Virtual power plant day-ahead-intra-day two-stage transaction optimization method considering electricity-carbon coupling

Abstract

The invention discloses a virtual power plant day-ahead-intra-day two-stage transaction optimization method considering electricity-carbon coupling. The method comprises the following steps: step 1, constructing a transaction model in which a virtual power plant participates in an electric power spot market, and setting constraint conditions; 2, constructing a transaction model of the virtual power plant participating in the carbon market; step 3, based on the transaction model of the virtual power plant participating in the electric power spot market obtained in the step 1 and the transaction model of the virtual power plant participating in the carbon market obtained in the step 2, establishing a day-ahead electricity-carbon market transaction model of the virtual power plant, an intra-day electricity-carbon market transaction model of the virtual power plant and constraint conditions thereof; and step 4, solving the virtual power plant day-ahead electricity-carbon market transaction model and the virtual power plant intra-day electricity-carbon market transaction model established in the step 3, and executing the virtual power plant day-ahead-intra-day two-stage electricity-carbon market transaction according to the solving result. According to the invention, the virtual power plant can better carry out collaborative transaction of power and carbon.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant trading strategy optimization methods, and specifically to a virtual power plant day-ahead and intraday two-stage trading optimization method considering electricity-carbon coupling. Background Art

[0002] As the world attaches great importance to environmental issues, global energy is moving towards cleaner development. The carbon market's ability to use market mechanisms to control and reduce greenhouse gases can help my country achieve efficient emission reductions and promote the realization of the "dual carbon goals."

[0003] In recent years, both the electricity and carbon markets have flourished. The rapid changes in the electricity market are primarily driven by the rapid development of renewable energy and technological advancements, which have profoundly transformed the entire power supply chain. Simultaneously, with the increasing global focus on reducing greenhouse gas emissions, the establishment and improvement of carbon markets have also been steadily advancing, providing new economic incentives for related industries. Although the electricity and carbon markets operate relatively independently, each following distinct market rules and transaction processes, they nonetheless maintain a close and complex interplay.

[0004] As an emerging market participant, virtual power plants play a crucial role in both electricity and carbon markets. By integrating decentralized energy resources (such as distributed generation, energy storage systems, and demand response mechanisms), virtual power plants enable flexible resource scheduling and optimal allocation. In electricity markets, virtual power plants can not only formulate reasonable trading strategies based on market price signals but also provide the necessary flexibility and support when power supply is tight. In carbon markets, virtual power plants can effectively manage their carbon emissions and gain additional economic benefits from participating in carbon trading. Therefore, rationally designing the trading mechanisms and strategies of virtual power plants can not only enhance their market competitiveness but also help achieve the coordinated optimization of the power system and the carbon market, promoting the achievement of sustainable development goals. Summary of the Invention

[0005] The present invention provides a two-stage day-ahead and intraday trading optimization method for virtual power plants considering electricity-carbon coupling, so as to solve the problem of unreasonable design of virtual power plant trading strategies in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The two-stage day-ahead and intraday trading optimization method for virtual power plants considering electricity-carbon coupling includes the following steps:

[0008] Step 1: With the participation of virtual power plants in day-ahead power market transactions as the core decision-making objective, a trading model for virtual power plants to participate in the power spot market is constructed; at the same time, constraints related to contract power decomposition and delivery are set as constraints for the trading model for the virtual power plants to participate in the power spot market;

[0009] Step 2: Based on the transaction process of virtual power plants participating in the carbon market, a transaction model for virtual power plants participating in the carbon market is constructed;

[0010] Step 3: Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and taking the optimization of the pre-dispatch cost of each aggregation unit of the virtual power plant, the day-ahead electricity market trading income, and the expected trading income of the end-of-day carbon market to maximize the profit of the virtual power plant as the objective function, a virtual power plant day-ahead electricity-carbon market trading model is established, and at the same time, the constraint conditions of the virtual power plant day-ahead electricity-carbon market trading model are constructed;

[0011] Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and with the objective function of optimizing the dispatching cost of each aggregation unit and the trading income of the end-of-day carbon market to maximize the profit of the virtual power plant, a virtual power plant intraday electricity-carbon market trading model is established, and at the same time, the constraint conditions of the virtual power plant intraday electricity-carbon market trading model are constructed;

[0012] Step 4: Solve the virtual power plant day-ahead electricity-carbon market trading model and the virtual power plant intraday electricity-carbon market trading model established in step 3, and execute the virtual power plant day-ahead and intraday electricity-carbon market transactions in two stages according to the solution results.

