Method and system for processing day-ahead and intra-day electricity-carbon collaborative transactions of productive consumer

Abstract

The invention discloses a method and a system for processing day-ahead and intra-day electricity-carbon collaborative transactions of productive dimists. The method comprises the following steps: establishing an electric energy transaction model between producers and consumer and an external carbon market transaction model, carrying out modeling analysis on the energy consumption cost of the producers and consumer and the income of an energy storage operator, constructing a day-ahead transaction model by adopting a double-layer optimization framework, and realizing optimal capacity distribution and price optimization by analyzing the income of the operator; based on the multi-energy-storage-operator shared operation model, an electricity and carbon sharing model between producers and consumer is constructed, and the electricity and carbon sharing model meets virtual capacity sharing constraints and electricity and carbon sharing balance constraints; in the intraday transaction, the uncertainty of power generation and power utilization of the producer and the consumer is considered, the net load deviation is processed based on an ultra-short-term predicted value, and the carbon emission generated in the intraday transaction is deducted by using the residual carbon quota in the day-ahead stage. According to the scheme, the resource allocation efficiency is improved, and the system electricity and carbon cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of electricity-carbon collaborative trading, and specifically relates to a method and system for processing day-ahead and intraday electricity-carbon collaborative trading among multiple producers and consumers. Background Technology

[0002] The distributed utilization of renewable energy is a crucial direction for promoting a clean and low-carbon energy transition. With the large-scale integration of distributed energy sources (such as solar and wind power), traditional electricity users are gradually transforming into prosumers who both consume and produce electricity. This increases the electrification level and energy diversity on the demand side, while also placing higher demands on the flexibility and low-carbon operation of the power system. As a major source of carbon emissions, the synergy between the power industry's market mechanisms and the carbon market has become a research hotspot. Energy storage technology, especially shared energy storage, as a key technology for improving energy efficiency, has been widely applied in scenarios such as energy communities and virtual power plants. Tesla's Connected Solutions project achieves unified management and economies of scale by aggregating distributed energy storage. These practices demonstrate that shared energy storage can effectively reduce users' dependence on the upstream power grid and improve the local consumption capacity of new energy sources. Existing technologies employ economic models (such as game theory and mechanism design) to analyze market incentives and efficiency, determining the strategies of demand-side resources in the market. However, traditional methods focus on long-term carbon market verification, neglecting the coupling potential driven by profit-driven factors among demand-side participants in the electricity carbon spot market, thus failing to create effective short-term incentives. Regarding carbon emission accounting, existing mechanisms primarily use cap-and-load settlement, which fails to reflect the fairness and differences in the regulatory behaviors of different users, leading to insufficient user participation. Although the joint operation of energy storage and prosumers has been proposed as an effective means to reduce grid dependence and promote local consumption, existing shared energy storage models mostly focus on energy sharing, lacking mechanisms linked to carbon quotas, thus failing to achieve deep integration into the electricity carbon market. Although traditional power generation companies still dominate the current carbon market trading, the expansion of the carbon market and the inclusion of new user-side operators (such as VPPs and load aggregators) will force the existing trading mechanism to suffer from problems such as insufficient timeliness. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a multi-producer-consumer day-ahead and intraday electricity carbon collaborative trading method and system. This addresses the technical problems of an imperfect demand-side flexible resource participation mechanism in the electricity carbon market, which leads to difficulties in the consumption of distributed renewable energy and the inability to fully realize carbon emission reduction potential; the lack of a day-ahead and intraday two-stage collaborative trading mechanism, which makes it impossible to respond in real time to uncertainties in power generation and consumption; and the failure of the carbon quota allocation and trading mechanism to effectively link with electricity trading, thus affecting the system's low-carbon operation efficiency.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution.

[0005] This invention first discloses a method for processing multi-producer-consumer day-ahead and intraday electricity-carbon co-trading, the method comprising the following steps: Step 1: Establish an electricity trading model between producers and consumers and an external carbon market trading model. Model and analyze the energy costs of producers and consumers and the revenue of energy storage operators. Use a two-layer optimization framework to build a day-ahead trading model. Analyze the revenue of operators to achieve optimal capacity allocation and price optimization. Step 2: Based on the multi-energy storage operator shared operation model, construct an electricity carbon sharing model among producers and consumers. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints. Step 3: In intraday trading, take into account the uncertainties in power generation and consumption by producers and consumers, process the net load deviation based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset the carbon emissions generated in intraday trading.

