average efficiency, irradiance, solar absorptivity, and solar conversion efficiency. A physical simulation model of the PV unit is then built based on this formula, resulting in a third simulation model. The fixed energy consumption, operating energy consumption, and total energy consumption of the carbon capture device are determined based on historical operating parameters. An energy consumption expression for the carbon capture device is then constructed based on these parameters. A physical simulation model of the carbon capture device is constructed using the energy consumption expression of the collection device, resulting in a fourth simulation model. The total energy consumption, fixed energy consumption, and operating energy consumption of the electrical conversion device are determined based on the historical operating parameters. An energy consumption expression for the electrical conversion device is then constructed based on these values. A physical simulation model of the electrical conversion device is then constructed based on this energy consumption expression, resulting in a fifth simulation model. Finally, a power plant operation model is constructed based on the first, second, third, fourth, and fifth simulation models.

[0007] Optionally, before constructing the power plant operation model based on the first simulation model, the second simulation model, the third simulation model, the fourth simulation model, and the fifth simulation model, the method further includes: constructing a first target formula based on the power generation of the CHP unit and the total energy consumption of the electrical conversion device, the first target formula being used to simulate the flow of electrical energy output by the CHP unit; determining the demand response power based on the historical operating parameters; and constructing a load transfer formula based on the power generation of the CHP unit, the power generation of the WPP unit, the power generation of the PV unit, and the demand response power to obtain a second target formula, the second target formula being used to simulate the trend of load transfer of the virtual power plant with the change of power generation at any given time.

[0008] Optionally, an operating cost model for each component is constructed based on the historical operating parameters to obtain a power plant cost model, including: determining the fuel cost and power generation of the CHP unit based on the historical operating parameters; fitting the fuel cost and power generation to obtain a third objective formula, which is used to simulate the trend of fuel cost variation with power generation; determining the start-up and shutdown costs and a first variable of the CHP unit based on the historical operating parameters; fitting the start-up and shutdown costs and the first variable to obtain a fourth objective formula, which is used to simulate the trend of start-up and shutdown costs variation with the first variable, where the first variable is a binary variable indicating whether the CHP unit is started; combining the third objective formula and the fourth objective formula to obtain the operating cost model of the CHP unit; determining the operating cost, depreciation cost, and power generation of the WPP unit based on the historical operating parameters; constructing an operating cost model of the WPP unit based on the operating cost, depreciation cost, and power generation of the WPP unit, which is used to simulate the trend of the total cost of the WPP unit variation with power generation; and based on the historical operating parameters... The parameters determine the operating cost, depreciation cost, and power generation of the PV unit. Based on these parameters, an operating cost model for the PV unit is constructed to simulate the trend of the total cost of the PV unit changing with power generation. The energy consumption of the carbon capture device is determined based on the historical operating parameters. Based on this energy consumption, an operating cost model for the carbon capture device is constructed to simulate the trend of the total cost of the carbon capture device changing with energy consumption. The load of the virtual power plant is then considered. The load transfer volume is used to construct a demand response cost model, which simulates the trend of demand response cost changes with the load transfer volume; the curtailment cost model of the virtual power plant is determined based on the historical operating parameters, which simulates the trend of curtailment penalties of the virtual power plant with the curtailment costs; the operating cost model is constructed based on the operating cost model of the CHP unit, the operating cost model of the WPP unit, the operating cost model of the PV unit, the operating cost model of the carbon capture device, and the demand response cost model.

[0009] Optionally, determining the uncertainty parameters of the output load of the PV unit, the WPP unit, and the virtual power plant based on the historical operating parameters includes: determining the fluctuation range of the WPP unit based on the historical operating parameters; determining the uncertainty parameters of the WPP unit through an envelope constraint model based on the fluctuation range and the historical operating parameters to obtain a first uncertainty parameter; determining the fluctuation range of the PV unit based on the historical operating parameters; determining the uncertainty parameters of the PV unit through an envelope constraint model based on the fluctuation range and the historical operating parameters to obtain a second uncertainty parameter; determining the fluctuation range of the output load of the virtual power plant based on the historical operating parameters; determining the uncertainty parameters of the output load of the virtual power plant through an envelope constraint model based on the fluctuation range and the historical operating parameters to obtain a third uncertainty parameter; constructing the uncertainty factor group based on the first uncertainty parameter, the second uncertainty parameter, and the third uncertainty parameter; and constructing a stochastic model based on the uncertainty factor group.

[0010] Optionally, constructing a target constraint group based on the power plant operation model includes: constructing the system balance constraints of the virtual power plant corresponding to the power plant operation model to obtain a first target constraint; constructing the CHP unit constraints of the virtual power plant corresponding to the power plant operation model to obtain a second target constraint; constructing the WPP unit constraints of the virtual power plant corresponding to the power plant operation model to obtain a third target constraint; constructing the PV unit constraints of the virtual power plant corresponding to the power plant operation model to obtain a fourth target constraint; constructing the reserve capacity constraints of the virtual power plant corresponding to the power plant operation model to obtain a fifth target constraint; constructing the carbon capture device constraints of the virtual power plant corresponding to the power plant operation model to obtain a sixth target constraint; and combining the first target constraint, the second target constraint, the third target constraint, the fourth target constraint, the fifth target constraint, and the sixth target constraint to obtain the target constraint group.

[0011] Optionally, based on the system uncertainty, risk aversion and opportunity seeking are solved using the IGDT method to obtain the first uncertainty and the second uncertainty, including: obtaining a preset robustness coefficient; transforming the objective function based on the system uncertainty and the preset robustness coefficient to obtain a risk aversion formula set; substituting the group of uncertain factors into the risk aversion formula set to obtain the first uncertainty; obtaining the minimum value of the system uncertainty and transforming the objective function to obtain an opportunity seeking formula set; substituting the group of uncertain factors into the opportunity seeking formula set to obtain the second uncertainty.

[0012] According to another aspect of this application, a cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method is provided. The device includes: an acquisition unit for acquiring historical operating parameters, which include input and output data of each component of the virtual power plant and the operating cost of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electrical and thermal energy; the PV unit receives solar energy and outputs electrical energy; the WPP unit receives wind energy and outputs electrical energy; and the carbon capture device… The system receives electrical energy from the CHP unit to collect carbon dioxide output from the CHP unit and outputs it to the electrical conversion device. The electrical conversion device receives electrical energy from the WPP unit to convert the carbon dioxide from the carbon capture device into methane. A first construction unit is used to construct physical simulation models of each component based on the historical operating parameters, resulting in a power plant operation model. This power plant operation model simulates the changing trends of output data and energy flow of each component with input data. A second construction unit is used to construct operating cost models of each component based on the historical operating parameters. A power plant cost model is obtained, which is used to simulate the changing trend of the operating costs of each component with input data; a third construction unit is used to determine the uncertainty parameters of the output load of the PV unit, the WPP unit, and the virtual power plant based on the historical operating parameters, to obtain an uncertainty factor group, and to construct a stochastic model based on the uncertainty factor group, which is used to simulate the changing trend of system uncertainty with the uncertainty factor group; a fourth construction unit is used to construct a target constraint group based on the power plant operation model, which is used to limit the parameter values ​​of the power plant operation model, and the target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints; a calculation unit is used to solve for risk avoidance and opportunity seeking based on the system uncertainty using the IGDT method, to obtain the first uncertainty and the second uncertainty, and to solve the power plant operation model based on the first uncertainty and the second uncertainty, with the minimization of the output of the operating cost model as the objective function to obtain the power plant control parameters, and to control the operation of the virtual power plant according to the power plant control parameters.

