Virtual power plant income distribution method based on generalized Nash bargaining in electricity-carbon coupling market

Abstract

The invention relates to a virtual power plant income distribution method based on generalized Nash bargaining in an electricity-carbon coupling market. The method comprises the following steps: S1, establishing a virtual power plant aggregation model; s2, with maximization of the overall profit of the virtual power plant as a target, a virtual power plant optimization scheduling model is established based on profit-cost factors, and the profit-cost factors include load power utility, peak regulation market profit, power grid interaction profit, carbon market profit, light abandoning cost and peak regulation deviation penalty; s3, solving the virtual power plant optimization scheduling model; and S4, according to the optimal solution obtained in the step S3, by taking maximization of the generalized Nash bargaining utility function as a target, constructing a virtual power plant income distribution model based on the generalized Nash bargaining, and performing solving to obtain an income distribution scheme of each aggregation main body of the virtual power plant. Compared with the prior art, scientific scheduling of each distributed main body of the virtual power plant can be realized, and meanwhile, the enthusiasm of each main body in participating in the electricity-carbon coupling market transaction is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid transaction revenue distribution, and in particular relates to a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market. Background Art

[0002] Large cities have obvious dual-peak characteristics of electricity, few local power generation resources, an increasing proportion of new energy, and abundant adjustable load resources. Virtual power plants can aggregate multiple adjustable loads and distributed photovoltaic and other heterogeneous resources through advanced communication and computer technologies, which has great strategic significance in promoting the consumption of new energy, promoting energy transformation, and saving energy and reducing carbon emissions.

[0003] The "Implementation Rules for the Management of Power Auxiliary Services in the East China Region" (hereinafter referred to as the "Rules") clearly stipulate that the grid-connected entities of power auxiliary services in East China include virtual power plants. The "Rules" mention that power auxiliary services in the East China region are divided into basic auxiliary services and paid auxiliary services. Among them, basic auxiliary services are provided by the grid-connected entities as an obligation and no compensation is required; paid peak-shaving in paid auxiliary services includes deep peak-shaving compensation, start-stop peak-shaving compensation for large traditional power generating units, and adjustable load peak-shaving (valley-filling) compensation. In general, paid peak-shaving in the East China region can be summarized as follows: when electricity is sufficient, the behavior of reducing power generation output and increasing load can bring benefits. At the same time, the carbon market is also developing rapidly. Traditional power generating units will be issued initial carbon quotas and often need to purchase more carbon quotas from the carbon market to cover carbon emissions. The load may face indirect carbon emission assessments for purchasing electricity from the power grid. Therefore, judging from the development trend of the power environment, virtual power plants may need to participate in power peak regulation and be assessed by the carbon market in the future. On the one hand, virtual power plants participate in power peak regulation by adjusting loads and abandoning light. On the other hand, virtual power plants can apply for CCER certificates through aggregated distributed photovoltaics to help loads offset a certain amount of carbon emissions. If there are excess carbon quotas, they can be put into the market for trading and profit.

[0004] In the electricity-carbon coupled market, the existing profit distribution method of virtual power plants is difficult to achieve optimal distribution, resulting in weak enthusiasm of various entities to participate in transactions. Therefore, how to design the profit distribution of various participants in virtual power plants needs further study. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a virtual power plant profit distribution method based on generalized Nash bargaining in an electricity-carbon coupling market, so as to realize the scientific scheduling of various distributed entities of the virtual power plant. At the same time, while ensuring fair and reasonable distribution, it ensures that each entity can obtain higher profits after aggregation than before aggregation, thereby increasing the enthusiasm of each entity to participate in electricity-carbon coupling market transactions.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market, comprising the following steps:

[0008] S1. Establishing a virtual power plant aggregation model, wherein the virtual power plant aggregation model includes multiple adjustable loads and multiple distributed photovoltaics;

[0009] S2. With the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on the virtual power plant aggregation model and the benefit-cost factors, wherein the benefit-cost factors include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty;

[0010] S3, solving the virtual power plant optimization scheduling model to obtain the optimal scheduling plan of the virtual power plant in each time period;

[0011] S4. According to the optimal solution obtained in step S3, with the goal of maximizing the generalized Nash bargaining utility function, a virtual power plant profit distribution model based on generalized Nash bargaining is constructed and solved to obtain the profit distribution plan of each aggregated entity of the virtual power plant.