[0013] In a further step 1, based on the electricity purchase price in the electricity spot market, the electricity sales price in the electricity spot market, the electricity purchase amount of the virtual power plant in a unit time period, the electricity sales amount of the virtual power plant in a unit time period, the maximum electricity purchase amount of the virtual power plant in a unit time period, the maximum electricity sales amount of the virtual power plant in a unit time period, the actual delivery amount of the virtual power plant in the medium and long-term contract in period t, and the income of the virtual power plant participating in the electricity spot market transactions, a trading model for the virtual power plant participating in the electricity spot market is constructed.

[0014] In the further step 1, the peak-valley curve decomposition method is used to perform a refined electricity curve decomposition of the signed medium- and long-term electricity contracts, thereby setting the relevant constraints on the contract electricity decomposition and delivery.

[0015] In a further step 2, a trading model for the virtual power plant participating in the carbon market is constructed based on the allocation, trading and settlement processes included in a carbon emission rights trading compliance cycle in the trading process of the virtual power plant participating in the carbon market.

[0016] In the further step 3, the constraints of the virtual power plant day-ahead electricity-carbon market trading model include pre-dispatching constraints of carbon capture units in the virtual power plant, pre-dispatching constraints of interruptible loads in the virtual power plant, pre-dispatching constraints of energy storage batteries in the virtual power plant, carbon market pre-trading constraints, and energy balance constraints.

[0017] In the further step 3, the constraints of the intraday electricity-carbon market trading model of the virtual power plant include carbon capture unit pre-dispatching constraints, interruptible load pre-dispatching constraints, energy storage battery pre-dispatching constraints, carbon market pre-trading constraints, and energy balance constraints.

[0018] In the further step 4, the virtual power plant day-ahead electricity-carbon market trading model and the virtual power plant intraday electricity-carbon market trading model established in step 3 are solved by GAMS software.

[0019] The present invention optimizes the market participation strategy of virtual power plants by constructing a trading model for them to participate in the electricity spot market, improves the efficiency and benefits of electricity trading, and ensures that virtual power plants can flexibly respond to market changes. The present invention further enhances the competitiveness and market adaptability of virtual power plants in carbon emission trading by establishing a corresponding carbon market trading model. It not only focuses on the compliance of carbon trading, but also takes into account economic benefits and promotes sustainable development. Therefore, the present invention realizes the effective linkage of electricity and carbon markets by constructing a virtual power plant electricity-carbon joint trading model covering both the day-ahead and intraday stages, optimizes resource allocation, and enables virtual power plants to better conduct coordinated transactions of electricity and carbon, improve overall operational efficiency, and promote green and low-carbon development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and examples.

[0022] like Figure 1 As shown, this embodiment discloses a two-stage day-ahead and intraday trading optimization method for a virtual power plant considering electricity-carbon coupling, comprising the following steps:

[0023] Step 1: With the participation of virtual power plants in day-ahead electricity market transactions as the core decision-making goal, a trading model for virtual power plants to participate in the electricity spot market is constructed; at the same time, relevant constraints on contract electricity decomposition and delivery are set as constraint conditions for the trading model for the virtual power plants to participate in the electricity spot market.

[0024] Specifically in this embodiment, the electricity purchase price λ based on the electricity spot market t E2，B , electricity sales price in the electricity spot market λ tE2，S , the amount of electricity purchased by the virtual power plant in a unit period P t E2,B , the amount of electricity sold by the virtual power plant in a unit time period P t E2,S , the maximum amount of electricity purchased by the virtual power plant in a unit time period P E2,Bmax , the maximum electricity sales of the virtual power plant in a unit time period P t E2,Smax , and the actual delivery power P of the virtual power plant in the medium and long-term contract during period t t El , the income of virtual power plants participating in electricity spot market transactions R E2 , constructing a trading model for the virtual power plant to participate in the electricity spot market. The trading model for the virtual power plant to participate in the electricity spot market established thereby can effectively prevent the virtual power plant from maliciously manipulating the electricity spot market for profit. The trading model for the virtual power plant to participate in the electricity spot market established in this embodiment is shown in the following formula:

[0025]

[0026] 0≤P t E2,B ≤P E2,Bmax

[0027] 0≤P t E2,S ≤P E2,Smax

[0028] P t E2,S +P t E1 +L t -P t IL -P t SL +P t ESS,C =P t E2,B +P t W +P t G +P t ESS,D

[0029] Where: t represents the scheduling period; R E2 Represents the income of the virtual power plant participating in electricity spot market transactions; t E2，B represents the electricity purchase price in the electricity spot market; t E2，S Represents the electricity sales price in the electricity spot market; P tE2,B represents the amount of electricity purchased by the virtual power plant in a unit period; P t E2,S represents the electricity sales of the virtual power plant in a unit time period; P E2,Bmax represents the maximum amount of electricity purchased by the virtual power plant in a unit period; P t E2,Smax represents the maximum amount of electricity sold by the virtual power plant in a unit period; P t El represents the actual delivery power of the virtual power plant in the medium and long-term contract during period t; L t represents the power of the system load during period t; P t IL The actual power consumption of the interruptible load during period t; P t Sl represents the actual power consumption of the transferable load during period t; P t ESS,C Indicates the maximum charging power of the energy storage battery; P t W Indicates the output power of wind power in a certain period; P t G represents the power generation capacity of the carbon capture unit during period t; P t ESS,D Indicates the maximum discharge power of the energy storage battery.

[0030] In this embodiment, to ensure smooth connection between the medium- and long-term electricity market and the electricity spot market, a peak-valley curve decomposition method is also used to perform refined power curve decomposition on the signed medium- and long-term electricity contracts. This sets the relevant constraints on contract power decomposition and delivery, which serve as the constraints of the trading model for the virtual power plant to participate in the electricity spot market. The set relevant constraints on contract power decomposition and delivery are shown in the following formula:

[0031]

[0032] P t E1 ≥ρ t Q E1

[0033] Where: P t E1 represents the actual delivery power of the virtual power plant in the medium and long-term contract in period t, and the contract power is divided according to the peak, flat and valley decomposition method; ρ t is the contract electricity decomposition coefficient of period t; Q El Indicates the total actual delivered electricity volume of VPP medium and long-term contracts.

[0034] Step 2: Based on the allocation, trading, and settlement processes included in a carbon emission trading compliance cycle in the transaction process of virtual power plants participating in the carbon market, a transaction model for virtual power plants participating in the carbon market is constructed. The transaction model for virtual power plants participating in the carbon market is shown in the following formula:

[0035] R C2 =λ C1 E C2 -C Pen

[0036]

[0037] -χ B Q C1 ≤E C2 ≤χ S Q C1

[0038]

[0039] Where: R C2 represents the transaction income of virtual power plants in the carbon market; E C2 represents the carbon quota trading volume of virtual power plants in the carbon market; E VPP represents the actual carbon emissions of the virtual power plant during the contract fulfillment period; e G represents the carbon emission intensity of thermal power units; S , χ B They represent the maximum sales coefficient and purchase coefficient of the carbon quota of the virtual power plant, both of which are positive numbers; σ represents the average carbon emission factor of the regional power grid to which the virtual power plant belongs; C Pen k represents the penalty that the virtual power plant needs to pay for excess carbon emissions at the end of the compliance period; C is the penalty coefficient; C1 represents the carbon trading price; T represents the sum of time; Q Cl Represents carbon emission quota.

[0040] Step 3: Construct the virtual power plant day-ahead electricity-carbon market trading model, the virtual power plant intraday electricity-carbon market trading model, and their respective constraints, as follows:

[0041] (1) Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and with the objective function of optimizing the pre-dispatch cost of each aggregation unit of the virtual power plant, the day-ahead electricity market trading income, and the expected trading income of the end-of-day carbon market to maximize the profit of the virtual power plant, a virtual power plant day-ahead electricity-carbon market trading model is established. The virtual power plant day-ahead electricity-carbon market trading model is shown as follows:

[0042]