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

[0014] According to another aspect of this application, a virtual power plant planning 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.

[0015] By applying the technical solution of this application, cost modeling is performed based on physical modeling. Then, under constraints, the objective function is to minimize the cost. The negative and positive expectations of the objective function are solved separately according to IGDT theory and finally fused to determine the scheduling parameters of the virtual power plant and control the virtual power plant. This method solves the problem in the prior art of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet the requirements of low cost and low carbon. Attached Figure Description

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for a cost optimization method of an electro-thermal coupled virtual power plant based on a stochastic optimization method provided in an embodiment of this application is shown.

[0017] Figure 2 A flowchart illustrating a cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method, according to an embodiment of this application, is shown.

[0018] Figure 3 A structural block diagram of a cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method, according to an embodiment of this application, is shown.

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

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

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

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] As described in the background section, existing virtual power plant scheduling schemes are still based on traditional virtual power plants, ignoring the uncertainties brought about by the introduction of new energy sources. Therefore, existing technologies lack scheduling methods that consider the energy conversion relationships in virtual power plants that integrate multiple energy sources. To address the problem of the lack of a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet low-cost and low-carbon requirements, embodiments of this application provide a cost optimization method, optimization device, computer-readable storage medium, processor, and optimization system for an electric-thermal coupled virtual power plant based on a stochastic optimization method.

[0025] 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.

[0026] 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 cost optimization method of an electro-thermal coupled virtual power plant based on a stochastic optimization method, 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.

[0027] 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.

[0028] This embodiment provides a cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0029] Figure 2 This is a flowchart illustrating a cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method, according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Obtain historical operating parameters. The historical operating parameters include the input and output data of each component of the virtual power plant and the operating cost of each component. The components include CHP units, WPP units, PV units, carbon capture devices, and electrical conversion devices. The CHP units receive natural gas and output electrical and thermal energy. The PV units receive solar energy and output electrical energy. The WPP units receive wind energy and output electrical energy. The carbon capture devices receive electrical energy from the CHP units to collect carbon dioxide output by the CHP units and output it to the electrical conversion devices. The electrical conversion devices receive electrical energy from the WPP units to convert the carbon dioxide from the carbon capture devices into methane.

[0031] Specifically, the internal structure of the virtual power plant is first defined. In the embodiments of this application, the virtual power plant includes wind turbine units (WPP units), photovoltaic units (PV units), and combined heat and power (CHP units). Electricity is generated based on these generator units and supplied to the main power grid. Furthermore, this application also includes an electric-assisted carbon capture device to absorb carbon dioxide and supply it to the power conversion unit (P2G) for catalytic methane production. Step S202 involves constructing physical simulation models of each of the aforementioned components based on the historical operating parameters, resulting in a power plant operation model. This power plant operation model is used to simulate the changing trends of the output data and energy flow of each of the aforementioned components with the input data.

[0032] Specifically, based on the above structure, the energy flow between the various components of the virtual power plant is determined. The virtual power plant system mainly includes electrical energy flow, heat energy flow, carbon energy flow, and natural gas flow. The heat energy flow is mainly generated by the CHP unit and supplies the heat load. The natural gas flow and carbon energy flow can be combined, including generation and consumption. Consumption is the generation of electricity by the CHP unit, while generation is the carbon dioxide absorbed by the carbon capture equipment entering the electrical conversion equipment and being converted into methane, which is supplied for consumption and sold to the natural gas network. Based on the above energy flow, the above power plant operation model is obtained by associating the various components through energy flow.

[0033] Step S203: Based on the above historical operating parameters, construct the operating cost model of each of the above components to obtain the power plant cost model. The operating cost model is used to simulate the changing trend of the operating cost of each of the above components with the input data.

[0034] Specifically, based on the operating parameters of the power plant operation model mentioned above, the operating costs of the generator set, the carbon capture device, and the electrical conversion device are determined respectively, and operating cost models are constructed respectively to obtain the power plant cost model mentioned above.

[0035] Step S204: Determine the uncertainty parameters of the output load of the PV unit, the WPP unit, and the virtual power plant based on the above historical operating parameters to obtain the uncertainty factor group. Construct a stochastic model based on the uncertainty factor group. The stochastic model is used to simulate the changing trend of system uncertainty with the uncertainty factor group.

[0036] Specifically, the uncertainty parameters of the load are simulated, and the above model is optimized by combining information gap decision theory.

[0037] Step S205: Construct a target constraint group based on the above power plant operation model. The target constraint group is used to limit the parameter values ​​of the above power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints.

[0038] Specifically, to ensure the normal operation of the virtual power plant, the input and / or output parameters of the physical simulation models of each of the above-mentioned components are set to restrict and construct the above-mentioned target constraint group.

[0039] Step S206: Based on the above system uncertainties, risk avoidance and opportunity seeking are solved using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the above operating cost model as the objective function to obtain the power plant control parameters. The operation of the virtual power plant is controlled according to the above power plant control parameters.

[0040] Specifically, based on the IGDT method, the power plant operation model is solved with the objective function of minimizing the output of the above operating cost model under the consideration of uncertainty, so as to obtain the power plant control parameters, and the operation of the above virtual power plant is controlled according to the above power plant control parameters.

[0041] In this embodiment, firstly, historical operating parameters are obtained. These parameters include the input and output data of each component of the virtual power plant, and the operating costs of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat; the PV unit receives solar energy and outputs electricity; the WPP unit receives wind energy and outputs electricity; the carbon capture device receives electricity from the CHP unit to collect carbon dioxide output from the CHP unit and outputs it to the electrical conversion device; the electrical conversion device receives electricity from the WPP unit to convert the carbon dioxide from the carbon capture device into methane. Then, a physical simulation model of each component is constructed based on the historical operating parameters to obtain a power plant operating model. This model simulates the changing trends of the output data and energy flow of each component with the input data. Finally, an operating cost model is constructed based on the historical operating parameters to obtain a power plant cost model. This cost model is used to simulate the changing trends of the output data and energy flow of each component with the input data. The operating costs of each of the aforementioned components are simulated to reflect the changing trends of input data. Then, based on the historical operating parameters, the uncertainty parameters of the output load of the PV unit, WPP unit, and virtual power plant are determined, resulting in a set of uncertainties. A stochastic model is constructed based on this set of uncertainties to simulate the changing trends of system uncertainty with the set of uncertainties. Next, a set of target constraints is constructed based on the power plant operation model to limit the parameter values ​​of the model. This set of target constraints includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints. Finally, based on the system uncertainties, risk avoidance and opportunity seeking solutions are performed using the IGDT method to obtain the first and second uncertainties. Based on the first and second uncertainties, the power plant operation model is solved with the goal of minimizing the output of the operating cost model to obtain power plant control parameters. The virtual power plant is then controlled according to these power plant control parameters. This application performs cost modeling based on physical modeling, and then, under constraints, uses minimizing cost as the objective function. Based on IGDT theory, it solves for the negative and positive expectations of the objective function, and finally merges them to determine the scheduling parameters of the virtual power plant and control the virtual power plant. This method solves the problem in the existing technology of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet the requirements of low cost and low carbon emissions.

