Power system optimization method based on electric carbon collaborative virtual power plant

Abstract

The present application relates to a power system optimization method based on electric carbon collaborative virtual power plant, comprising: determining the total carbon emission control target, considering the carbon emission cost, and constructing a virtual power plant benefit optimization model; solving the virtual power plant benefit optimization model to obtain the output power of each energy supply unit of the virtual power plant; calculating the carbon emission quota demand increment of the virtual power plant; according to the historical carbon emission information and the current load condition of the virtual power plant, predicting the overall carbon emission demand of the virtual power plant; using the modified finite difference method, solving the optimal carbon emission quota purchase strategy and the carbon compliance minimum cost at each time; combining the solving result with the output power of each energy supply unit to obtain the optimal solution of the virtual power plant benefit optimization model, that is, the optimal scheduling scheme. The present application realizes the cost optimization of the virtual power plant, and reduces the overall carbon emission cost of the power system under the premise of ensuring the power demand and the total carbon emission control target of the virtual power plant.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power optimization scheduling, and particularly relates to a power system optimization method based on an electricity-carbon collaborative virtual power plant. BACKGROUND

[0002] With the continuous growth of global energy demand and the transformation of energy structure, as an integrated energy system of multiple distributed energy resources, virtual power plant has the ability of autonomous operation and scheduling. The internal load scheduling optimization of the virtual power plant is the key to realize efficient energy utilization and system operation optimization. Under the premise of ensuring the power demand and the total carbon emission control target, optimizing the energy scheduling of each virtual power plant and reducing the overall carbon emission cost is also a key problem. SUMMARY

[0003] The application aims to solve the above problems, and provides a power system optimization method based on an electricity-carbon collaborative virtual power plant. A cost optimization model of the virtual power plant is constructed, and a two-stage solving method is adopted. The first stage is to solve the power generation of the virtual power plant per day, and the second stage is to solve the optimal carbon emission limit purchase strategy and the minimum carbon emission cost of the virtual power plant, so as to ensure that the virtual power plant reduces the overall carbon emission cost of the power system under the premise of ensuring the power demand and the total carbon emission control target.

[0004] In order to achieve the above purpose, the technical scheme provided by the application is as follows:

[0005] The power system optimization method based on the electricity-carbon collaborative virtual power plant comprises a power generation module, a user module, a power grid module and a heat grid module. The power grid module and the heat grid module exchange power and heat with the power generation module. The power generation module comprises a plurality of virtual power plants, and each virtual power plant comprises an energy supply unit and a carbon emission control module. The carbon emission control module is used for predicting and calculating the overall carbon emission demand of the virtual power plant, analyzing carbon emission data, identifying carbon emission trends and main sources. The energy supply unit comprises a renewable energy supply unit, a non-renewable energy supply unit, a calculation unit and an information transmission unit. The calculation unit is used for calculating the control parameters of the energy supply unit, the operation cost of the virtual power plant and the carbon emission cost. The information transmission unit is used for transmitting the calculation result data of the calculation unit.

[0006] The power system optimization method adopts a two-stage step-by-step solving method to solve the benefit optimization model of the virtual power plant. In the first stage, the output power of the energy supply unit of the benefit optimization model of the virtual power plant is solved, and in the second stage, the optimal carbon emission limit purchase strategy and the minimum carbon emission cost of the virtual power plant are solved.

[0007] The power system optimization method comprises the following steps:

[0008] Step 1: Determine the total carbon emission control target, consider the carbon emission cost, and construct a virtual power plant benefit optimization model;

[0009] 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;

[0010] 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;

[0011] 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;

[0012] 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.

[0013] Step 6: Combine the optimal carbon emission limit purchase strategy and the minimum cost of carbon compliance 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.

[0014] 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.

[0015] Preferably, in step 1, the virtual power plant efficiency optimization model is:

[0016] (1)

[0017] 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.

