Virtual power plant optimal scheduling system using modified finite difference method

Abstract

This invention belongs to the field of power grid optimization dispatching, specifically involving a virtual power plant optimization dispatching system using the modified finite difference method. It constructs a virtual power plant benefit optimization model and solves it using a two-stage step-by-step approach. In the first stage, the minimum carbon compliance cost of the virtual power plant is not considered, and the output power of the power supply units in the virtual power plant benefit optimization model is calculated. In the second stage, the modified finite difference method is used to solve for the optimal carbon emission allowance purchase strategy and the minimum carbon emission cost of the virtual power plant. The system specifically includes a virtual power plant cost optimization modeling module, a virtual power plant power generation solution module, an optimal carbon emission allowance purchase strategy module, an optimal dispatching scheme module, and a dispatching instruction sending module. This achieves virtual power plant cost optimization, reducing the overall carbon emission cost of the power system while ensuring that the virtual power plant meets power demand and carbon emission control targets.

Description

Technical Field

[0001] This invention belongs to the field of power optimization dispatching, specifically relating to a virtual power plant optimization dispatching system using the modified finite difference method. Background Technology

[0002] With the continuous growth of global energy demand and the transformation of the energy structure, virtual power plants, as an energy system that integrates multiple distributed energy resources and has autonomous operation and dispatch capabilities, not only require the optimization of their internal load dispatch to achieve efficient energy utilization and system operation optimization, but also require optimizing the energy dispatch of each virtual power plant and reducing the overall carbon emission cost while ensuring that electricity demand and total carbon emission control targets are met. Summary of the Invention

[0003] The purpose of this invention is to address the above-mentioned problems by providing a virtual power plant optimal scheduling system using the modified finite difference method. This system constructs a cost optimization model for the virtual power plant and employs a two-stage solution method. The first stage calculates the daily power generation of the virtual power plant, and the second stage calculates the optimal carbon emission quota purchase strategy and the minimum carbon emission cost for the virtual power plant. This ensures that the virtual power plant reduces the overall carbon emission cost of the power system while meeting electricity demand and carbon emission control targets.

[0004] To achieve the above objectives, the technical solution provided by this invention is as follows: The virtual power plant optimization dispatch system employing the modified finite difference method divides the power system into a generation module, a user module, a grid module, and a heating network module. The generation module includes multiple virtual power plants of different types, each containing power supply units and a carbon emission control module. The power supply units include renewable energy power supply units and non-renewable energy power supply units. The carbon emission control module is used to predict and calculate the overall carbon emission demand of the virtual power plants, analyze carbon emission data, and identify carbon emission trends.

[0005] The virtual power plant optimization scheduling system includes the following modules: The virtual power plant cost optimization modeling module is used to build a virtual power plant benefit optimization model; The virtual power plant power generation calculation module is used to calculate the daily power generation of the virtual power plant based on the virtual power plant benefit optimization model. The optimal carbon emission allowance purchase strategy module is used to calculate the optimal carbon emission allowance purchase amount and the minimum carbon compliance cost that the virtual power plant needs to execute at each carbon trading moment, based on the virtual power plant benefit optimization model and the daily power generation output of the virtual power plant power generation solution module. The optimal scheduling scheme module obtains the optimal scheduling scheme for various virtual power plants based on the daily power generation output by the virtual power plant power generation solution module, the optimal carbon emission quota purchase amount output by the optimal carbon emission quota purchase strategy module, and the lowest carbon compliance cost. The dispatch instruction sending module sends dispatch instructions to the operators of the power supply units of each virtual power plant according to the optimal dispatch scheme output by the optimal dispatch scheme module.

[0006] The above-mentioned method for optimizing the scheduling of virtual power plants includes the following steps: Step 1: Determine the total carbon emission control target for the power system, consider carbon emission costs, and construct a virtual power plant benefit optimization model; Step 2: Without considering the minimum cost of carbon compliance for the virtual power plant, solve the optimization model for the efficiency of the virtual power plant to obtain the output power of each power supply unit of the virtual power plant; Step 3: Based on the sum of the output power of the power supply units obtained in Step 2, calculate the incremental carbon emission limit demand of the virtual power plant; Step 4: Based on the historical carbon emission information of the virtual power plant and the current load situation, predict the overall carbon emission demand of the virtual power plant; Step 5: Based on the incremental carbon emission allowance demand of the virtual power plant obtained in Step 3 and the total carbon emission demand of the virtual power plant obtained in Step 4, use the modified finite difference method to solve for the optimal carbon emission allowance purchase strategy and the minimum cost of carbon compliance at each time point. Step 6: Combine the optimal carbon emission limit purchase strategy and the minimum cost of carbon compliance obtained in Step 5 with the output power of each power supply unit obtained in Step 2 to obtain the optimal solution of the virtual power plant benefit optimization model, i.e., the optimal dispatch scheme. Step 7: Based on the optimal solution of the virtual power plant benefit optimization model obtained in Step 6, guide the operation and scheduling of the power system.