[0006] The present invention further includes the following preferred embodiments: The establishment of the electricity trading model between producers and consumers and the external carbon market trading model further includes: Electricity trading is prioritized among producers and consumers. When the supply of electricity within a producer or consumer exceeds demand, the producer or consumer obtains revenue from the electricity market by selling surplus electricity to other producers or consumers. When the supply of electricity within a producer or consumer falls short of demand, the producer or consumer purchases electricity from other producers or consumers or from the higher-level distribution network. Indirect carbon emissions are generated when prosumers purchase electricity from the grid. Prosumers also participate in external carbon markets to purchase additional carbon allowances.

[0007] The construction of the electricity carbon sharing model among producers and consumers further includes calculating: (1) (2) in, For producers and consumers i The cost of electricity and carbon. , Producers and consumers respectively i exist t The amount of electricity purchased and sold from the upper-level power grid at all times; T is the total dispatch cycle; For producers and consumers i The cost of sharing electricity carbon with other producers and consumers, For the interaction of electricity between producers and consumers, Carbon allowances for mutual benefit between producers and consumers , These are the electricity price and carbon price, respectively, used to mutually support producers and consumers. This indicates the capacity-sharing price for energy storage operators; Indicates producer-consumer i Leasing dynamic capacity space for energy storage; This represents the price of electricity purchased from the grid / the price of electricity sold to the grid. This indicates the price of carbon allowances traded in the external carbon market; Indicates producer-consumer i Total carbon emissions; For producers and consumers i Free carbon emission allowances.

[0008] The The calculation method is as follows: (3) in, This represents the average carbon emission coefficient of the upstream distribution network.

[0009] The virtual capacity sharing constraints include: (4) (5) (6) (7) in, They represent producers and consumers, respectively. i The charging power of energy storage and the discharging power of receiving energy storage. This indicates its charging and discharging power limit; Indicates producer-consumer i The interaction status with energy storage; This indicates the charging and discharging efficiency of energy storage, and the dynamic capacity allocated to each producer and consumer meets the overall capacity state constraints of the shared energy storage. It requires that the capacity at the beginning and end of the total dispatch cycle of energy storage operators be equal, where This indicates the total number of producers and consumers.

[0010] The carbon sharing balance constraint includes: (8) in, Indicates producer-consumer i The current day forecast value of internal renewable energy output. Showing off consumers i Daily forecast of internal electrical load; Producers and consumers i The carbon emission balance equation is (9) in For producers and consumers i Total carbon emissions, Provide them with free carbon emission allowances.

[0011] The method of using remaining carbon allowances from the day-ahead phase to offset carbon emissions generated during intraday trading further includes: If the multi-prosumer alliance has no carbon allowance surplus after the day's trading, the carbon emission costs generated during the day's trading will be borne by each prosumer. If, after the day-ahead transaction, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day's transaction can be fully offset from the alliance's total allowance, then it is determined that there are no carbon emission costs. If, after the day-ahead trading, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day-ahead trading cannot be fully offset from the alliance's total allowance, then the offset amount will be allocated based on the net load deviation.

[0012] This invention also discloses a multi-producer-consumer day-ahead and intraday electricity-carbon co-trading system utilizing the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method, comprising: The producer-consumer day-ahead trading module is used to establish electricity trading models between producers and consumers and external carbon market trading models. It models and analyzes the energy costs of producers and consumers and the revenue of energy storage operators. It uses a two-layer optimization framework to build the day-ahead trading model and achieves optimal capacity allocation and price optimization by analyzing the operator's revenue. The energy storage day-ahead sharing module is used to construct an electricity carbon sharing model among producers and consumers based on a multi-energy storage operator sharing operation model. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints. The intraday trading processing module is used to take into account the uncertainties of power generation and consumption by producers and consumers in intraday trading, process net load deviations based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset carbon emissions generated in intraday trading.

[0013] Accordingly, this application also discloses a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method.

[0014] Accordingly, this application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method.

[0015] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a multi-producer-consumer day-ahead and intraday electricity-carbon collaborative trading method and system, which reduces the dependence of producers and consumers on the upper-level distribution network, reduces fossil energy consumption and carbon emissions; enhances the local consumption capacity of distributed new energy through electricity-carbon sharing and energy storage complementarity; enhances the incentive effect of the carbon market through the day-ahead and intraday carbon quota linkage deduction mechanism; improves resource allocation efficiency and reduces the overall electricity-carbon cost of the system through a two-level optimization model; and enhances the power system's supply and demand balance capacity and operational stability. Attached Figure Description

[0016] Figure 1 This is a flowchart of the multi-producer-consumer day-ahead and intraday electricity-carbon collaborative trading processing method in this invention.

[0017] Figure 2 This is a schematic diagram of the multi-producer-consumer day-ahead and intraday electricity-carbon collaborative trading framework in this invention.