[0018] Furthermore, the formula for calculating the system operating revenue of the virtual power plant is as follows:

[0019] (2)

[0020] In the formula Indicates the first System operation income of the virtual power plant, Income from selling electricity to the grid for the virtual power plant, Income from selling heat energy to the heat grid for the virtual power plant, Income from selling energy to users for the virtual power plant, Load response compensation income obtained by the virtual power plant participating in grid dispatch, 、 Pgtand Phtrespectively represent the transmission power between the virtual power plant and the grid and the heat grid, 、 Ppriceand Pheatrespectively represent the electricity price and the heat price, 、 Ploadand Pheatrespectively represent the power load and the heat load provided by the virtual power plant to users after demand response, Base compensation for interruptible load, Pintrepresentable interruptible load power; t represents the t-th time period; T represents the number of time periods, T = 24.

[0021] Further, the calculation formula of the system operation cost of the virtual power plant is:

[0022] ; (3)

[0023] In the formula, Cngrepresents the consumption cost of natural gas in the dispatching period, Cg, g = 1, 2, … M, represents the cost required for the operation of the energy supply unit g, Cp, a = 1, 2, … N, represents the cost required for treating the pollution source a, Pngrepresents the price of natural gas, Xg, g = 1, 2, … M, represents the state parameter of the gas turbine, LHV represents the low calorific value of natural gas, Pcogrepresents the rated output power of the cogeneration system, Pcbrepresents the rated power of the gas boiler system, Cg, g = 1, 2, … M, represents the operation and maintenance cost per unit power of the energy supply unit g, Pg, g = 1, 2, … M, represents the rated power of the energy supply unit g, Pp, a = 1, 2, … N, represents the output power of the pollution source in the virtual power plant system, Pp, a = 1, 2, … N, represents the amount of pollutant a generated per unit output power of the pollution source, Cp, a = 1, 2, … N, represents the basic pollution discharge cost per unit pollutant a, Cmin, g = 1, 2, … M, represents the minimum cost of the virtual power plant, Cminrepresents the carbon compliance minimum cost of the virtual power plant, M is the number of energy supply units, and N is the number of categories of pollutants.

[0024] Preferably, in the step 2, without considering the carbon compliance minimum cost of the virtual power plant, only considering the system operation income of the virtual power plant and the system operation cost of the virtual power plant, according to different load conditions of each day, a non-dominated sorting genetic algorithm II is used to solve the virtual power plant benefit optimization model to obtain the sum of energy supply of the virtual power plant generating unit in each day in a year , , represents the sum of energy supply of the virtual power plant on the th day.

[0025] Further, in the step 3, the carbon emission quota demand increment is calculated, and the calculation formula of the carbon emission quota demand increment is:

[0026] ; (4)

[0027] In the formula, is the real carbon emission intensity, is the carbon emission benchmark, represents the th day in a year, represents the time step.

[0028] Preferably, in the step 4, based on historical carbon emission information and current load conditions, a long short-term memory network is used to obtain a predicted value of the total carbon emission demand of all virtual power plants , a price function of the carbon emission quota price and is established, that is,

[0029] ; (5)

[0030] In the formula, is the upper index of carbon, and are cost coefficients of the carbon price.

[0031] Preferably, in the step 5, a modified finite difference method is used to obtain the optimal carbon emission quota purchase amount k and the carbon compliance minimum cost of each carbon trading time .

[0032] The specific process of the modified finite difference method includes:

[0033] (1) The optimization space of the carbon emission quota is gridded into a two-dimensional grid, the horizontal axis of the two-dimensional grid is , and the vertical axis is , represents the carbon emission quota required at the th time, respectively represent the upper and lower boundaries of the carbon emission limit, is the quantization step size, and respectively represent the time and the index unit of the carbon emission quota demand;

[0034] (2) Set the initial value, according to the predicted value of the overall carbon emission demand of the virtual power plant, set as the initial value;

[0035] (3) Iteratively solve the optimal carbon emission limit purchase quantity and the minimum carbon compliance cost that need to be executed at each carbon trading time:

[0036] Calculate the price of the carbon emission limit on the th day through formula (5) ;

[0037] Loop to solve the th type of virtual power plant:

[0038] Calculate the carbon cost function through the following formula:

[0039] ; (6)

[0040] In the formula, respectively represent the values of the coordinates and in the two-dimensional grid corresponding to ; represents the purchase quantity of the carbon emission quota on the th day, is the carbon cost control function, represents the value space of the purchase quantity of the carbon emission quota,

[0041] ;

[0042] In the formula, represents the maximum carbon emission limit demand increment within a day;

[0043] ; (7)

[0044] ; (8)

[0045] wherein represents the initial value of , is the risk cost parameter of the carbon emission quota demand, represents the quantization step size of ; and represents the carbon emission limit demand increment.​

[0046] The value mode of the carbon emission cost calculation function is: through taking different values in the value interval iterative calculation When reaches the minimum value, at this time, Thus, the carbon emission cost calculation function is obtained .

[0047] Preferably, the carbon emission cost calculation function is:

[0048] ; (9)

[0049] In the formula, represents the 365th day, represents the penalty function of carbon emission; is the risk cost when the carbon emission quota requirement is delayed to meet;

[0050] ; (10)

[0051] In the formula, represents the carbon emission quota requirement amount allowed to cross the period.

[0052] Further, in order to minimize the carbon emission cost, for , the minimum value is obtained , and the calculation formula is:

[0053] ; (11)

[0054] Derivate the formula (11),

[0055] ; (12)

[0056] Satisfy the boundary condition:

[0057] ;

[0058] In the formula, represents the penalty function of carbon emission, .

[0059] Compared with the prior art, the beneficial effects of the present application include:

[0060] 1) The present application integrates dispersed energy resources such as renewable energy and non-renewable energy through a virtual power plant, forms a virtual centralized energy system, and realizes power demand response and power generation and energy supply optimization operation control by using the intelligent calculation unit and information transmission unit of the virtual power plant, thereby achieving optimal control of the cost of the virtual power plant under the premise of ensuring the total power demand and carbon emission control target.

[0061] 2) The application constructs a cost optimization model of virtual power plant, and adopts a two-stage solving method, the first stage solves the power generation of virtual power plant every day, and the second stage solves the optimal carbon emission limit purchase strategy and the lowest carbon emission cost of virtual power plant, which can effectively reduce the overall carbon emission cost of power system.

[0062] 3) The application uses the advantages of the modified finite difference method, such as wide applicability, ability to handle nonlinear problems, simple and intuitive calculation process, and high flexibility, to solve the optimal carbon emission limit purchase strategy and the lowest carbon compliance cost at each time. As a method for numerically solving partial differential equations, the modified finite difference method is suitable for various types of equations, including complex equations that describe the operation of virtual power plants and carbon emission limit purchase strategies. Through the modified finite difference method, these problems can be handled more accurately, making it suitable for solving the benefit optimization model of virtual power plant. The operation and carbon emission management of virtual power plant often involve nonlinear problems such as the volatility of power generation and the dynamic changes of carbon emission limits. The modified finite difference method can handle these nonlinear problems to ensure the accuracy and reliability of the solution. The modified finite difference method divides the continuous solution domain into difference grids, replacing the continuous solution domain with finite grid nodes, thereby discretizing the original problem into a difference format. This method has a simple and intuitive calculation process, is easy to implement and understand, and is convenient for popularization and application in practical applications. The modified finite difference method can be adapted to adaptive grid methods, dynamically adjusting the grid or difference format according to the characteristics of the solution. Compared with traditional finite difference methods, it can better handle boundary conditions. BRIEF DESCRIPTION OF DRAWINGS

[0063] The application will be further described below in conjunction with the drawings and examples.

[0064] Figure 1 A schematic diagram of the power system of the embodiment of the application.