[0007] Compared with the prior art, the beneficial effects of the present invention include: 1) This invention integrates dispersed energy resources, such as renewable and non-renewable energy, into a virtual centralized energy system through a virtual power plant, and utilizes the intelligent computing unit and information transmission unit of the virtual power plant to achieve... Electricity demand response and optimized operation control of power generation and supply, while ensuring the control targets for total electricity demand and carbon emissions, achieve the optimal cost control of virtual power plants.

[0008] 2) This invention constructs a cost optimization model for a virtual power plant and adopts a two-stage solution method. The first stage solves for the daily power generation of the virtual power plant, and the second stage solves for the optimal carbon emission quota purchase strategy and the minimum carbon emission cost of the virtual power plant, which can effectively reduce the overall carbon emission cost of the power system.

[0009] 3) This invention leverages the advantages of the modified finite difference method (MFD), including its wide applicability, ability to handle nonlinear problems, simple and intuitive computation process, and high flexibility, to solve for the optimal carbon emission allowance purchase strategy and the minimum cost of carbon compliance at various time points. As a numerical method for solving partial differential equations, the MFD is applicable to various types of equations, including complex equations describing virtual power plant operation and carbon emission allowance purchase strategies. The MFD allows for more precise handling of these problems, making it suitable for solving virtual power plant benefit optimization models. Virtual power plant operation and carbon emission management often involve nonlinear problems such as fluctuations in power generation and dynamic changes in carbon emission allowances. The MFD can handle these nonlinear problems, ensuring the accuracy and reliability of the solution results. The MFD discretizes the original problem into a difference scheme by dividing the continuous solution domain into a difference grid and replacing the continuous solution domain with a finite number of grid nodes. This method is simple and intuitive to calculate, easy to implement and understand, and convenient for promotion and application in practical applications. The modified finite difference method can adapt to the adaptive mesh method and dynamically adjust the mesh or difference scheme according to the characteristics of the solution. Compared with the traditional finite difference method, it can better handle boundary conditions. Attached Figure Description

[0010] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0011] Figure 1 This is a schematic diagram of a power system according to an embodiment of the present invention.

[0012] Figure 2 This is a distribution chart of the optimal carbon emission allowance purchase volume for a Class 1 virtual power plant in one year, as shown in this embodiment of the invention. Detailed Implementation

[0013] like Figure 1As shown, in this embodiment, the power system includes a power generation module, a user module, a power grid module, and a heating network module. The power grid module and the heating network module exchange electricity and heat with the power generation module, respectively. The power generation module includes four types of virtual power plants: Class 1, Class 2, Class 3, and Class 4. Each virtual power plant includes a power supply unit and a carbon emission control module. The carbon emission control module predicts the overall carbon emission demand of the virtual power plant based on a long short-term memory network, analyzes carbon emission data, and identifies carbon emission trends and main sources. The power supply unit includes renewable energy supply units, non-renewable energy supply units, a calculation unit, and an information transmission unit. The calculation unit is used to calculate the control parameters of the power supply unit and the operating cost and carbon emission cost of the virtual power plant. The information transmission unit is used to transmit the calculation results data from the calculation unit. The user module is an electricity consumption unit that sends a demand response to the power generation module. The above modules are connected by transmission lines to transmit electrical energy, and they are also interconnected to transmit information.

[0014] The virtual power plant optimization scheduling system using the modified finite difference method in this embodiment includes: The virtual power plant cost optimization modeling module is used to build a virtual power plant benefit optimization model; The virtual power plant power generation calculation module is used to calculate the daily power generation of the virtual power plant based on the virtual power plant benefit optimization model. The optimal carbon emission allowance purchase strategy module is used to calculate the optimal carbon emission allowance purchase amount and the minimum carbon compliance cost that the virtual power plant needs to execute at each carbon trading moment, based on the virtual power plant benefit optimization model and the daily power generation output of the virtual power plant power generation solution module. The optimal scheduling scheme module obtains the optimal scheduling scheme for various virtual power plants based on the daily power generation output by the virtual power plant power generation solution module, the optimal carbon emission quota purchase amount output by the optimal carbon emission quota purchase strategy module, and the lowest carbon compliance cost. The dispatch instruction sending module sends dispatch instructions to the operators of the power supply units of each virtual power plant according to the optimal dispatch scheme output by the optimal dispatch scheme module.