[0012] Furthermore, in step S1, the power utility of the i-th adjustable load in time period t is The modeling is as follows:

[0013]

[0014] in, is the load amount of load i in time period t, and the load consumption power constraint is and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The maximum value of the corresponding quadratic function, P loadimin and P loadimax are the minimum load and the maximum load, respectively;

[0015] Output of the kth distributed photovoltaic system in the scenario ω during time period t The constraints of photovoltaic output are obtained through prediction by scenario generation method as follows:

[0016]

[0017] Among them, P PVkmax The maximum photovoltaic output.

[0018] Furthermore, in step S2, the expression of the virtual power plant optimization scheduling model is specifically as follows:

[0019] max P=R load +R F +P G +R CM -Cq d -C ferror

[0020] Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the income of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, C ferror It represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows:

[0021]

[0022] in, represents the electricity utility of the i-th load in period t;

[0023]

[0024] in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows:

[0025]

[0026] in, and are the minimum load and maximum load in period t respectively;

[0027] P G =R G -C G

[0028] Among them, R G is the revenue from electricity sales of the virtual power plant, C G The cost of purchasing electricity for the virtual power plant;

[0029]

[0030] Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t;

[0031]

[0032] Among them, λ p The cost price of abandoned power from distributed photovoltaic power generation;

[0033]

[0034] Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t.

[0035] Furthermore, the revenue from electricity sales of the virtual power plant is R G and the virtual power plant electricity purchase cost C G The calculation formula is as follows:

[0036]

[0037] in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t.

[0038] Furthermore, the baseline of the virtual power plant period t The calculation formula is as follows:

[0039]

[0040] Among them, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

[0041] Furthermore, the optimal dispatching plan of the virtual power plant in each time period obtained in step S3 includes the load amount of each load in each time period, the output and abandoned light amount of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the revenue items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

[0042] Furthermore, in step S4, the expression of the virtual power plant revenue distribution model based on generalized Nash bargaining is specifically as follows:

[0043]

[0044] Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, respectively.

[0045] Furthermore, the utility function E of distributed photovoltaic k and load i in generalized Nash bargaining is PVk and E loadi The specific expression is as follows:

[0046] E PVk =ln(P PVkA -P PVkS )

[0047] E loaai =ln(P loadiA -P loadis )

[0048] P PVkA ≥P PVkS

[0049] P loadiA ≥P loadis

[0050] Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadiS They are the profits of distributed photovoltaics and loads before they participate in virtual power plant aggregation.

[0051] Furthermore, the profit P of distributed photovoltaics before participating in virtual power plant aggregation PVks The specific expression is as follows:

[0052]

[0053] Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, is the wholesale electricity price, λCM is the carbon quota price, x a1 Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients. It represents the amount of abandoned light of distributed photovoltaic k in time period t.

[0054] Furthermore, the profit P before the load participates in the virtual power plant aggregation loadiS The specific expression is as follows:

[0055]

[0056] in, represents the electricity utility of the i-th load in period t, is the load amount of load i in time period t, is the electricity price for tomorrow’s period t, λ CM is the carbon quota price, x a2 The carbon emission coefficient for purchasing electricity from the grid for the virtual power plant load.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The present invention proposes a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market. First, a virtual power plant aggregation model including multiple adjustable loads and multiple distributed photovoltaics is established. On this basis, with the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established by comprehensively considering the load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty and other revenue-cost factors, which can improve the accuracy of obtaining the scheduling plan; then, according to the optimal solution of the virtual power plant optimization scheduling model, with the goal of maximizing the generalized Nash bargaining utility function, a virtual power plant revenue distribution model based on generalized Nash bargaining is constructed and solved to obtain the revenue distribution plan of each aggregation subject of the virtual power plant. While ensuring fair and reasonable distribution, it ensures that each subject can obtain higher revenue after aggregation than before aggregation, thereby improving the enthusiasm of each subject to participate in electricity-carbon coupling market transactions, realizing the optimal allocation of power resources and carbon emission resources, and promoting green and low-carbon development.

[0059] 2. The present invention predicts the output of each distributed photovoltaic in different scenarios at different time periods through the scenario generation method. The power system can more accurately understand the power generation of distributed photovoltaics, thereby reasonably arranging the power generation ratio of traditional energy and renewable energy. This helps to optimize the allocation of power resources, improve the overall efficiency of the power system, and provide more accurate data support for power market transactions and electricity price formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of the method of the present invention;

[0061] Figure 2 Schematic diagram of virtual power plants participating in electricity-carbon coupling market dispatch. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0063] Example:

[0064] This embodiment provides a virtual power plant revenue allocation method based on generalized Nash bargaining in an electricity-carbon coupling market. Figure 1 As shown, the following steps are included:

[0065] S1. Establish a virtual power plant aggregation model.

