Virtual power plant revenue allocation method based on generalized Nash bargaining in electricity-carbon coupling market

Abstract

The present invention relates to a virtual power plant revenue distribution method based on generalized Nash bargaining in an electricity-carbon coupling market, comprising the following steps: S1, establishing a virtual power plant aggregation model; S2, establishing a virtual power plant optimization scheduling model based on revenue-cost factors with the goal of maximizing the overall profit of the virtual power plant, wherein the revenue-cost factors include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, curtailment cost, and peak-shaving deviation penalty; S3, solving the virtual power plant optimization scheduling model; S4, based on the optimal solution obtained in step S3, constructing and solving a virtual power plant revenue distribution model based on generalized Nash bargaining with the goal of maximizing the generalized Nash bargaining utility function, and obtaining a revenue distribution plan for each aggregated entity of the virtual power plant. Compared with the existing technology, the present invention can achieve scientific scheduling of each distributed entity of the virtual power plant, while increasing the enthusiasm of each entity to participate in electricity-carbon coupling market transactions.

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 a distinct dual-peak electricity demand pattern, limited local power generation resources, a growing share of renewable energy, and abundant adjustable load resources. Virtual power plants, leveraging advanced communications and computing technologies, can aggregate heterogeneous resources such as multiple adjustable loads and distributed photovoltaics. This holds significant strategic significance in promoting the absorption of renewable energy, advancing energy transformation, and bolstering energy conservation and carbon reduction.

[0003] The "Detailed Implementation Rules for the Management of Electric Power Auxiliary Services in the East China Region" (hereinafter referred to as the "Detailed Rules") clearly stipulate that the grid-connected entities of the East China Electric Power Auxiliary Services include virtual power plants. The "Detailed Rules" state that the East China Electric Power Auxiliary Services are divided into basic ancillary services and paid ancillary services. Among them, basic ancillary services are provided by the grid-connected entities as an obligation and do not require compensation; paid peak-shaving in paid ancillary services includes deep peak-shaving compensation, start-up and shutdown peak-shaving compensation for large traditional power generation units, and adjustable load peak-shaving (valley-filling) compensation. Generally speaking, 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 both generate benefits. At the same time, the carbon market is also developing rapidly. Traditional power generation 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 subject to carbon market assessment 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 research. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a virtual power plant profit distribution method based on generalized Nash bargaining in the 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 benefit-cost factors, wherein the benefit-cost factors include load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, curtailment cost, and peak-shaving deviation penalty;

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

[0011] S4. Based on 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 aggregation entity of the virtual power plant.

[0012] Furthermore, in step S1, the electricity 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 absorption 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 on photovoltaic output are obtained through the scenario generation method prediction:

[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 Indicates the load power utility, R F P represents the profit of virtual power plant participating in the power peak load regulation market, 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 plants, 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 previous day 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, The specific expression of the peak load regulation 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 is the cost price of abandoned distributed photovoltaic power;

[0033]

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

[0035] Furthermore, the virtual power plant electricity sales revenue 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 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 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.

[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 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 participating in virtual power plant aggregation.

[0051] Furthermore, the profit P of distributed photovoltaics before participating in virtual power plant aggregation is 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 profit distribution method based on generalized Nash bargaining in the 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 benefit-cost factors such as load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty, 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 profit distribution model based on generalized Nash bargaining is constructed and solved to obtain the profit 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 benefits after aggregation than before aggregation, thereby increasing 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 system in different scenarios at different time periods through the scenario generation method. The power system can more accurately understand the power generation situation of distributed photovoltaic systems, 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 scheduling. DETAILED DESCRIPTION

[0062] The present invention is described in detail below with reference to 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 distribution method based on generalized Nash bargaining in the 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 Figure 1, the virtual power plant aggregation model includes i adjustable loads and k distributed photovoltaic units. The virtual power plant aggregates various adjustable loads and distributed photovoltaic units to maximize profits, participating in the power peaking and carbon markets while simultaneously interacting with the main grid. Specifically, the virtual power plant participates in day-ahead peaking by adjusting the amount of adjustable loads and curtailing solar power. The distributed photovoltaic units in the virtual power plant obtain carbon allowances by applying for CCERs. The power purchased from the grid by the virtual power plant is subject to indirect carbon emission assessments, and any excess carbon allowances can be traded in the market. The virtual power plant also interacts with the main grid, thus achieving optimal scheduling of the virtual power plant in the electricity-carbon coupled market.

[0067] The electricity 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 absorption 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 on photovoltaic output are obtained through the scenario generation method prediction:

[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 benefit-cost factors according to the virtual power plant aggregation model.

[0074] The benefit-cost factors include load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, curtailment cost, and peak-shaving deviation penalty. 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 Indicates the load power utility, R F P represents the profit of virtual power plant participating in the power peak load regulation market, 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 plants, C ferror represents the virtual power plant peak load deviation penalty. The specific calculation formulas for each part are as follows:

[0077] (1) Load power utility R load

[0078]

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

[0080]

[0081] in, It indicates the peak-shaving price for each period of the previous day 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, The specific expression of the peak load regulation 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) 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 during period t, is the net power of interaction between the virtual power plant and the grid during period t.