[0042] In order to obtain a power plant operation model, in one optional implementation, step S202 above includes:

[0043] Step S2021: Determine the power generation, heat production, and gas input power of the CHP unit based on the historical operating parameters mentioned above; determine the power conversion efficiency based on the power generation and gas input power; determine the heat conversion efficiency based on the heat production and gas input power; construct the power output formula based on the power generation, gas input power, and power conversion efficiency; construct the heat output formula based on the heat production, gas input power, and heat conversion efficiency; and construct the physical simulation model of the CHP unit based on the power output formula and heat output formula of the CHP unit to obtain the first simulation model.

[0044] Specifically, the above-mentioned first and fourth simulation models are as follows:

[0045]

[0046] in, and These represent the power generation and heat production of the aforementioned CHP unit at time t, respectively. Let η be the gas input power of the aforementioned CHP unit at time t. e,CHP and η h,CHP These refer to the power and heat conversion efficiencies of the aforementioned CHP units. The natural gas input power for the CHP unit at time t. and P represents the lower and upper limits of the natural gas input power for the CHP unit. CHP,down and P CHP,up These represent the lower and upper limits of the ramp-up power of the aforementioned CHP units, respectively. and These are the lower and upper limits of the electricity-to-heat ratio for the aforementioned CHP units, respectively.

[0047] The distribution of electricity generated by combined heat and power (CHP) is as follows:

[0048]

[0049] This indicates that the electrical power generated by the CHP unit at time t is supplied to the electrical load; This indicates that the electricity generated by the CHP unit at time t is supplied to the carbon capture equipment.

[0050] Step S2022: Determine the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of the WPP unit based on the above historical operating parameters. Construct a physical simulation model of the WPP unit based on the correlation between the above power generation, real-time wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of the WPP unit to obtain the second simulation model.

[0051] Specifically, the second simulation model mentioned above is:

[0052]

[0053] in, Let v be the power generation of the aforementioned WPP unit at time t. t For the above real-time wind speed, v in For the aforementioned cut-in wind speed, v out For the cut-out wind speed mentioned above, v ra For the above rated wind speed, This refers to the rated power of the fan in the aforementioned WPP unit.

[0054] Step S2023: Based on the historical operating parameters, determine the power generation, photovoltaic panel area, average efficiency, and irradiance of the PV unit. Based on the power generation, photovoltaic panel area, average efficiency, and irradiance, determine the solar absorptivity and solar conversion efficiency. Based on the power generation, photovoltaic panel area, average efficiency, irradiance, solar absorptivity, and solar conversion efficiency, construct a power output formula. Based on the power output formula of the PV unit, construct a physical simulation model of the PV unit to obtain the third simulation model.

[0055] Specifically, the third simulation model mentioned above is:

[0056]

[0057] in, Let S be the power generation capacity of the aforementioned PV unit at time t. PV The area of ​​the photovoltaic panels mentioned above; Let η be the light intensity at time t; PV The above average efficiency; μ inv and μ sor These represent the conversion rate and absorption rate of the aforementioned solar energy, respectively; loss represents the photovoltaic power generation loss.

[0058] Step S2024: Determine the fixed energy consumption, operating energy consumption and total energy consumption of the carbon capture device based on the historical operating parameters. Construct the energy consumption expression of the carbon capture device based on the fixed energy consumption, operating energy consumption and total energy consumption of the carbon capture device. Construct the physical simulation model of the carbon capture device based on the energy consumption expression of the carbon capture device to obtain the fourth simulation model.

[0059] Specifically, the fourth simulation model mentioned above is:

[0060]

[0061] in, This represents the total energy consumption of the aforementioned carbon capture device. The fixed energy consumption of the aforementioned carbon capture device, This refers to the operating energy consumption of the aforementioned carbon capture device.

[0062] Because the energy consumption of the carbon capture equipment is provided by the CHP unit, therefore...

[0063] Step S2025: Determine the total energy consumption, fixed energy consumption, and operating energy consumption of the electrical conversion device based on the historical operating parameters. Construct an energy consumption expression for the electrical conversion device based on the fixed energy consumption, operating energy consumption, and total energy consumption. Construct a physical simulation model of the electrical conversion device based on the energy consumption expression, and obtain the fifth simulation model.

[0064] Specifically, the fifth simulation model mentioned above is: Q pg,t =P pg,t η pg Q pg,t P represents the volume of gas generated through electro-gas conversion. pg,t This refers to the electricity consumed in the electricity-to-gas conversion process. η pg It represents electrical conversion efficiency.

[0065] Step S2026: Construct the power plant operation model based on the first simulation model, the second simulation model, the third simulation model, the fourth simulation model, and the fifth simulation model.

[0066] In order to obtain the second objective formula, in an optional implementation, before constructing the power plant operation model based on the first simulation model, the second simulation model, the third simulation model, the fourth simulation model, and the fifth simulation model, the method further includes:

[0067] Step S301: Construct a first target formula based on the power generation of the CHP unit and the total energy consumption of the electrical conversion device. The first target formula is used to simulate the flow of electrical energy output by the CHP unit.

[0068] Specifically, the formula for the first objective mentioned above is:

[0069] Q pg,t =P pg,t η pg ;

[0070] Among them, Q pg,t The amount of gas generated by the aforementioned electrical conversion device. P pg,t η represents the total energy consumption of the aforementioned electrical conversion device. pg It represents electrical conversion efficiency.

[0071] Step S302: Determine the demand response power based on the above historical operating parameters, and construct a load transfer formula based on the power generation of the above CHP unit, the power generation of the above WPP unit, the power generation of the above PV unit and the above demand response power to obtain the second target formula. The second target formula is used to simulate the load transfer amount of the above virtual power plant at any time with the change trend of power generation.

[0072] Specifically, the formula for the second objective mentioned above is:

[0073]

[0074]

[0075] in, Let n be the load transfer amount of the nth load at time t. Let P be the initial load transfer amount of the nth load at time t, M be the price elasticity of demand matrix, and P be the initial load transfer amount of the nth load at time t. j (t) represents the power of unit j at time t. Let be the initial power of unit j at time t. The load transfer in / out coefficient is a 0-1 variable. This indicates that the load has been transferred. This indicates that load transfer-in and load transfer-out cannot occur simultaneously. This represents the maximum load transfer amount.