[0015] The method of the virtual power plant optimization scheduling system in the embodiment includes the following steps: Step 1: Determine the total carbon emission control target, consider the carbon emission cost, and construct a virtual power plant benefit optimization model.

[0016] The optimal model for the efficiency of a virtual power plant is as follows: (1) In the formula, This indicates the economic benefits of a virtual power plant. Indicates the first System operating revenue for virtual power plants For the first The operating cost of a virtual power plant-like system. For the first The lowest cost for carbon compliance of virtual power plants; K represents the number of different types of virtual power plants. For the first The number of virtual power plants.

[0017] The formula for calculating the system operating revenue of a virtual power plant is as follows: (2) In the formula Indicates the first System operating revenue for virtual power plants Revenue from the sale of electricity from a virtual power plant to the grid. Revenue from the sale of heat energy by virtual power plants to the heating network. Revenue from the sale of energy by virtual power plants to users. The load response compensation revenue obtained by virtual power plants participating in grid dispatch. , These represent the power transmission between the virtual power plant and the power grid and heating network, respectively. , These represent electricity price and heat price, respectively. , These represent the electrical load and thermal load that the virtual power plant provides to users after demand response, respectively. As a baseline compensation for interrupted loads, This represents the interruptible load power; t represents the t-th time period; T represents the number of time periods, T=24.

[0018] The formula for calculating the system operating cost of a virtual power plant is: (3) In the formula The cost of natural gas consumption during the dispatch period. The costs required for the operation of the power generation unit, The cost of treating pollution sources, Indicates the price of natural gas. These are the state parameters of the gas turbine. This indicates the low calorific value of natural gas. The rated output power of the combined heat and power system. Indicates the rated power of the gas-fired boiler system. The operating and maintenance cost per unit power of power unit g. This represents the rated power of the power supply unit g, where g = 1, 2, ... M. This represents the output power of the pollution source in the virtual power plant system. This represents the amount of pollutant 'a' produced per unit output power of the pollution source, where a = 1, 2, ..., N. This represents the basic cost of discharging pollutant a per unit. For the first The lowest cost for carbon compliance of a virtual power plant, where M is the number of power supply units and N is the number of types of pollutants.

[0019] Step 2: Solve the virtual power plant efficiency optimization model to obtain the output power of each power supply unit in the virtual power plant; In this embodiment, the minimum carbon compliance cost of the virtual power plant is not considered for the time being; only the system operating revenue and system operating cost of the virtual power plant are considered. Based on different daily load conditions, the non-dominated sorting genetic algorithm II is used to solve the virtual power plant benefit optimization model to obtain the sum of daily energy supply of the virtual power plant's power supply units throughout the year. , , Indicates the virtual power plant The sum of the energy supplied by the heavens.

[0020] Step 3: Based on the output power of the power supply unit obtained in Step 2, calculate the incremental carbon emission limit requirement of the virtual power plant; The formula for calculating the incremental demand for carbon emission allowances is as follows: (4) In the formula For the true carbon emission intensity, As a benchmark for carbon emissions, Indicates the first of the year sky, Indicates the time step.

[0021] Step 4: Based on the historical carbon emission information of the virtual power plant and the current load situation, predict the overall carbon emission demand of the virtual power plant; Based on historical carbon emission information and current load conditions, a long short-term memory network is used to predict the overall carbon emission demand of all virtual power plants. Establish carbon emission cap-price and The price function is, i.e. (5) in This is a superscript index for carbon. and It is the cost coefficient of carbon price.