[0066] like Figure 2 As shown in the figure, the virtual power plant aggregation model includes i adjustable loads and k distributed photovoltaics. The virtual power plant aggregates various adjustable loads and distributed photovoltaics to participate in the power peak-shaving market and the carbon market with the goal of maximizing profits, and interacts with the main grid at the same time. Specifically, the virtual power plant participates in the day-ahead peak-shaving by adjusting the adjustable load and abandoning light. The distributed photovoltaics of the virtual power plant obtain carbon quotas by applying for CCER. The power purchased by the virtual power plant from the power grid is subject to indirect carbon emission assessment. The excess carbon quota can be traded in the market and interact with the main grid at the same time, thereby realizing the optimal scheduling of the virtual power plant under the electricity-carbon coupling market.

[0067] The power utility of the i-th adjustable load in time period t The modeling is as follows:

[0068]

[0069] in, is the load amount of load i in time period t, and the load consumption power constraint is and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The maximum value of the corresponding quadratic function, P loadimin and P loadimax are the minimum load and the maximum load respectively.

[0070] Output of the kth distributed photovoltaic system in the scenario ω during time period t The constraints of photovoltaic output are obtained through prediction by scenario generation method as follows:

[0071]

[0072] Among them, P PVkmax The maximum photovoltaic output.

[0073] S2. With the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on the virtual power plant aggregation model and the benefit-cost factors.

[0074] The benefit-cost factors include load electricity utility, peak-shaving market benefits, grid interaction benefits, carbon market benefits, abandoned solar power costs and peak-shaving deviation penalties. The expression of the virtual power plant optimization scheduling model is as follows:

[0075] max P=R load +R F +P G +R cM -C qd -C ferror

[0076] Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the income of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, C ferror Represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows:

[0077] (1) Load power efficiency R load

[0078]

[0079] (2) The revenue of virtual power plants participating in the power peak load regulation market R F

[0080]

[0081] in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows:

[0082]

[0083] in, and are the minimum load and maximum load in period t respectively.

[0084] (3) The benefits of interaction between virtual power plants and power grids P G

[0085] P G =R G -C G

[0086] Among them, R G is the revenue from electricity sales of the virtual power plant, C G is the electricity purchase cost of the virtual power plant, and the calculation formulas are as follows:

[0087]

[0088] in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t.

[0089] (4) The benefits of virtual power plants participating in the carbon market R CM

[0090]

[0091] Among them, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t.

[0092] (5) Virtual power plant curtailment cost C qd

[0093]

[0094] Among them, λ p It is the cost price of abandoned power from distributed photovoltaic power generation.

[0095] (6) Virtual power plant peak load deviation penalty C ferror

[0096]

[0097] Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t, and the calculation formula is as follows:

[0098]

[0099] S3. Solve the virtual power plant optimization dispatch model to obtain the optimal dispatch plan of the virtual power plant in each time period. The optimal dispatch plan of the virtual power plant in each time period obtained in this step includes the load of each load in each time period, the output and abandoned light of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the income items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

[0100] In a preferred embodiment, the KKT condition can be used to solve the problem, and the following Lagrangian function is specifically constructed:

[0101]

[0102] in, and are the Lagrange coefficients of the lower and upper limits of load peak regulation, and are the Lagrangian coefficients of the lower and upper limits of photovoltaic peak regulation, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkmax It is the upper limit of photovoltaic peak regulation.

[0103] S4. According to the optimal solution obtained in step S3, with the goal of maximizing the generalized Nash bargaining utility function, a virtual power plant profit distribution model based on generalized Nash bargaining is constructed and solved to obtain the profit distribution plan of each aggregated entity of the virtual power plant.

[0104] The expression of the virtual power plant revenue distribution model based on generalized Nash bargaining is as follows:

[0105]

[0106] Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, and the specific expressions are as follows:

[0107] E PVk =ln(P PVkA -P PVks )

[0108] E loadi =ln(P loadiA -Ploadis )

[0109] Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadiS They are the profits of distributed photovoltaics and loads before they participate in virtual power plant aggregation.