[0076] In order to obtain the power plant cost model, in one optional implementation, step S203 above includes:

[0077] Step S2031: Determine the fuel cost and power generation of the CHP unit based on the historical operating parameters. Based on the fitting of the fuel cost and power generation, obtain the third objective formula. The third objective formula is used to simulate the trend of the fuel cost changing with the power generation.

[0078] Specifically, the formula for the third objective mentioned above is:

[0079]

[0080] in, For the fuel cost of the aforementioned CHP units, a CHP ,b CHP and c CHP Let Q be the energy consumption coefficient of the aforementioned CHP unit. CHP,t This refers to the power generation capacity of the aforementioned CHP units.

[0081] Step S2032: Determine the start-up and shutdown costs and the first variable of the CHP unit based on the above historical operating parameters. Fit the start-up and shutdown costs and the first variable to obtain the fourth objective formula. The fourth objective formula is used to simulate the change trend of the start-up and shutdown costs with the first variable. The first variable is a binary variable indicating whether the CHP unit is started.

[0082] Specifically, the formula for the fourth objective mentioned above is:

[0083]

[0084] in, For the start-up and shutdown costs of the aforementioned CHP units, u CHP,t The above-mentioned CHP unit's power generation state variable is a 0-1 variable, where 1 indicates the CHP unit is in operation and 0 indicates the CHP unit is in shutdown. CHP,t This refers to the startup cost of the aforementioned CHP units.

[0085] Step S2033: Combine the above third objective formula and the above fourth objective formula to obtain the above CHP unit operating cost model;

[0086] Specifically, the operating cost model for the above-mentioned CHP units is as follows:

[0087]

[0088] Among them, C CHP The operating cost of the aforementioned CHP units.

[0089] Step S2034: Determine the operating cost, depreciation cost, and power generation of the WPP unit based on the above historical operating parameters; construct the operating cost model of the WPP unit based on the operating cost, depreciation cost, and power generation of the WPP unit; the operating cost model of the WPP unit is used to simulate the trend of the total cost of the WPP unit changing with the power generation.

[0090] Specifically, the operating cost model for the aforementioned WPP units is as follows:

[0091]

[0092] Among them, C WPP The operating costs of the aforementioned WPP units, The above WPP unit's operating cost at time t is... The depreciation cost of the aforementioned WPP units, Let p be the power generation of the aforementioned WPP unit at time t. WPP For WPP units, this is the conversion factor.

[0093] Step S2035: Determine the operating cost, depreciation cost, and power generation of the PV unit based on the above historical operating parameters. Construct an operating cost model for the PV unit based on the operating cost, depreciation cost, and power generation of the PV unit. The operating cost model of the PV unit is used to simulate the trend of the total cost of the PV unit changing with the power generation.

[0094] Specifically, the operating cost model for the above PV units is as follows:

[0095]

[0096] Among them, C PV The operating cost of the above PV units, Let be the operating cost of the aforementioned PV unit at time t. The depreciation cost of the aforementioned PV units, Let p be the power generation of the PV unit at time t. PV This represents the conversion factor for the PV unit.

[0097] Step S2036: Determine the energy consumption of the carbon capture device based on the historical operating parameters, and construct an operating cost model for the carbon capture device based on the energy consumption. The operating cost model is used to simulate the trend of the total cost of the carbon capture device changing with energy consumption.

[0098] Specifically, the operating cost model for the aforementioned carbon capture device is as follows:

[0099]

[0100] Among them, C CC The total cost of the aforementioned carbon capture device, Let be the energy consumption of the carbon capture device at time t, and let a, b, and c be the operating cost coefficients of the carbon capture device.

[0101] Step S2037: Based on the load transfer amount of the virtual power plant, construct a demand response cost model. The demand response cost model is used to simulate the changing trend of the demand response cost with the load transfer amount.

[0102] Specifically, the demand response cost model described above is as follows:

[0103]

[0104] in, Let these be the electrical load transfer cost and the heat load transfer cost for the nth load, respectively. and These are the subsidy prices for transferring electrical load and transferring heat load, respectively.

[0105] Step S2038: Determine the curtailment cost model of the virtual power plant based on the above historical operating parameters. The curtailment cost model is used to simulate the trend of curtailment penalties of the virtual power plant with the changing costs of curtailment.

[0106] Specifically, the cost model for curtailment of solar and wind power in the aforementioned virtual power plant is as follows:

[0107]

[0108] Where, ζ wpp ζ is the WPP conversion factor. pv For PV conversion factor, For the abandoned wind power, For the amount of light energy wasted, This refers to the actual amount of wind power generated and fed into the grid. This refers to the actual amount of photoelectric power generated when connected to the network.

[0109] Step S2039: Construct the above-mentioned operating cost model based on the above-mentioned operating cost model of CHP unit, the above-mentioned operating cost model of WPP unit, the above-mentioned operating cost model of PV unit, the above-mentioned operating cost model of carbon capture device, and the demand response cost model:

[0110] minC VPP =min(C CHP +C WPP +C PV +C CC +C DR +C cur );

[0111] Among them, C VPP This represents the total cost of the virtual power plant.

[0112] In order to determine the uncertain parameters, in one optional implementation, step S204 above includes:

[0113] Step S2041: Determine the fluctuation range of the WPP unit based on the above historical operating parameters, and determine the uncertainty parameters of the WPP unit through the envelope constraint model according to the fluctuation range and the above historical operating parameters to obtain the first uncertainty parameter;

[0114] Specifically, the aforementioned first uncertainty parameter:

[0115]

[0116] Among them, P WPP This represents the actual power generation of the aforementioned WPP units. For the historical data of the aforementioned WPP units within the scheduling cycle, α WPPThe fluctuation range of the aforementioned WPP units is a dimensionless value, reflecting the maximum deviation of the aforementioned WPP units from historical data.

[0117] Step S2042: Determine the fluctuation range of the PV unit based on the above historical operating parameters, and determine the uncertainty parameters of the PV unit through the envelope constraint model according to the fluctuation range and the above historical operating parameters to obtain the second uncertainty parameter;

[0118] Specifically, the second uncertainty parameter mentioned above is:

[0119]

[0120] Among them, P PV This represents the actual power generation of the aforementioned PV units; This refers to the historical data of the aforementioned PV units within the scheduling cycle; α PV The fluctuation range of the above PV units is a dimensionless value, reflecting the maximum deviation of the above PV units from historical data.

[0121] Step S2043: Based on the above historical operating parameters, determine the fluctuation range of the output load of the above virtual power plant, and determine the uncertainty parameter of the output load of the above virtual power plant through the envelope constraint model according to the fluctuation range and the above historical operating parameters, so as to obtain the third uncertainty parameter.

[0122] Specifically, the third uncertainty parameter mentioned above is:

[0123]

[0124] Among them, P L This refers to the output load of the aforementioned virtual power plant; This refers to the historical data of the output load of the aforementioned virtual power plant during the scheduling period; α L The range of fluctuation in the output load of the aforementioned virtual power plant is a dimensionless value, reflecting the maximum deviation of the output load from historical data.