[0022] Step 5: Use the modified finite difference method to solve for the optimal carbon emission allowance purchase strategy and the minimum cost of carbon compliance at each time point; In the embodiment, the modified finite difference method is used to solve for the carbon trading time step. k Optimal carbon emission allowance purchase volume for virtual power plants and the lowest cost of carbon compliance ; The specific process of solving the modified finite difference method includes: (1) The optimization space of carbon emission limits is gridded into a two-dimensional grid, and the horizontal axis of the two-dimensional grid is... , vertical axis Build an index , express The carbon emission limits required at any given time, These represent the upper and lower boundaries of the carbon emission limit, respectively. For the quantization step size, and They represent time respectively and carbon emission quota requirements Index unit; (2) Set initial values ​​based on the predicted total carbon emission demand of the virtual power plant. Set as the initial value; (3) Iteratively solve for the optimal carbon emission allowance purchase quantity and the minimum carbon compliance cost at each carbon trading time: Calculate the first using formula (5) The price of a day's carbon emission allowance ; For the first The virtual power plant is used for iterative solution: The carbon cost function is calculated using the following formula: (6) In the formula Represent the coordinates in the two-dimensional grid. and corresponding The value; Indicates the first The amount of carbon emission allowance purchased per day. For carbon cost control function, The range of possible values ​​for the amount of carbon emission allowance to be purchased. ; In the formula This indicates the maximum increase in demand for carbon emission allowances within a single day. (7) (8) in express initial value, Risk cost parameters for carbon emission quota requirements, express Quantization step size; This indicates the increase in demand for carbon emission quotas; The value is determined by: through In the range of values Iterative calculation with different values ​​in the middle ,when When the minimum value is reached, at this time, Thus obtain .

[0023] The carbon emission cost calculation function is as follows: (9) In the formula It represents the 365th day. A penalty function representing carbon emissions; The risk costs of delaying the fulfillment of carbon emission quota requirements; (10) In the formula This indicates the amount of carbon emission allowance required to span multiple cycles; for Find the minimum value The calculation formula is: (11) Differentiate equation (11), (12) Boundary conditions are satisfied: ; In the formula The penalty function representing carbon emissions. .

[0024] Step 6: Combine the solution results from Step 5 with the output power of each power supply unit obtained in Step 2 to obtain the optimal solution of the virtual power plant benefit optimization model, i.e., the optimal dispatch scheme. Step 7: Based on the optimal solution of the virtual power plant benefit optimization model obtained in Step 6, guide the operation and scheduling of the power system.

[0025] In this embodiment, by modifying the finite difference method, flexible adjustments and optimizations can be made according to the specific circumstances and needs of the virtual power plant. For example, the division of the difference grid and the selection of the difference formula can be adjusted based on different power generation data, carbon emission quota purchase strategies, and other conditions to obtain more accurate solution results.

[0026] In the embodiments, Class 1, Class 2, Class 3, and Class 4 virtual power plants all use combined heat and power (CHP) systems for power and heat supply, as shown in Table 1.

[0027] Table 1. Parameter Table for Four Types of Virtual Power Plants

[0028] As can be seen, the four types of virtual power plants have the same generating units, but their power generation characteristics, installed capacity, and operating conditions differ across categories. Taking a Class 1 virtual power plant as an example... The value is 1, and the step size is... The value is 0.5, which is the lower limit of the carbon emission limit. A value of 0 represents the upper limit of the carbon emission limit. Given 130, the optimal carbon emission allowance purchase quantity for a Class 1 virtual power plant is as follows: Figure 2 As shown. Figure 2 In the diagram, the X-axis represents 365 days in a year, the Y-axis represents carbon emission demand in thousands of tons, and the Z-axis represents the amount of carbon emission allowances purchased in thousands of tons. Figure 2 The right side shows the color bars for purchasing carbon emission allowances; different colors correspond to different carbon emission allowance purchase volumes. Figure 2 The optimal carbon emission allowance purchase amount can be found by referring to the color bars on the right side of the heat map shown.

[0029] In this embodiment, the user module does not contain any power generation unit and cannot be used for power generation. Each user module can be considered as a unit participating in electricity trading, but its power generation is limited to negative values, thus becoming a one-way power purchase unit. The grid module and heating network module contain power generation units and can also be power purchase units, purchasing electricity from the virtual power plant. This invention only addresses the carbon emission problem of the virtual power plant; carbon emission issues are not considered for other modules. It primarily focuses on market electricity demand, prioritizing meeting electricity needs, and optimizing the resource scheduling of power supply units to improve resource utilization and enhance environmental friendliness.