[0110] Before the aggregation, the photovoltaic grid is connected, and the income of distributed photovoltaic k comes from the carbon quota obtained by grid connection and application of CCER certificates:

[0111]

[0112] Before the load i is aggregated, it does not meet the peak load access requirements. It only has the electricity utility, the cost of purchasing electricity from the grid, and the assessment by the carbon market:

[0113]

[0114] It is necessary to ensure that the benefits of each aggregation entity are higher after aggregation, so as to increase the enthusiasm of each entity to participate in the electricity-carbon coupling market transactions:

[0115] P PVkA ≥P PVkS

[0116] P loadiA ≥P loadis

[0117] Example 2

[0118] This embodiment provides an electronic device, including a memory and a processor, wherein the processor is used to execute a program stored in the memory, wherein the program includes a number of instructions and can execute all or part of the steps of the method described in Example 1. The memory includes a computer-readable storage medium, which can be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0119] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A virtual power plant revenue allocation method based on generalized Nash bargaining in an electricity-carbon coupling market, characterized in that: The following steps are involved: S1. Establishing a virtual power plant aggregation model, wherein the virtual power plant aggregation model includes multiple adjustable loads and multiple distributed photovoltaics; S2. With the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on the virtual power plant aggregation model and the benefit-cost factors, wherein the benefit-cost factors include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty; S3, solving the virtual power plant optimization scheduling model to obtain the optimal scheduling plan of the virtual power plant in each time period; S4. According to the optimal solution obtained in step S3, with the goal of maximizing the generalized Nash bargaining utility function, a virtual power plant profit distribution model based on generalized Nash bargaining is constructed and solved to obtain the profit distribution plan of each aggregated entity of the virtual power plant.

2. According to claim 1, a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market is characterized in that: In step S1, the power utility of the i-th adjustable load in time period t is The modeling is as follows: in, is the load amount of load i in time period t, and the load consumption power constraint is and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The corresponding maximum value of the quadratic function, P loadimin and P loadimax are the minimum load and the maximum load, respectively; Output of the kth distributed photovoltaic system in the scenario ω during time period t The constraints of photovoltaic output are obtained through prediction by scenario generation method as follows: Among them, P PVkmax The maximum photovoltaic output.

3. According to claim 1, a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market is characterized in that: In step S2, the expression of the virtual power plant optimization scheduling model is specifically as follows: maxP=R load +R F +P G +R CM -C qd -C ferror Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the income of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, C ferror It represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows: in, represents the electricity utility of the i-th load in period t; in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows: in, and are the minimum load and maximum load in period t respectively; P G =R G -C G Among them, R G is the revenue from electricity sales of the virtual power plant, C G The cost of purchasing electricity for the virtual power plant; Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t; Among them, λ p The cost price of abandoned power from distributed photovoltaic power generation; Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t.

4. According to claim 3, a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market is characterized in that: Virtual power plant electricity sales revenue R G and the virtual power plant electricity purchase cost C G The calculation formula is as follows: in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t.

5. According to claim 3, a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market is characterized in that: Baseline of the virtual power plant period t The calculation formula is as follows: Among them, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

6. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 1 is characterized in that: The optimal dispatching plan of the virtual power plant in each time period obtained in step S3 includes the load amount of each load in each time period, the output and abandoned light amount of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the income items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

7. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 1 is characterized in that: In step S4, the expression of the virtual power plant revenue distribution model based on generalized Nash bargaining is specifically as follows: Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, respectively.

8. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 7 is characterized in that: Utility function E of distributed photovoltaic k and load i in generalized Nash bargaining PVk and E loadi The specific expression is as follows: HAVE BEEN PVk =ln(P PVkA -P PVkS ) HAVE BEEN loadi =ln(P loadiA -P loadiS ) P PVkA ≥P PVkS P loadiA ≥P loadiS Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadiS They are the profits of distributed photovoltaics and loads before they participate in virtual power plant aggregation.

9. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 8 is characterized in that: Profit P of distributed photovoltaics before participating in virtual power plant aggregation PVkS The specific expression is as follows: Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, is the wholesale electricity price, λ CM is the carbon quota price, x a1 Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients. It represents the amount of abandoned light of distributed photovoltaic k in time period t.

10. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 8 is characterized in that: Profit P before load participation in virtual power plant aggregation loadiS The specific expression is as follows: in, represents the electricity utility of the i-th load in period t, is the load amount of load i in time period t, is the electricity price for tomorrow’s period t, λ CM is the carbon quota price, x a2 The carbon emission coefficient for purchasing electricity from the grid for the virtual power plant load.

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