[0125] Step S2044: Construct the uncertainty factor group based on the first uncertainty parameter, the second uncertainty parameter, and the third uncertainty parameter, and construct a stochastic model based on the uncertainty factor group.

[0126] Specifically, the above stochastic model is as follows:

[0127] α=α WPP *λ WPP +α PV *λ PV +α L *λ L ;

[0128] Where α represents the overall uncertainty level of the system; λ WPP , λ PV and λ L The weights for the aforementioned WPP units, PV units, and output load uncertainties are respectively determined by the objective weighting method.

[0129] In an optional implementation, to construct the target constraint set, step S205 above includes:

[0130] Step S2051: Construct the system balance constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the first objective constraint:

[0131]

[0132] Among them, Q CHP For the active power output of the aforementioned CHP units, Q j,WPP For the active power output of the aforementioned WPP unit j, Q m,pv The active power output of the PV unit m is given by D, where D represents the system load demand.

[0133] Step S2052: Construct the CHP unit constraints of the virtual power plant corresponding to the above power plant operation model to obtain the second objective constraint:

[0134]

[0135] in, This represents the maximum dispatchable output of the aforementioned CHP units; This is the minimum dispatchable output of the aforementioned CHP units;

[0136] Step S2053: Construct the WPP unit constraints of the virtual power plant corresponding to the above power plant operation model to obtain the third objective constraint:

[0137]

[0138] in, This represents the maximum dispatchable output of the aforementioned WPP unit j. This represents the minimum dispatchable output of the aforementioned WPP unit j;

[0139] Step 2054: Construct the PV unit constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the fourth objective constraint:

[0140]

[0141] in, This represents the maximum dispatchable output of the aforementioned PV unit m; This represents the minimum dispatchable output of the aforementioned PV unit m;

[0142] Step S2055: Construct the reserve capacity constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the fifth objective constraint:

[0143]

[0144] Where D(t) is the load demand of the virtual power plant system at time t, R(t) is the reserve demand of the virtual power plant system at time t, l is the line loss rate of the virtual power plant system, θ is the self-consumption rate of the virtual power plant units, and Q max (t) represents the maximum unit output of the virtual power plant unit at time t;

[0145] Step S2056: Construct the carbon capture device constraints of the virtual power plant corresponding to the above power plant operation model, and obtain the sixth objective constraint:

[0146]

[0147] Among them, P CC,min and P CC,max These are the minimum and maximum output limits for the aforementioned carbon capture device. The ramp rate constraint for the aforementioned carbon capture device;

[0148] Step S2057: Combine the above-mentioned first objective constraint, second objective constraint, third objective constraint, fourth objective constraint, fifth objective constraint and sixth objective constraint to obtain the above-mentioned objective constraint group;

[0149] To obtain uncertainty, in an optional implementation, step S206 above includes:

[0150] Step S2061: Obtain the preset robustness coefficient. Based on the above system uncertainty and the above preset robustness coefficient, transform the above objective function to obtain the risk avoidance formula set.

[0151] Specifically, the above risk aversion formula set is as follows:

[0152]

[0153] Wherein, β1 is the aforementioned preset robustness coefficient; the larger β1 is, the stronger the system's robustness and risk resistance. F is the objective function of the deterministic model, i.e., the aforementioned operating cost model. F0 is the optimization target baseline value, i.e., the cost obtained by the model solution when the WPP unit, PV unit, and load are all historical data. (1+β1)F0 is the expected cost of the model under risk-averse decision-making. and This refers to the uncertainties of the aforementioned WPP units, PV units, and loads under risk-averse decision-making.

[0154] Step S2062: Substitute the above group of uncertain factors into the above group of risk avoidance formulas to obtain the above first uncertainty;

[0155] Specifically, if the actual processing capacity of wind power and photovoltaic power is lower than historical data during actual operation, that is... The system's scheduling cost increases. In order to achieve the worst-case scenario under the robustness model, the output of wind power and photovoltaic power should be minimized and the load fluctuation should be maximized. At this time, the cost of the objective function takes the maximum value. The above risk avoidance formula set is transformed as follows. Then, the above first uncertainty parameter, the above second uncertainty parameter, and the above third uncertainty parameter are solved according to the following formula. They are substituted into the above stochastic model to obtain the above first uncertainty.

[0156]

[0157] Step S2063: Based on the above system uncertainty, the objective function is transformed to obtain the opportunity to seek a set of formulas;

[0158] Specifically, the above opportunities seek a set of formulas:

[0159]

[0160] Where α2 represents the systemic uncertainty of the opportunity-seeking model, β2 is the opportunity bias factor, and (1-β2)F0 is the expected cost under this scenario. and The uncertainty of the above WPP units, PV units and load under opportunity-seeking decision-making;

[0161] Step S2064: Substitute the above set of uncertain factors into the above set of opportunity-seeking formulas to obtain the above second uncertainty;

[0162] Specifically, when the fluctuation range of wind power and photovoltaic units is maximized and the upward fluctuation of load is minimized, the solution is performed under the condition of minimum uncertainty. The above opportunities are transformed into a set of formulas as follows. Then, the above first uncertainty parameter, the above second uncertainty parameter, and the above third uncertainty parameter are solved according to the following formula. Substitute them into the above stochastic model to obtain the above second uncertainty.

[0163]

[0164] In the above embodiments, the power plant operation model is further constrained based on the first uncertainty and the second uncertainty mentioned above:

[0165]

[0166] Specifically, under the above constraints, uncertainty is introduced into the above power plant operation model, and then the power plant control parameters are obtained by solving the above power plant operation model with the minimization of the output of the above operation cost model as the objective function. The operation of the above virtual power plant is controlled according to the above power plant control parameters.

[0167] 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.

[0168] This application also provides a cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method. It should be noted that this cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method can be used to execute the cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0169] The following describes the cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method provided in the embodiments of this application.

[0170] Figure 3 This is a structural block diagram of a cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method, according to an embodiment of this application. Figure 3 As shown, the device includes:

[0171] The acquisition unit 10 is used to acquire historical operating parameters, including input and output data of each component of the virtual power plant and operating costs of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat. The PV unit receives solar energy and outputs electricity. The WPP unit receives wind energy and outputs electricity. The carbon capture device receives electricity from the CHP unit to collect carbon dioxide output by the CHP unit and outputs it to the electrical conversion device. The electrical conversion device receives electricity from the WPP unit to convert carbon dioxide from the carbon capture device into methane.

[0172] The first building unit 20 is used to build physical simulation models of each of the above-mentioned components based on the above-mentioned historical operating parameters, so as to obtain a power plant operation model. The power plant operation model is used to simulate the changing trends of the output data and energy flow of each of the above-mentioned components with the input data.

[0173] The second construction unit 30 is used to construct the operating cost model of each of the above-mentioned components based on the above-mentioned historical operating parameters, so as to obtain the power plant cost model. The above-mentioned operating cost model is used to simulate the changing trend of the operating cost of each of the above-mentioned components with the input data.