[0030] This invention considers carbon emissions in addition to the optimization of the optimal economic benefits of conventional virtual power plants, primarily addressing the issue of carbon costs. Under the premise of ensuring that electricity demand and total carbon emission control targets are met, it optimizes the energy dispatch of the power supply units of each virtual power plant. Based on this, it provides the optimal carbon emission allowance purchase strategy to be implemented at each moment, and also obtains the lowest carbon compliance cost. Under the conditions of ensuring power supply demand, carbon emission demand, and responding to policy requirements, it maximizes the economic benefits of virtual power plants.

Claims

1. A virtual power plant optimal dispatching system employing the modified finite difference method, characterized in that, The total carbon emission control target of the power system is determined, a virtual power plant benefit optimization model is constructed, and a two-stage step-by-step solution method is adopted to solve the virtual power plant benefit optimization model. In the first stage, the minimum carbon compliance cost of the virtual power plant is not considered, and the output power of the power supply unit of the virtual power plant benefit optimization model is solved. In the second stage, the modified finite difference method is used to solve the optimal carbon emission quota purchase strategy and the minimum carbon emission cost of the virtual power plant. The system includes: The virtual power plant cost optimization modeling module is used to build a virtual power plant benefit optimization model; The virtual power plant power generation calculation module is used to calculate the daily power generation of the virtual power plant based on the virtual power plant benefit optimization model. The optimal carbon emission allowance purchase strategy module is used to calculate the optimal carbon emission allowance purchase amount and the minimum carbon compliance cost that the virtual power plant needs to execute at each carbon trading moment, based on the virtual power plant benefit optimization model and the daily power generation output of the virtual power plant power generation solution module. The optimal scheduling scheme module obtains the optimal scheduling scheme for various virtual power plants based on the daily power generation output by the virtual power plant power generation solution module, the optimal carbon emission quota purchase amount output by the optimal carbon emission quota purchase strategy module, and the lowest carbon compliance cost. The dispatch instruction sending module sends dispatch instructions to the operators of the power supply units of each virtual power plant according to the optimal dispatch scheme output by the optimal dispatch scheme module.

2. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 1, characterized in that, The power system is divided into a power generation module, a user module, a power grid module, and a heating network module. The power generation module includes multiple different types of virtual power plants, and each virtual power plant includes a power supply unit and a carbon emission control module. Energy supply units include renewable energy supply units and non-renewable energy supply units; The carbon emission control module is used to predict and calculate the overall carbon emission demand of the virtual power plant, analyze carbon emission data, and identify carbon emission trends.

3. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 1, characterized in that, The optimal model for the efficiency of a virtual power plant is as follows: ；（1） In the formula, This indicates the economic benefits of a virtual power plant. Indicates the first System operating revenue for virtual power plants For the first The operating cost of a virtual power plant-like system. For the first The lowest cost for carbon compliance of virtual power plants; K represents the number of different types of virtual power plants. For the first The number of virtual power plants.

4. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 3, characterized in that, In the virtual power plant efficiency optimization model, the formula for calculating the system operating revenue of the virtual power plant is: ；（2） In the formula Indicates the first System operating revenue for virtual power plants Revenue from the sale of electricity from a virtual power plant to the grid. Revenue from the sale of heat energy by virtual power plants to the heating network. Revenue from the sale of energy by virtual power plants to users. The load response compensation revenue obtained by virtual power plants participating in grid dispatch. , These represent the power transmission between the virtual power plant and the power grid and heating network, respectively. , These represent electricity price and heat price, respectively. , These represent the electrical load and thermal load that the virtual power plant provides to users after demand response, respectively. As a baseline compensation for interrupted loads, This represents the interruptible load power; t represents the t-th time period; T represents the number of time periods.

5. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 4, characterized in that, The formula for calculating the system operating cost of a virtual power plant is: ；（3） In the formula The cost of natural gas consumption during the dispatch period. The costs required for the operation of the power generation unit, The cost of treating pollution sources, Indicates the price of natural gas. These are the state parameters of the gas turbine. This indicates the low calorific value of natural gas. The rated output power of the combined heat and power system. Indicates the rated power of the gas-fired boiler system. The operating and maintenance cost per unit power of power unit g. This represents the rated power of the power supply unit g, where g = 1, 2, ... M. This represents the output power of the pollution source in the virtual power plant system. This represents the amount of pollutant 'a' produced per unit output power of the pollution source, where a = 1, 2, ..., N. This represents the basic pollution discharge cost per unit of pollutant a, where M is the number of power supply units and N is the number of types of pollutants.

6. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 5, characterized in that, The first stage of the two-stage step-by-step solution does not consider the minimum carbon compliance cost of the virtual power plant, but only the system operating revenue and system operating cost of the virtual power plant. The non-dominated sorting genetic algorithm II is used to solve the virtual power plant benefit optimization model to obtain the sum of the daily energy supply of the virtual power plant's power supply units throughout the year. , , Indicates the virtual power plant The sum of the energy supplied by the heavens; The formula for calculating the incremental demand for carbon emission allowances is as follows: ；（4） In the formula For the true carbon emission intensity, As a benchmark for carbon emissions, Indicates the first of the year sky, Indicates the time step.

7. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 6, characterized in that, Based on historical carbon emission information and current load conditions, a long short-term memory network is used to predict the overall carbon emission demand of all virtual power plants. Establish carbon emission cap-price and The price function is, i.e. ；（5） in This is a superscript index for carbon. and It is the cost coefficient of carbon price.

8. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 7, characterized in that, The second stage of the two-stage step-by-step solution method uses the modified finite difference method to obtain the solution for each carbon trading time step. k Optimal carbon emission allowance purchase volume for virtual power plants and the lowest cost of carbon compliance ; The specific process of solving the modified finite difference method includes: (1) The optimization space of carbon emission limits is gridded into a two-dimensional grid, and the horizontal axis of the two-dimensional grid is... , vertical axis Build an index , express The carbon emission limits required at any given time, These represent the upper and lower boundaries of the carbon emission limit, respectively. For the quantization step size, and They represent time respectively and carbon emission quota requirements Index unit; (2) Set initial values ​​based on the predicted total carbon emission demand of the virtual power plant. Set as the initial value; (3) Iteratively solve for the optimal carbon emission allowance purchase quantity and the minimum carbon compliance cost at each carbon trading time: Calculate the first using formula (5) The price of a day's carbon emission allowance ; For the first The virtual power plant is used for iterative solution: The carbon cost function is calculated using the following formula: ；（6） In the formula Represent the coordinates in the two-dimensional grid. and corresponding The value; Indicates the first The amount of carbon emission allowance purchased per day. For carbon cost control function, The range of possible values ​​for the amount of carbon emission allowance to be purchased. ； In the formula This indicates the maximum increase in demand for carbon emission allowances within a single day. ；（7） ；（8） in express initial value, Risk cost parameters for carbon emission quota requirements, express Quantization step size; This indicates the increase in demand for carbon emission quotas; The value is determined by: through In the range of values Iterative calculation with different values ​​in the middle ,when When the minimum value is reached, at this time, Thus obtain .

9. The virtual power plant optimal scheduling system using the modified finite difference method according to claim 8, characterized in that, The carbon emission cost calculation function is as follows: ； （9） In the formula It represents the 365th day. A penalty function representing carbon emissions; The risk costs of delaying the fulfillment of carbon emission quota requirements; ； （10） In the formula This indicates the amount of carbon emission allowance required to span multiple cycles; for Find the minimum value The calculation formula is: ；（11） Differentiate equation (11), ；（12） Boundary conditions are satisfied: ； In the formula The penalty function representing carbon emissions. .

10. The method of the virtual power plant optimization scheduling system as described in any one of claims 1-9, characterized in that, The method includes the following steps: Step 1: Determine the total carbon emission control target for the power system, consider carbon emission costs, and construct a virtual power plant benefit optimization model; Step 2: Without considering the minimum cost of carbon compliance for the virtual power plant, solve the optimization model for the efficiency of the virtual power plant to obtain the output power of each power supply unit of the virtual power plant; Step 3: Based on the sum of the output power of the power supply units obtained in Step 2, calculate the incremental carbon emission limit demand of the virtual power plant; Step 4: Based on the historical carbon emission information of the virtual power plant and the current load situation, predict the overall carbon emission demand of the virtual power plant; Step 5: Based on the incremental carbon emission allowance demand of the virtual power plant obtained in Step 3 and the total carbon emission demand of the virtual power plant obtained in Step 4, use the modified finite difference method to solve for the optimal carbon emission allowance purchase strategy and the minimum cost of carbon compliance at each time point. Step 6: Combine the optimal carbon emission limit purchase strategy and the minimum cost of carbon compliance obtained in Step 5 with the output power of each power supply unit obtained in Step 2 to obtain the optimal solution of the virtual power plant benefit optimization model, i.e., the optimal dispatch scheme. Step 7: Based on the optimal solution of the virtual power plant benefit optimization model obtained in Step 6, guide the operation and scheduling of the power system.

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