[0018] Figure 3 This is a schematic diagram of the multi-energy storage operator capacity sharing operation mode in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] The embodiments described in this application are merely some, not all, embodiments of the present invention. Based on the spirit of the present invention, other embodiments obtained by those skilled in the art without inventive effort are all within the protection scope of the present invention.

[0021] To address the shortcomings of existing technologies, this invention proposes a multi-producer-consumer day-ahead and intraday electricity carbon collaborative trading method and system. It adopts a two-stage multi-producer-consumer day-ahead and intraday electricity carbon collaborative trading model under a flexible resource capacity sharing framework. The overall strategy is as follows: Figure 1 (Trading strategy diagram) As shown, the core framework is as follows: Figure 2 (Transaction framework diagram) and a diagram of the multi-energy storage operator capacity sharing operation model are shown below. Figure 3 As shown.

[0022] The multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method disclosed in this invention includes the following steps: Step 1: Establish electricity trading models between producers and consumers and external carbon market trading models. Model and analyze the energy costs of producers and consumers and the revenue of energy storage operators. Use a two-layer optimization framework to construct a day-ahead trading model. Analyze the revenue of operators to achieve optimal capacity allocation and price optimization.

[0023] The first stage is the day-ahead market. Firstly, based on producer and consumer power generation and consumption forecasts, a distribution network thermal power generation plan and a shared energy storage dispatch plan are formulated according to the principle of minimizing electricity costs and maximizing shared energy storage revenue, as shown in equations (1)-(9). For example... Figure 2 As shown in the front-day market section on the left, the input for this stage is forecast data, and the output is the trading plan for electricity and carbon allowances.

[0024] Electricity trading is prioritized between producers and consumers. When there is an internal electricity surplus, it is sold to other producers and consumers for profit; when there is a shortage, priority is given to purchasing from other producers and consumers, with the remaining shortfall supplemented by the upstream distribution network. This electricity reconciliation process is as follows: Figure 1 P2P transactions among middle-class consumers and Figure 2 The diagram illustrates the energy interaction within a producer-consumer cluster. When the energy supply exceeds demand within a producer-consumer group, the surplus energy is sold to other producers-consumers to generate revenue from the electricity market. When the energy supply falls short of demand, producers-consumers purchase energy from other producers-consumers or from the upstream distribution network to meet their own energy needs.

[0025] Prosumers incur indirect carbon emissions when purchasing electricity from the grid. To avoid hefty penalties for exceeding carbon emission limits, prosumers must participate in external carbon markets to purchase additional carbon allowances. This process is as follows: Figure 2 The connection between the "upper-level distribution network" and the "external carbon market" is shown in the diagram.

[0026] A two-layer optimization framework is used to construct the day-ahead trading model, as shown in equations (1)-(10). The upper layer is a shared energy storage operator decision model, which aims to maximize revenue and optimize the capacity sharing price; the lower layer is a multi-prosumer alliance decision model, which aims to minimize the carbon cost of electricity and optimize electricity consumption behavior and energy storage leasing strategy. Figure 3 The structure and information interaction of this two-layer optimization framework are demonstrated.

[0027] Step 2: Based on the shared operation model of multiple energy storage operators, construct an electricity carbon sharing model among producers and consumers. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints.

[0028] All producers and consumers within the energy storage sharing system are interconnected via bidirectional power and communication links, and the entire system is connected to the upper-level public power grid through a single grid connection point.

[0029] Surplus electricity will be stored in leased shared energy storage, while shortages will be met by releasing energy stored in the leased storage, thereby reducing carbon emissions. Shared storage reduces dependence on the upper-level grid, reduces carbon emissions, and achieves supply and demand matching and local consumption in the vicinity.

[0030] like Figure 3As shown, producers and consumers lease virtual capacity space for energy storage. Surplus electricity is stored in the storage, while shortages are supplemented by the storage's discharge. The carbon emission reductions generated by this model create a carbon allowance surplus, which can be used for intraday phase deductions. This linkage exists in… Figure 2 The "carbon allowance" arrow points from the day-ahead market to the intraday market.

[0031] A producer-consumer carbon cost model (Equations 1-3), virtual capacity sharing constraints (Equations 4-7), and carbon sharing balance constraints (Equations 8-9) are established. These models describe... Figure 2 The economic relationships and physical constraints within and between the middle-class consumer cluster and external markets.