[0174] The third construction unit 40 is used to determine the uncertainty parameters of the output load of the PV unit, the WPP unit and the virtual power plant based on the above historical operating parameters, to obtain the uncertainty factor group, and to construct a stochastic model based on the above uncertainty factor group. The stochastic model is used to simulate the changing trend of system uncertainty with the above uncertainty factor group.

[0175] The fourth construction unit 50 is used to construct a target constraint group based on the above power plant operation model. The target constraint group is used to limit the parameter values ​​of the above power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints.

[0176] The calculation unit 60 is used to solve for risk avoidance and opportunity seeking based on the above system uncertainty using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the above operating cost model as the objective function to obtain the power plant control parameters. The operation of the virtual power plant is controlled according to the above power plant control parameters.

[0177] In this embodiment, the acquisition unit acquires historical operating parameters, including input and output data of each component of the virtual power plant and the operating costs of each component. These components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat; the PV unit receives solar energy and outputs electricity; the WPP unit receives wind energy and outputs electricity; the carbon capture device receives electricity from the CHP unit to collect carbon dioxide output from the CHP unit and outputs it to the electrical conversion device; and the electrical conversion device receives electricity from the WPP unit to convert the carbon dioxide from the carbon capture device into methane. The first construction unit constructs physical simulation models of each component based on the historical operating parameters, obtaining a power plant operation model. This power plant operation model simulates the changing trends of output data and energy flow of each component with input data. The second construction unit constructs operating cost models of each component based on the historical operating parameters, obtaining a power plant cost model. This operating cost model is used to simulate the changing trends of output data and energy flow of each component with input data. The simulation unit simulates the changing trend of the operating costs of each of the aforementioned components with the input data; the third construction unit determines the uncertainty parameters of the output load of the aforementioned PV unit, WPP unit, and virtual power plant based on the aforementioned historical operating parameters, obtaining an uncertainty factor group, and constructs a stochastic model based on the aforementioned uncertainty factor group, which is used to simulate the changing trend of system uncertainty with the aforementioned uncertainty factor group; the fourth construction unit constructs a target constraint group based on the aforementioned power plant operation model, which is used to limit the parameter values ​​of the aforementioned power plant operation model, and the aforementioned target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints; the calculation unit performs risk avoidance and opportunity seeking solutions respectively based on the aforementioned system uncertainty using the IGDT method, obtaining the first uncertainty and the second uncertainty, and solves the aforementioned power plant operation model based on the aforementioned first uncertainty and the aforementioned second uncertainty, with the minimization of the output of the aforementioned operating cost model as the objective function to obtain the power plant control parameters, and controls the operation of the aforementioned virtual power plant according to the aforementioned power plant control parameters. This application performs cost modeling based on physical modeling, and then, under constraints, uses minimizing cost as the objective function. Based on IGDT theory, it solves for the negative and positive expectations of the objective function, and finally merges them to determine the scheduling parameters of the virtual power plant and control the virtual power plant. This method solves the problem in the existing technology of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet the requirements of low cost and low carbon emissions.

[0178] The aforementioned cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method includes a processor and a memory. The acquisition unit, first construction unit, second construction unit, third construction unit, fourth construction unit, and calculation unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0179] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the cost optimization efficiency of the virtual power plant.

[0180] 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.

[0181] 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 execute the cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method.

[0182] Specifically, cost optimization methods for electro-thermal coupled virtual power plants based on stochastic optimization methods include:

[0183] Step S201: Obtain historical operating parameters. The historical operating parameters include the input and output data of each component of the virtual power plant and the operating cost of each component. The components include CHP units, WPP units, PV units, carbon capture devices, and electrical conversion devices. The CHP units receive natural gas and output electrical and thermal energy. The PV units receive solar energy and output electrical energy. The WPP units receive wind energy and output electrical energy. The carbon capture devices receive electrical energy from the CHP units to collect carbon dioxide output by the CHP units and output it to the electrical conversion devices. The electrical conversion devices receive electrical energy from the WPP units to convert the carbon dioxide in the carbon capture devices into methane.

[0184] Step S202: Based on the above historical operating parameters, construct physical simulation models of each of the above components to obtain the power plant operation model. The power plant operation model is used to simulate the changing trends of the output data and energy flow of each of the above components with the input data.

[0185] Step S203: Based on the above historical operating parameters, construct the operating cost model of each of the above components to obtain the power plant cost model. The above operating cost model is used to simulate the changing trend of the operating cost of each of the above components with the input data.

[0186] Step S204: Determine the uncertainty parameters of the output load of the PV unit, the WPP unit and the virtual power plant based on the above historical operating parameters to obtain the uncertainty factor group. Construct a stochastic model based on the uncertainty factor group. The stochastic model is used to simulate the changing trend of system uncertainty with the uncertainty factor group.

[0187] Step S205: Construct a target constraint group based on the above power plant operation model. The target constraint group is used to limit the parameter values ​​of the above power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints.

[0188] Step S206: Based on the above system uncertainties, risk avoidance and opportunity seeking are solved using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the above operating cost model as the objective function to obtain the power plant control parameters. The operation of the virtual power plant is controlled according to the above power plant control parameters.

[0189] This invention provides a processor for running a program, wherein the program executes the cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method.

[0190] This invention provides a virtual power plant planning system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the following steps of a cost optimization method for an electro-thermal coupled virtual power plant based on a stochastic optimization method:

[0191] Step S201: Obtain historical operating parameters. The historical operating parameters include the input and output data of each component of the virtual power plant and the operating cost of each component. The components include CHP units, WPP units, PV units, carbon capture devices, and electrical conversion devices. The CHP units receive natural gas and output electrical and thermal energy. The PV units receive solar energy and output electrical energy. The WPP units receive wind energy and output electrical energy. The carbon capture devices receive electrical energy from the CHP units to collect carbon dioxide output by the CHP units and output it to the electrical conversion devices. The electrical conversion devices receive electrical energy from the WPP units to convert the carbon dioxide in the carbon capture devices into methane.

[0192] Step S202: Based on the above historical operating parameters, construct physical simulation models of each of the above components to obtain the power plant operation model. The power plant operation model is used to simulate the changing trends of the output data and energy flow of each of the above components with the input data.

[0193] Step S203: Based on the above historical operating parameters, construct the operating cost model of each of the above components to obtain the power plant cost model. The above operating cost model is used to simulate the changing trend of the operating cost of each of the above components with the input data.

[0194] Step S204: Determine the uncertainty parameters of the output load of the PV unit, the WPP unit and the virtual power plant based on the above historical operating parameters to obtain the uncertainty factor group. Construct a stochastic model based on the uncertainty factor group. The stochastic model is used to simulate the changing trend of system uncertainty with the uncertainty factor group.

[0195] Step S205: Construct a target constraint group based on the above power plant operation model. The target constraint group is used to limit the parameter values ​​of the above power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints.