[0043] Where: t represents the time period; λ t E，S represents the electricity price in the electricity market during period t, λ t E，B It represents the electricity purchase price in the electricity market during period t; P t E,S 、P t E,B They represent the electricity sales and electricity purchases of the virtual power plant during period t; k 0 , k, k s Each represents the fixed cost, operating cost, and startup cost of the carbon capture unit; They represent the state variables of the carbon capture unit during period t, indicating whether the unit is running and started, and are 0-1 integer variables; P t G represents the power generation capacity of the carbon capture unit during period t; m IL,R represents the capacity cost of interruptible load used for backup, represents the dispatch cost required for the i-th level of interruptible load; represents the allowed interruptible load reserve capacity agreed upon between the virtual power plant and the electricity user during period t; represents the interruption power of the i-th level interruptible load during period t; m ESS Represents the loss cost of charging and discharging the energy storage battery, P t ESS,C 、P t ESS,D They represent the charging power and discharging power of the energy storage battery during period t respectively; represents the expected revenue of the virtual power plant participating in the carbon market. If it is a negative value, it represents the cost; f DA represents the total profit of the virtual power plant in the day-ahead electricity-carbon market transaction; n IL Represents the sum of interruptible loads at different levels; i represents the interruptible load at level i.

[0044] At the same time, this embodiment uses the pre-dispatch constraints of the carbon capture units in the virtual power plant, the pre-dispatch constraints of the interruptible loads in the virtual power plant, the pre-dispatch constraints of the energy storage batteries in the virtual power plant, the carbon market pre-trading constraints, and the energy balance constraints to construct the constraint conditions of the virtual power plant day-ahead electricity-carbon market trading model. The constraints are as follows:

[0045] a) The pre-dispatch constraint of the carbon capture unit is as follows:

[0046]

[0047] Where: P G,min Indicates the rated minimum power generation of the carbon capture unit, P G,maxrepresents the rated maximum power generation of the carbon capture unit; r G Indicates the maximum ramp power of the carbon capture unit; P t CCS represents the energy loss required for the operation of the carbon capture system during period t; θ represents the electrical power consumed by the carbon capture system to process unit CO2; Indicates the amount of CO2 regenerated in the regeneration tower during period t; represents the storage amount of CO2 in the rich liquid storage during period t; β represents the capture efficiency of the carbon capture system; represents the net carbon emissions of the carbon capture unit during period t; e represents the carbon emission intensity of the carbon capture unit; Represents the amount of CO2 absorbed in the absorption tower during period t; E CCS,2max represents the maximum CO2 regeneration amount that the carbon capture system can achieve per unit time; S CCS,max Represents the maximum CO2 storage capacity that can be achieved in the solution storage of the carbon capture system.

[0048] b) The interruptible load pre-dispatch constraint is as follows:

[0049]

[0050] Where: Represents the total interruption power of each level of interruptible load; L t represents the base load within the virtual power plant; ω represents the ratio of the interruptible load to the base load; P IL,max Indicates the maximum interruption power of the interruptible load in a continuous period of time; P s,t,n IL It represents the interrupting power of the nth level interruptible load; n represents the nth level interruptible load.

[0051] c) The energy storage battery pre-dispatch constraint is as follows:

[0052]

[0053] 0≤P t ESS,C ≤P ESS,Cmax

[0054] 0≤P t ESS,D ≤P ESS,Dmax

[0055]

[0056] Where: Indicates the storage capacity of the energy storage battery during period t; η ESS,C ,η ESS,DRespectively represent the charging efficiency and discharging efficiency of the energy storage battery; P ESS,Cmax 、P ESS,Dmax Respectively represent the maximum charging power and maximum discharging power of the energy storage battery; S ESS,max 、S ESS,min Each represents the upper and lower limits of the energy storage capacity of the energy storage battery; and It indicates the initial and final storage capacity of the energy storage battery.

[0057] d) The carbon market pre-trading constraints are as follows:

[0058] E C =Q VPP -E VPP

[0059]

[0060] Where: Q VPP Represents the carbon quota of the virtual power plant; E VPP represents the actual carbon emissions of the virtual power plant, and its value is expressed as the sum of the net carbon emissions of the carbon capture unit in one day; B G represents the carbon emission baseline value of the carbon capture unit; C represents the benchmark price of carbon quota on the trading day; l represents the interval length of carbon quota trading volume; α is the carbon quota price growth rate coefficient; E C Indicates the actual carbon quota trading volume.