[0065] Figure 2 A distribution diagram of the optimal carbon emission limit purchase amount of Class 1 virtual power plant in a year. DETAILED DESCRIPTION

[0066] As Figure 1As shown, the power system optimization method based on the electric-carbon collaborative virtual power plant includes a power generation module, a user module, a power grid module, and a heat grid module. The power grid module and the heat grid module exchange power and heat with the power generation module. The power generation module includes four types of virtual power plants, namely Class1, Class2, Class3, and Class4. The virtual power plants include energy supply units and carbon emission control modules. The carbon emission control module predicts the total carbon emission demand of the virtual power plant based on a long short-term memory network, analyzes carbon emission data, identifies carbon emission trends and main sources. The energy supply unit includes renewable energy supply units, non-renewable energy supply units, and calculation units and information transmission units. The calculation unit is used to calculate the control parameters of the energy supply unit and the operation cost and carbon emission cost of the virtual power plant. The information transmission unit is used to transmit the calculation result data of the calculation unit. The user module is a power consumption unit that sends demand response to the power generation module. The above modules are connected by power transmission lines to transmit electric energy. At the same time, the modules are communicatively connected to transmit information.

[0067] The power system optimization method based on the electric-carbon collaborative virtual power plant includes the following steps:

[0068] Step 1: Determine the total carbon emission control target, consider the carbon emission cost, and construct a virtual power plant benefit optimization model.

[0069] In the embodiment, the virtual power plant benefit optimization model is:

[0070] ; (1)

[0071] In the formula, represents the economic benefit of the virtual power plant, represents the system operation income of the first class virtual power plant, is the system operation cost of the first class virtual power plant, is the minimum carbon compliance cost of the first class virtual power plant; K is the number of virtual power plant types, is the number of the first class virtual power plant.

[0072] The calculation formula of the system operation income of the virtual power plant is:

[0073] ; (2)

[0074] In the formula, represents the system operation income of the first class virtual power plant, is the income of the virtual power plant selling power to the power grid, is the income of the virtual power plant selling heat energy to the heat grid, 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.

[0075] The formula for calculating the system operating cost of a virtual power plant is:

[0076] (3)

[0077] 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.

[0078] 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;

[0079] In the embodiment, without considering the carbon compliance minimum cost of the virtual power plant, only considering the system operation income of the virtual power plant and the system operation cost of the virtual power plant, according to different load conditions of each day, a non-dominated sorting genetic algorithm II is used to solve the virtual power plant benefit optimization model, to obtain the sum of energy supply of the virtual power plant generating unit in each day in a year , , represents the sum of energy supply of the virtual power plant on the day.

[0080] Step 3: According to the output power of the energy supply unit obtained in step 2, the carbon emission quota demand increment of the virtual power plant is calculated;

[0081] The carbon emission quota demand increment is calculated, and the calculation formula of the carbon emission quota demand increment is:

[0082] ; (4)

[0083] In the formula, is the real carbon emission intensity, is the carbon emission benchmark, represents the day in a year, represents the time step.

[0084] Step 4: According to the historical carbon emission information of the virtual power plant and the current load condition, the overall carbon emission demand of the virtual power plant is predicted;

[0085] Based on the historical carbon emission information and the current load condition, a long short-term memory network is used to predict the predicted value of the overall carbon emission demand of all virtual power plants , and a price function of the carbon emission quota price and is established, that is,

[0086] ; (5)

[0087] In the formula, is the upper index of carbon, and are the cost coefficients of the carbon price.

[0088] Step 5: Using the modified finite difference method, the optimal carbon emission quota purchase strategy and the carbon compliance minimum cost at each time are solved;

[0089] In the embodiment, the modified finite difference method is used to solve the optimal carbon emission quota purchase amount k and the carbon compliance minimum cost of the virtual power plant of the type at each carbon trading time.