[0196] Step S206: Based on the above system uncertainties, risk avoidance and opportunity seeking are solved using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the above operating cost model as the objective function to obtain the power plant control parameters. The operation of the virtual power plant is controlled according to the above power plant control parameters.

[0197] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a cost optimization method step of at least the above-described electro-thermal coupled virtual power plant based on a stochastic optimization method.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0208] This application presents a cost optimization method for an electro-thermal coupled virtual power plant based on stochastic optimization. First, historical operating parameters are obtained. These parameters include the input and output data of each component of the virtual power plant, and the operating costs of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat; the PV unit receives solar energy and outputs electricity; the WPP unit receives wind energy and outputs electricity; the carbon capture device receives electricity from the CHP unit to collect carbon dioxide output from the CHP unit and outputs it to the electrical conversion device; the electrical conversion device receives electricity from the WPP unit to convert the carbon dioxide from the carbon capture device into methane. Next, a physical simulation model of each component is constructed based on the historical operating parameters to obtain a power plant operating model. This operating model simulates the changing trends of the output data and energy flow of each component with the input data. Finally, an operating cost model of each component is constructed based on the historical operating parameters to obtain a power plant cost model. The aforementioned operating cost model is used to simulate the changing trend of the operating costs of each of the aforementioned components with input data. Then, based on the aforementioned historical operating parameters, the uncertainty parameters of the output load of the aforementioned PV units, WPP units, and virtual power plants are determined, resulting in an uncertainty factor set. A stochastic model is constructed based on this uncertainty factor set to simulate the changing trend of system uncertainty with the aforementioned uncertainty factor set. Next, a target constraint set is constructed based on the aforementioned power plant operating model to limit the parameter values ​​of the aforementioned power plant operating model. This target constraint set includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints. Finally, based on the aforementioned system uncertainties, risk avoidance and opportunity seeking solutions are performed using the IGDT method, respectively, to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operating model is solved with the goal of minimizing the output of the aforementioned operating cost model to obtain the power plant control parameters. The virtual power plant is then controlled according to these power plant control parameters. This application performs cost modeling based on physical modeling, and then, under constraints, uses minimizing cost as the objective function. Based on IGDT theory, it solves for the negative and positive expectations of the objective function, and finally merges them to determine the scheduling parameters of the virtual power plant and control the virtual power plant. This method solves the problem in the existing technology of lacking a virtual power plant scheduling optimization method that integrates cogeneration units, carbon capture and electrical conversion equipment to meet the requirements of low cost and low carbon emissions.

[0209] 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 cost optimization method for an electro-thermal coupled virtual power plant based on stochastic optimization, characterized in that, The method includes: Historical operating parameters are obtained, including input and output data of each component of the virtual power plant and the operating cost of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat. The PV unit receives solar energy and outputs electricity. The WPP unit receives wind energy and outputs electricity. The carbon capture device receives electricity from the CHP unit to collect carbon dioxide output by the CHP unit and outputs it to the electrical conversion device. The electrical conversion device receives electricity from the WPP unit to convert the carbon dioxide in the carbon capture device into methane. Based on the historical operating parameters, a physical simulation model of each component is constructed to obtain the power plant operation model. The power plant operation model is used to simulate the changing trends of the output data and energy flow of each component with the input data. Based on the historical operating parameters, an operating cost model for each of the components is constructed to obtain a power plant cost model. The operating cost model is used to simulate the changing trend of the operating cost of each of the components with the input data. Based on the historical operating parameters, the uncertainty parameters of the output load of the PV unit, the WPP unit, and the virtual power plant are determined to obtain the uncertainty factor group. Based on the uncertainty factor group, a stochastic model is constructed. The stochastic model is used to simulate the changing trend of system uncertainty with the uncertainty factor group. Based on the power plant operation model, a target constraint group is constructed. The target constraint group is used to limit the parameter values ​​of the power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints. Based on the system uncertainty, risk avoidance and opportunity seeking are solved using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the operating cost model as the objective function to obtain the power plant control parameters. The virtual power plant is then controlled according to the power plant control parameters.

2. The method according to claim 1, characterized in that, Based on the historical operating parameters, physical simulation models of each component are constructed to obtain the power plant operation model, including: The power generation, heat production, and gas input power of the CHP unit are determined based on the historical operating parameters. The power conversion efficiency is determined based on the power generation and gas input power, and the heat conversion efficiency is determined based on the heat production and gas input power. A power output formula is constructed based on the power generation, gas input power, and power conversion efficiency. A heat output formula is constructed based on the heat production, gas input power, and heat conversion efficiency. A physical simulation model of the CHP unit is constructed based on the power output formula and heat output formula of the CHP unit, resulting in the first simulation model. Based on the historical operating parameters, the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the WPP unit are determined. Based on the correlation between the power generation, real-time wind speed, cut-in wind speed, cut-out wind speed, and rated wind speed of the WPP unit, a physical simulation model of the WPP unit is constructed to obtain the second simulation model. The power generation, photovoltaic panel area, average efficiency, and irradiance of the PV unit are determined based on the historical operating parameters. The solar absorptivity and solar conversion efficiency are determined based on the power generation, photovoltaic panel area, average efficiency, and irradiance. A power output formula is constructed based on the power generation, photovoltaic panel area, average efficiency, irradiance, solar absorptivity, and solar conversion efficiency. A physical simulation model of the PV unit is then constructed based on the power output formula of the PV unit to obtain a third simulation model. The fixed energy consumption, operating energy consumption, and total energy consumption of the carbon capture device are determined based on the historical operating parameters. An energy consumption expression for the carbon capture device is constructed based on the fixed energy consumption, operating energy consumption, and total energy consumption of the carbon capture device. A physical simulation model of the carbon capture device is constructed based on the energy consumption expression of the carbon capture device, resulting in a fourth simulation model. The total energy consumption, fixed energy consumption, and operating energy consumption of the electrical conversion device are determined based on the historical operating parameters. An energy consumption expression for the electrical conversion device is constructed based on the fixed energy consumption, operating energy consumption, and total energy consumption. A physical simulation model of the electrical conversion device is constructed based on the energy consumption expression, resulting in the fifth simulation model. The power plant operation model is constructed based on the first simulation model, the second simulation model, the third simulation model, the fourth simulation model, and the fifth simulation model.

3. The method according to claim 2, characterized in that, Before constructing the power plant operation model based on the first simulation model, the second simulation model, the third simulation model, the fourth simulation model, and the fifth simulation model, the method further includes: A first target formula is constructed based on the power generation capacity of the CHP unit and the total energy consumption of the electrical conversion device. The first target formula is used to simulate the flow of electrical energy output by the CHP unit. The demand response power is determined based on the historical operating parameters. A load transfer formula is constructed based on the power generation of the CHP unit, the power generation of the WPP unit, the power generation of the PV unit, and the demand response power, resulting in a second target formula. The second target formula is used to simulate the trend of load transfer of the virtual power plant with the change of power generation at any time.