[0061] e) Energy balance constraint is as follows:

[0062] P t E,S +L t -P t IL +P t ESS,C +P t CCS =P t E,B +P t W +P t G +P t ESS,D

[0063] Where: P t E,S It represents the electricity sales of the virtual power plant during period t; P t CCS represents the total energy loss of the carbon capture unit during period t; P t E,BIt represents the amount of electricity purchased by the virtual power plant during period t;

[0064] (2) Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and with the objective function of optimizing the dispatching cost of each aggregation unit and the trading income of the end-of-day carbon market to maximize the profit of the virtual power plant, a virtual power plant intraday electricity-carbon market trading model is established. The virtual power plant intraday electricity-carbon market trading model is shown in the following formula:

[0065]

[0066] Where: R C It represents the revenue of the virtual power plant from participating in carbon market transactions at the end of the day. If it is a negative value, it represents the cost; It represents the actual income of virtual power plants participating in electricity market transactions. They represent the actual electricity sales price and electricity purchase price in the electricity market during period t respectively; They represent the actual electricity sales and purchases of the virtual power plant during period t; f ID Represents the total profit of the virtual power plant's intraday electricity-carbon market transactions.

[0067] At the same time, this embodiment uses carbon capture unit pre-dispatching constraints, interruptible load pre-dispatching constraints, energy storage battery pre-dispatching constraints, carbon market pre-trading constraints, and energy balance constraints to construct the constraints of the virtual power plant's intraday electricity-carbon market trading model. Each constraint is consistent with the virtual power plant's day-ahead electricity-carbon market trading model.

[0068] Step 4: Use GAMS software to solve the virtual power plant day-ahead electricity-carbon market trading model and the virtual power plant intraday electricity-carbon market trading model established in step 3, and execute the virtual power plant day-ahead and intraday electricity-carbon market transactions in two stages based on the solution results.

[0069] The following uses a virtual power plant consisting of wind turbines, carbon capture equipment, energy storage batteries and interruptible loads as an example, and sets the electricity purchase price in the electricity market to 110% of the electricity sales price as the price cap; in the ladder-type carbon trading model, the benchmark price of carbon quotas is 100 yuan / ton, the length of each trading interval is 80 tons, and the price growth rate is set to 8%. The transaction optimization method of this embodiment is compared with another virtual power plant operation mode to illustrate the effect of the transaction optimization method of this embodiment.

[0070] In order to measure the impact of the virtual power plant with carbon capture units participating in the electricity-carbon market transaction on the overall operating income, two operation schemes are set as shown in Table 1:

[0071] Table 1 Two different virtual power plant operation modes

[0072]

[0073] In Table 1, Scheme 1 is that the virtual power plant only participates in electricity market transactions and does not participate in carbon market transactions. Scheme 2 is the transaction optimization method of this embodiment, that is, the virtual power plant participates in electricity market transactions and carbon market transactions at the same time.

[0074] The above two solutions are solved and the results are shown in Table 2:

[0075] Table 2: Virtual power plant benefits under different operating modes

[0076]

[0077] Table 2 details the profits of virtual power plants under different scenarios. Scenario 1 clearly shows that the virtual power plant incurred a fine of ¥83,766.90 from regulators for exceeding carbon emission standards. This was primarily due to the excessive carbon emission intensity of the virtual power plant's internal generators, exceeding the prescribed baseline, and the company's failure to promptly purchase sufficient additional carbon allowances from the carbon market to offset the shortfall. In contrast, Scenario 2, the trading optimization method used in this embodiment, adopted a more proactive strategy. The virtual power plant in this scenario actively participated in carbon market transactions, effectively replenishing its required carbon allowances. This not only reduced carbon emission costs but also further expanded its power generation potential. Furthermore, Scenario 2, the trading optimization method used in this embodiment, innovatively incorporated a carbon capture system into the virtual power plant's operations. While this reduced electricity market revenue to some extent, by utilizing a portion of the electricity generated in the carbon capture system's CO2 capture process, the virtual power plant's actual carbon emissions were effectively reduced, significantly increasing its carbon market revenue. Ultimately, this resulted in a ¥28,933.07 increase in total profit for the virtual power plant compared to Scenario 1. Therefore, the strategy of virtual power plants participating in both electricity and carbon markets not only reduces carbon emissions costs but also generates higher profits by expanding trading channels. This not only benefits the sustainable development of virtual power plants but also promotes the coordinated development of the power system and the carbon market, effectively avoiding high fines for excess carbon emissions.