[0090] The specific process of solving by modified finite difference method includes:

[0091] (1) The optimization space of carbon emission quota is gridded into a two-dimensional grid, the horizontal axis of the two-dimensional grid , the vertical axis , the index , represents the required carbon emission limit at time , respectively represents the upper and lower boundaries of the carbon emission limit, is the quantified step size, and respectively represent the index units of time and carbon emission quota demand ;

[0092] (2) Set the initial value, according to the predicted value of the overall carbon emission demand of the virtual power plant, set as the initial value;

[0093] (3) Iteratively solve the optimal carbon emission limit purchase quantity and the minimum carbon compliance cost to be performed at each carbon trading time:

[0094] Calculate the carbon emission limit price of the th day through formula (5);

[0095] Loop to solve the th virtual power plant:

[0096] Calculate the carbon cost function through the following formula:

[0097] ; (6)

[0098] In the formula, respectively represent the value of and in the two-dimensional grid corresponding to the coordinate ; represents the purchase quantity of carbon emission quota on the th day, is the carbon cost control function, represents the value space of the purchase quantity of carbon emission quota,

[0099] ;

[0100] In the formula, represents the maximum carbon emission limit demand increment in a day;

[0101] ; (7)

[0102] ​; (8)

[0103] wherein is the initial value of , is the risk cost parameter of carbon emission quota demand, quantifies the step length;

[0104] The value of is obtained by: taking different values in the value interval iteratively calculating , when reaches the minimum value, at this time, , thus obtaining . .

[0105] The carbon emission cost calculation function is:

[0106] ; (9)

[0107] wherein represents the 365th day, represents the penalty function of carbon emission; is the risk cost when the carbon emission quota demand is delayed to be met;

[0108] ; (10)

[0109] wherein represents the allowed cross-period carbon emission quota demand.

[0110] In order to minimize the carbon emission cost, the minimum value of is obtained for , and the calculation formula is:

[0111] ; (11)

[0112] Derivate the formula (11),

[0113] ; (12)

[0114] Satisfy the boundary condition:

[0115] ;

[0116] wherein represents the penalty function of carbon emission, .

[0117] Step 6: Combine the solution of step 5 with the output power of each energy supply unit obtained in step 2 to obtain the optimal solution of the virtual power plant benefit optimization model, that is, the optimal scheduling scheme.​​

[0118] Step 7: According to the optimal solution of the virtual power plant benefit optimization model obtained in step 6, guiding power system operation scheduling.

[0119] In the embodiment, by modifying the finite difference method, flexible adjustment and optimization can be carried out according to the specific situation and demand of the virtual power plant. For example, according to different power generation data, carbon emission quota purchase strategy and other conditions, the division of the difference grid and the selection of the difference formula can be adjusted to obtain more accurate solution results.

[0120] In the embodiment, the Class1, Class2, Class3 and Class4 virtual power plants all use combined heat and power systems for power supply and heat supply, as shown in Table 1.

[0121] Table 1 Parameter table of four types of virtual power plants

[0122]

[0123] It can be seen that the four types of virtual power plants have the same unit composition, and their power generation characteristics, installed capacity and working conditions are different in different types of virtual power plants. Taking the Class1 virtual power plant as an example, The value is 1, the step The value is 0.5, the lower boundary of carbon emission quota The value is 0, the upper boundary of carbon emission quota 130, the optimal carbon emission quota purchase amount of the Class1 virtual power plant obtained by solving is as Figure 2 shown. Figure 2 In the figure, the X-axis represents 365 days a year, the Y-axis represents the carbon emission demand in kiloton, and the Z-axis represents the carbon emission quota purchase amount in kiloton, Figure 2 The color bar on the right side is the color bar of the carbon emission quota purchase amount, and different carbon emission quota purchase amounts correspond to different colors. In Figure 2 the heat map shown in the figure, according to the color bar on the right, the corresponding optimal carbon emission quota purchase amount can be found.

[0124] In the embodiment, the user module does not contain any power generation unit and cannot be used for power generation. Each user module can be regarded as a unit participating in power trading, but the power generation is limited to negative value, so it becomes a one-way power purchase unit. The grid module and the heat network module contain power generation units and can also be power purchase units, which can purchase power from the virtual power plant. The present application only aims at the carbon emission problem of the virtual power plant, and other modules do not consider the carbon emission problem. The main purpose is to meet the power demand based on the market demand for electricity, to optimize the scheduling of power supply units and improve the resource utilization rate, and to enhance the environmental friendliness.