4. The method according to claim 3, characterized in that, Based on the historical operating parameters, an operating cost model for each of the constituent components is constructed to obtain the power plant cost model, including: The fuel cost and power generation of the CHP unit are determined based on the historical operating parameters. A third objective formula is obtained by fitting the fuel cost and the power generation. The third objective formula is used to simulate the trend of the fuel cost changing with the power generation. The start-up and shutdown costs and the first variable of the CHP unit are determined based on the historical operating parameters. The start-up and shutdown costs and the first variable are fitted to obtain a fourth objective formula. The fourth objective formula is used to simulate the change trend of the start-up and shutdown costs with the first variable. The first variable is a binary variable of whether the CHP unit is started. By combining the third objective formula and the fourth objective formula, the operating cost model of the CHP unit is obtained; The operating cost, depreciation cost, and power generation of the WPP unit are determined based on the historical operating parameters. An operating cost model for the WPP unit is constructed based on the operating cost, depreciation cost, and power generation of the WPP unit. The operating cost model of the WPP unit is used to simulate the trend of the total cost of the WPP unit changing with the power generation. The operating cost, depreciation cost, and power generation of the PV unit are determined based on the historical operating parameters. An operating cost model for the PV unit is constructed based on the operating cost, depreciation cost, and power generation of the PV unit. The operating cost model of the PV unit is used to simulate the trend of the total cost of the PV unit changing with the power generation. The energy consumption of the carbon capture device is determined based on the historical operating parameters. An operating cost model of the carbon capture device is constructed based on the energy consumption of the carbon capture device. The operating cost model of the carbon capture device is used to simulate the trend of the total cost of the carbon capture device changing with energy consumption. Based on the load transfer volume of the virtual power plant, a demand response cost model is constructed. The demand response cost model is used to simulate the changing trend of the demand response cost with the load transfer volume. The curtailment cost model of the virtual power plant is determined based on the historical operating parameters. The curtailment cost model is used to simulate the trend of curtailment penalties of the virtual power plant with the changing costs of curtailment. The operating cost model is constructed based on the operating cost model of the CHP unit, the operating cost model of the WPP unit, the operating cost model of the PV unit, the operating cost model of the carbon capture device, and the demand response cost model.

5. The method according to claim 1, characterized in that, The uncertainty parameters for the output load of the PV unit, the WPP unit, and the virtual power plant are determined based on the historical operating parameters, including: Based on the historical operating parameters, the fluctuation range of the WPP unit is determined. Based on the fluctuation range and the historical operating parameters, the uncertainty parameters of the WPP unit are determined through an envelope constraint model to obtain the first uncertainty parameter. Based on the historical operating parameters, the fluctuation range of the PV unit is determined. Based on the fluctuation range and the historical operating parameters, the uncertainty parameters of the PV unit are determined through an envelope constraint model to obtain the second uncertainty parameter. Based on the historical operating parameters, the fluctuation range of the output load of the virtual power plant is determined. Based on the fluctuation range and the historical operating parameters, the uncertainty parameter of the output load of the virtual power plant is determined through the envelope constraint model, and a third uncertainty parameter is obtained. The uncertainty factor group is constructed based on the first uncertainty parameter, the second uncertainty parameter, and the third uncertainty parameter, and a stochastic model is constructed based on the uncertainty factor group.

6. The method according to claim 1, characterized in that, Based on the power plant operation model, a set of target constraints is constructed, including: Construct the system balance constraints of the virtual power plant corresponding to the power plant operation model to obtain the first objective constraint; Construct the CHP unit constraints of the virtual power plant corresponding to the power plant operation model to obtain the second objective constraint; Construct the WPP unit constraints of the virtual power plant corresponding to the power plant operation model to obtain the third objective constraint; The constraints of the PV units of the virtual power plant corresponding to the power plant operation model are constructed to obtain the fourth objective constraint; Construct the reserve capacity constraint of the virtual power plant corresponding to the power plant operation model to obtain the fifth objective constraint; The carbon capture device constraints of the virtual power plant corresponding to the power plant operation model are constructed to obtain the sixth objective constraint; The first objective constraint, the second objective constraint, the third objective constraint, the fourth objective constraint, the fifth objective constraint, and the sixth objective constraint are combined to obtain the objective constraint group.

7. The method according to claim 1, characterized in that, Based on the aforementioned system uncertainties, risk aversion and opportunity seeking are solved using the IGDT method, respectively, yielding the first uncertainty and the second uncertainty, including: Obtain a preset robustness coefficient, and based on the system uncertainty and the preset robustness coefficient, transform the objective function to obtain a set of risk avoidance formulas; Substituting the group of uncertain factors into the group of risk aversion formulas yields the first uncertainty. Based on minimizing the system uncertainty, the objective function is transformed to obtain an opportunity to seek a set of formulas; Substituting the set of uncertainties into the set of opportunity-seeking formulas yields the second uncertainty.

8. A cost optimization device for an electro-thermal coupled virtual power plant based on a stochastic optimization method, characterized in that, The device includes: An acquisition unit is used to acquire historical operating parameters, including input and output data of each component of the virtual power plant and the operating cost of each component. The components include a CHP unit, a WPP unit, a PV unit, a carbon capture device, and an electrical conversion device. The CHP unit receives natural gas and outputs electricity and heat. The PV unit receives solar energy and outputs electricity. The WPP unit receives wind energy and outputs electricity. The carbon capture device receives electricity from the CHP unit to collect carbon dioxide output by the CHP unit and outputs it to the electrical conversion device. The electrical conversion device receives electricity from the WPP unit to convert the carbon dioxide of the carbon capture device into methane. The first construction unit is used to construct a physical simulation model of each of the components based on the historical operating parameters to obtain a power plant operation model. The power plant operation model is used to simulate the changing trends of the output data and energy flow of each of the components with the input data. The second construction unit is used to construct an operating cost model for each of the components based on the historical operating parameters, thereby obtaining a power plant cost model. The operating cost model is used to simulate the changing trend of the operating cost of each component with the input data. The third construction unit is used to determine the uncertainty parameters of the output load of the PV unit, the WPP unit and the virtual power plant based on the historical operating parameters, obtain the uncertainty factor group, and construct a stochastic model based on the uncertainty factor group. The stochastic model is used to simulate the changing trend of system uncertainty with the uncertainty factor group. The fourth construction unit is used to construct a target constraint group based on the power plant operation model. The target constraint group is used to limit the parameter values ​​of the power plant operation model. The target constraint group includes at least system balance constraints, CHP unit constraints, WPP unit constraints, PV unit constraints, reserve capacity constraints, and carbon capture device constraints. The calculation unit is used to solve for risk avoidance and opportunity seeking based on the system uncertainty using the IGDT method to obtain the first uncertainty and the second uncertainty. Based on the first uncertainty and the second uncertainty, the power plant operation model is solved with minimizing the output of the operating cost model as the objective function to obtain the power plant control parameters. The virtual power plant is then controlled to operate according to the power plant control parameters.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, 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 planning 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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