[0078] The preferred embodiments of the present invention are described in detail above with reference to the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner unless there is any contradiction. Such combinations should also be regarded as the contents disclosed in this disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0079] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the scope of the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by those skilled in the art should fall within the scope of protection of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A two-stage trading optimization method for virtual power plants considering electricity-carbon coupling, characterized in that: The following steps are involved: Step 1: Taking the participation of virtual power plants in day-ahead power market transactions as the core decision-making goal, a trading model for virtual power plants to participate in the power spot market is constructed; at the same time, relevant constraints on contract power decomposition and delivery are set as constraint conditions for the trading model for the virtual power plants to participate in the power spot market; Step 2: Based on the transaction process of virtual power plants participating in the carbon market, a transaction model for virtual power plants to participate in the carbon market is constructed; Step 3: Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and taking the optimization of the pre-dispatch cost of each aggregation unit of the virtual power plant, the day-ahead electricity market trading income, and the expected trading income of the end-of-day carbon market to maximize the profit of the virtual power plant as the objective function, a virtual power plant day-ahead electricity-carbon market trading model is established, and at the same time, the constraint conditions of the virtual power plant day-ahead electricity-carbon market trading model are constructed; Based on the trading model of the virtual power plant participating in the electricity spot market obtained in step 1 and the trading model of the virtual power plant participating in the carbon market obtained in step 2, and taking the optimization of the dispatching cost of each aggregation unit and the trading income of the end-of-day carbon market to maximize the profit of the virtual power plant as the objective function, a virtual power plant intraday electricity-carbon market trading model is established, and at the same time, the constraint conditions of the virtual power plant intraday electricity-carbon market trading model are constructed; Step 4: Solve the virtual power plant day-ahead electricity-carbon market trading model and the virtual power plant intraday electricity-carbon market trading model established in step 3, and execute the virtual power plant day-ahead and intraday electricity-carbon market transactions in two stages according to the solution results.

2. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 1, based on the electricity purchase price in the electricity spot market, the electricity selling price in the electricity spot market, the electricity purchase amount of the virtual power plant in a unit time period, the electricity sales amount of the virtual power plant in a unit time period, the maximum electricity purchase amount of the virtual power plant in a unit time period, the maximum electricity sales amount of the virtual power plant in a unit time period, the actual delivery amount of the virtual power plant in the medium and long-term contract in time period t, and the income of the virtual power plant participating in the electricity spot market transactions, a trading model for the virtual power plant participating in the electricity spot market is constructed.

3. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 1, the peak-flat-valley curve decomposition method is used to perform a refined electricity curve decomposition of the signed medium- and long-term electricity contracts, thereby setting relevant constraints on the contract electricity decomposition and delivery.

4. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 2, according to the transaction process of the virtual power plant participating in the carbon market, a transaction model for the virtual power plant participating in the carbon market is constructed based on the allocation, transaction, and settlement processes included in a carbon emission rights trading compliance cycle.

5. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 3, the constraints of the virtual power plant day-ahead electricity-carbon market trading model include pre-dispatching constraints of carbon capture units in the virtual power plant, pre-dispatching constraints of interruptible loads in the virtual power plant, pre-dispatching constraints of energy storage batteries in the virtual power plant, carbon market pre-trading constraints, and energy balance constraints.

6. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 3, the constraints of the intraday electricity-carbon market trading model of the virtual power plant include carbon capture unit pre-dispatching constraints, interruptible load pre-dispatching constraints, energy storage battery pre-dispatching constraints, carbon market pre-trading constraints, and energy balance constraints.

7. The virtual power plant day-ahead and intraday two-stage transaction optimization method considering electricity-carbon coupling according to claim 1 is characterized in that: In step 4, the virtual power plant day-ahead electricity-carbon market trading model and the virtual power plant intraday electricity-carbon market trading model established in step 3 are solved using GAMS software.

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