[0125] A system of an electricity system optimization method based on an electric-carbon collaborative virtual power plant, comprising:

[0126] A virtual power plant cost optimization modeling module for constructing a virtual power plant benefit optimization model;

[0127] A virtual power plant power generation solving module for solving the daily power generation of the virtual power plant according to the virtual power plant benefit optimization model;

[0128] An optimal carbon emission limit purchase strategy module for solving the optimal carbon emission limit purchase amount and the minimum carbon compliance cost of the virtual power plant at each carbon trading time according to the virtual power plant benefit optimization model and the daily power generation of the virtual power plant output by the virtual power plant power generation solving module;

[0129] An optimal scheduling scheme module for obtaining the optimal scheduling scheme of each virtual power plant according to the daily power generation of the virtual power plant output by the virtual power plant power generation solving module and the optimal carbon emission limit purchase amount and the minimum carbon compliance cost output by the optimal carbon emission limit purchase strategy module;

[0130] A scheduling instruction sending module for sending scheduling instructions to the operators of the energy supply units of each virtual power plant according to the optimal scheduling scheme output by the optimal scheduling scheme module.

[0131] The present application considers the carbon emission problem on the basis of the optimal economic benefit optimization problem of the conventional virtual power plant, mainly solves the carbon cost problem, and under the premise of ensuring to meet the power demand and the total carbon emission control target, optimizes the energy scheduling of the energy supply units of each virtual power plant, based on which, the optimal carbon emission limit purchase strategy to be executed at each time is given, and the minimum carbon compliance cost can also be obtained, so that the virtual power plant economic benefit is maximized under the guarantee of the power supply demand, the carbon emission demand and the response policy requirement.

Claims

1. A power system optimization method based on an electric-carbon collaborative virtual power plant, characterized in that, The power system comprises a power generation module, a user module, a power grid module and a heat grid module, the power generation module comprises a plurality of virtual power plants of different types, the virtual power plants comprise energy supply units and carbon emission control modules; The carbon emission control module is used for predicting and calculating overall carbon emission demand of the virtual power plant, analyzing carbon emission data and identifying carbon emission trends; the energy supply units comprise renewable energy supply units and non-renewable energy supply units; The power system optimization method adopts a two-stage step-by-step solving mode to solve the virtual power plant benefit optimization model, the output power of the energy supply units of the virtual power plant benefit optimization model is solved in the first stage, and the optimal carbon emission quota purchase strategy and the minimum carbon emission cost of the virtual power plant are solved in the second stage; The power system optimization method comprises the following steps: Step 1: determining a carbon emission total amount control target, considering a carbon emission cost, and constructing a virtual power plant benefit optimization model; Step 2: solving the virtual power plant benefit optimization model without considering the minimum carbon compliance cost of the virtual power plant, and obtaining the output power of each energy supply unit of the virtual power plant; Step 3: calculating the carbon emission quota demand increment of the virtual power plant according to the sum of the output power of the energy supply units obtained in step 2; Step 4: predicting the overall carbon emission demand of the virtual power plant according to historical carbon emission information and current load conditions of the virtual power plant; Step 5: using a modified finite difference method to solve the optimal carbon emission quota purchase strategy and the minimum carbon compliance cost at each time according to the carbon emission quota demand increment of the virtual power plant obtained in step 3 and the overall carbon emission demand of the virtual power plant obtained in step 4; Step 6: combining the optimal carbon emission quota purchase strategy and the minimum carbon compliance cost obtained in step 5 with the output power of each energy supply unit obtained in step 2 to obtain the optimal solution of the virtual power plant benefit optimization model, that is, an optimal scheduling scheme; Step 7: guiding power system operation and scheduling according to the optimal solution of the virtual power plant benefit optimization model obtained in step 6.

2. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 1, characterized in that, In step 1, the virtual power plant benefit optimization model is: ；（1） In the formula, represents the economic benefit of the virtual power plant, represents the first class virtual power plant system operation income, is the first class virtual power plant system operation cost, is the first class virtual power plant carbon compliance minimum cost; K is the number of virtual power plant categories, is the number of the first class virtual power plant.

3. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 2, characterized in that, In step 1, the calculation formula of the system operation income 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.

4. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 3, characterized in that, In step 1, the calculation formula of the system operation cost of the virtual power plant is: ；（3） wherein is the cost of natural gas consumption at the dispatching period, is the cost required for the operation of the energy supply unit, is the cost required for the treatment of the pollution source, denotes the price of natural gas, is the state parameter of the gas turbine, denotes the low calorific value of natural gas, is the rated output power of the cogeneration system, denotes the rated power of the gas boiler system, is the operation and maintenance cost per unit power of the energy supply unit g, denotes the rated power of the energy supply unit g, g = 1, 2, … M, denotes the output power of the pollution source in the virtual power plant system, denotes the amount of pollutants a generated per unit output power of the pollution source, a = 1, 2, … N, denotes the basic pollution discharge cost per unit of pollutant a, M is the number of energy supply units, and N is the number of pollutant categories.

5. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 4, characterized in that, In step 2, 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. 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.

6. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 5, characterized in that, In step 3, the carbon emission quota demand increment is calculated, and the calculation formula of the carbon emission quota demand increment is: ；（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 power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 6, characterized in that, In step 4, based on historical carbon emission information and current load conditions, a long short-term memory network is used to predict the predicted value of the overall carbon emission demand of all virtual power plants , the price function of the carbon emission limit price and is ；（5） wherein is the upper index of carbon, and is the cost coefficient of carbon price.

8. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 7, characterized in that, In step 5, the modified finite difference method is used to solve each carbon trading time point k Optimal carbon emission limit purchase quantity of virtual power plant And the minimum cost of carbon compliance ; The specific process of solving by the modified finite difference method comprises: (1) The optimization space of carbon emission quota is meshed into a two-dimensional grid, the horizontal axis of the two-dimensional grid represents the time , the vertical axis represents the carbon emission quota , an index , represents the required carbon emission quota at the moment , represents the upper and lower boundaries of the carbon emission quota respectively, is the quantization step, and represent the index units of time and carbon emission quota demand respectively; (2) Set the initial value, according to the virtual power plant overall carbon emissions demand prediction value, will Set as the initial value; (3) iteratively solving the optimal carbon emission quota purchase amount and the minimum carbon compliance cost at each carbon trading time: The price of the carbon emission limit of the day is calculated by formula (5) ;​ The first The virtual power plant is solved in a loop: The carbon cost function is calculated by the following formula: ；（6） wherein respectively denote the coordinates in a two-dimensional grid and corresponding values of the carbon emission limit; denotes the amount of purchased carbon emission allowances for the day, is a carbon cost control function, denotes the value space of the amount of purchased carbon emission allowances, ； In the formula represents the maximum carbon emission limit requirement increment within a day; ；（7） ；（8） wherein denotes the initial value of is a risk cost parameter of the carbon emission quota demand, denotes quantifies the step size; denotes the increment of the carbon emission quota demand; The value mode is: through In the value interval Iterative calculation When The minimum value is reached, at which time, , so as to obtain .

9. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 8, characterized in that, The carbon emission cost calculation function is: ； （9） wherein denotes the 365th day, denotes a penalty function for carbon emissions; is the risk cost for the delay in meeting the carbon emission limit requirement; ； （10） In the formula represents the amount of carbon emission allowance demand allowed across cycles.

10. The power system optimization method based on the electric-carbon collaborative virtual power plant according to claim 9, characterized in that, For minimizing , the calculation formula is: ；（11） Derivate formula (11), ；（12） The boundary condition is satisfied: ； wherein a penalty function representing carbon emissions, .

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