[0089] (4) The income 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 by the virtual power plant to the grid during period t.

[0092] (5) Cost of curtailed solar power in a virtual power plant C qd

[0093]

[0094] Among them, λ p The cost price of abandoned distributed photovoltaic power.

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

[0096]

[0097] Among them, λ pf Penalty price for virtual power plant deviation, 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 scheduling model to obtain the optimal scheduling plan for the virtual power plant in each time period. The optimal scheduling plan for the virtual power plant in each time period obtained in this step includes the load amount of each load in each time period, the output and curtailment amount of each photovoltaic unit in each time period, the interactive power between the virtual power plant and the 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 grid.

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

[0101]

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

[0103] S4. Based on 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 aggregation entity of the virtual power plant.

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

[0105]

[0106] Among them, E PVk and E loadi are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, and their 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 participating in virtual power plant aggregation.

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

[0111]

[0112] Before load i is aggregated, it does not meet the peak load access requirements. It only has electricity utility, electricity purchase cost from the grid, and is assessed 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 comprising a memory and a processor, wherein the processor is configured to execute a program stored in the memory, wherein the program comprises a number of instructions and can execute all or part of the steps of the method described in Example 1. The memory comprises a computer-readable storage medium, which can specifically be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0119] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. 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 distribution method based on generalized Nash bargaining in an electricity-carbon coupling market, characterized by: 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 benefit-cost factors, wherein the benefit-cost factors include load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, curtailment cost, and peak-shaving deviation penalty; S3. Solve the virtual power plant optimization scheduling model to obtain the optimal scheduling plan of the virtual power plant in each time period; S4. Based on the optimal solution obtained in step S3, 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 a revenue distribution plan for each aggregation entity of the virtual power plant; In step S1, adjustable load in the time period Electricity usage The modeling is as follows: in, For load In the period The load capacity, load absorption power constraint is , 、 and are the secondary power utility parameters of the load, and They are The minimum and maximum loads of the time period, for The corresponding maximum value of the quadratic function, and are the minimum load and the maximum load, respectively; No. Distributed photovoltaics in the period Scenario Output The constraints on photovoltaic output are obtained through the scenario generation method prediction: in, The maximum photovoltaic output.

2. 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 S2, the expression of the virtual power plant optimization scheduling model is as follows: in, represents the overall profit of the virtual power plant, Indicates the load power utility, represents the profit of virtual power plants participating in the power peaking market, represents the benefits of interaction between the virtual power plant and the grid, represents the benefits of virtual power plants participating in the carbon market, represents the cost of curtailment of virtual power plants, It represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows: in, Indicates the The load in Electricity usage by time period; in, It indicates the peak-shaving price for each period of the previous day issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. Represents distributed photovoltaic In the period The amount of abandoned light, Indicates load In the period The specific expression of the peak load regulation constraint of the virtual power plant aggregation subject is as follows: in, and They are The minimum and maximum loads of the time period; in, The revenue from electricity sales of the virtual power plant, the cost of purchasing electricity for the virtual power plant; in, For the scene The probability of occurrence, for Time period scene Next Distributed photovoltaic output, is the carbon quota price, Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients, The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, Virtual power plant period Selling power to the grid; in, is the cost price of abandoned distributed photovoltaic power; in, Penalty price for virtual power plant deviation, Virtual power plant period baseline.

3. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 2 is characterized in that: Virtual power plant electricity sales revenue and the cost of electricity purchased by virtual power plants The calculation formula is as follows: in, Tomorrow's time electricity prices, is the wholesale electricity price, and Virtual power plant period Power purchased from the grid and virtual power plant periods Selling power to the grid, For virtual power plants and grid periods Net interactive power.

4. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 2 is characterized in that: Virtual power plant period Baseline The calculation formula is as follows: in, For the scene The probability of occurrence, For the scene Distributed photovoltaic output, For load In the period The load amount.

5. 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.

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: In step S4, the expression of the virtual power plant profit distribution model based on generalized Nash bargaining is specifically as follows: in, and They are respectively distributed photovoltaic in generalized Nash bargaining and load The utility function of .

7. The virtual power plant revenue distribution method based on generalized Nash bargaining in the electricity-carbon coupling market according to claim 6 is characterized in that: Distributed photovoltaics in generalized Nash bargaining and load The utility function and The specific expression is as follows: in, and are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, and They are the profits of distributed photovoltaics and loads before participating in virtual power plant aggregation.

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: Profits of distributed photovoltaics before participating in virtual power plant aggregation The specific expression is as follows: in, For the scene The probability of occurrence, for Time period scene Next Distributed photovoltaic output, is the wholesale electricity price, is the carbon quota price, Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients, Represents distributed photovoltaic In the period The amount of discarded light.

9. 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: Profit before load participation in virtual power plant aggregation The specific expression is as follows: in, Indicates the The load in The electricity usage of the time period, For load In the period The load, Tomorrow's time electricity prices, is the carbon quota price, The carbon emission coefficient for purchasing electricity from the grid for the virtual power plant load.

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