[0032] (1) (2) in, For producers and consumers i The cost of electricity and carbon. , Producers and consumers respectively i exist t The amount of electricity purchased and sold from the upper-level power grid at all times; T is the total dispatch cycle; For producers and consumers i The cost of sharing electricity carbon with other producers and consumers, Indicates producer-consumer i Provide carbon sharing services. For producers and consumers i Accept carbon sharing services. For the interaction of electricity between producers and consumers, Indicates producer-consumer i It provides electricity services to other producers and consumers, and conversely, is a recipient of electricity services. Carbon allowances for mutual benefit between producers and consumers Indicates producer-consumer i It provides surplus carbon allowances to other producers and consumers, and in turn, is a recipient of carbon emission allowances. , These are electricity prices and carbon prices, which are mutually beneficial between producers and consumers. This indicates the capacity-sharing price for energy storage operators. Indicates producer-consumer i Renting dynamic capacity space for energy storage. This represents the price of electricity purchased from the grid / the price of electricity sold to the grid. This indicates the price of carbon allowances traded in the external carbon market. Indicates producer-consumer i The total carbon emissions are related to the amount of electricity purchased from the upstream distribution network. For producers and consumers i Free carbon emission allowances.

[0033] (3) in, This represents the average carbon emission coefficient of the upstream distribution network.

[0034] (4) (5) (6) (7) in, They represent producers and consumers, respectively. i The charging power of energy storage and the discharging power of receiving energy storage. This indicates its charging and discharging power limit. Indicates producer-consumer i The interaction with energy storage This indicates that the producer-consumer charges the energy storage, and conversely, the producer-consumer receives energy from the energy storage and discharges it. This represents the charging and discharging efficiency of energy storage. Equation (6) reflects the dynamic allocation of shared energy storage capacity, with the dynamic capacity allocated to each producer and consumer satisfying the overall capacity state constraint of the shared energy storage. Furthermore, to ensure the sustainable operation of the shared energy storage capacity leasing model, the capacity at the beginning and end of the total scheduling cycle of the energy storage operator must be equal, as shown in equation (7), where... This indicates the total number of producers and consumers.

[0035] (8) in, Indicates producer-consumer i The current day forecast value of internal renewable energy output. Showing off consumers i Daily forecast of internal electrical load.

[0036] (9) The above formula represents the producer-consumer relationship. i The carbon emission balance equation, in which For producers and consumers i Total carbon emissions, Provide them with free carbon emission allowances.

[0037] Establish a revenue model for energy storage operators (Formula 10-11) and a capacity-sharing price constraint. This model corresponds to... Figure 3 Revenue mechanism for energy storage operators as service providers.

[0038] (10) (11) in, This indicates the revenue of energy storage operators.

[0039] Step 3: In intraday trading, take into account the uncertainties in power generation and consumption by producers and consumers, process the net load deviation based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset the carbon emissions generated in intraday trading.

[0040] The second phase is the intraday market. For example... Figure 2 As shown in the "Intraday Market" section on the right, considering forecast bias and supply-demand changes, net load deviation is processed based on ultra-short-term forecasts. To achieve the supply guarantee target, the electricity shortage of producers and consumers is directly supplemented by the upstream distribution network. The carbon emissions generated in this process are offset using the remaining carbon allowances from the day-ahead phase through energy storage and other emission reduction methods, specifically in three scenarios: If the multi-prosumer alliance has no carbon allowance surplus after the day's trading, the carbon emission costs generated during the day's trading will be borne by each prosumer. If, after the day-ahead transaction, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day's transaction can be fully offset from the alliance's total allowance, then it is determined that there are no carbon emission costs. If, after the day-ahead trading, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day-ahead trading cannot be fully offset from the alliance's total allowance, then the offset amount will be allocated based on the net load deviation.

[0041] Figure 2 The arrow indicating "carbon allowance" points from the day-to-day indicator to the day-to-day indicator, marked with "deduction," reflecting this core linkage mechanism. The carbon costs for electricity generated by each producer and consumer during the day-to-day phase include the cost of purchasing electricity from the upstream distribution network, carbon emission costs, and the cost of penalties for curtailing electricity.

[0042] The beneficial effects of this invention are as follows: Compared with the prior art, this invention provides a multi-producer-consumer day-ahead and intraday electricity-carbon collaborative trading method and system, which reduces the dependence of producers and consumers on the upper-level distribution network, reduces fossil energy consumption and carbon emissions; enhances the local consumption capacity of distributed new energy through electricity-carbon sharing and energy storage complementarity; enhances the incentive effect of the carbon market through the day-ahead and intraday carbon quota linkage deduction mechanism; improves resource allocation efficiency and reduces the overall electricity-carbon cost of the system through a two-level optimization model; and enhances the power system's supply and demand balance capacity and operational stability.

[0043] This invention can be a system, method, and / or computer program product. This invention also discloses a multi-producer-consumer day-ahead and intraday electricity-carbon co-trading system based on the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method, comprising: The producer-consumer day-ahead trading module is used to establish electricity trading models between producers and consumers and external carbon market trading models. It models and analyzes the energy costs of producers and consumers and the revenue of energy storage operators. It uses a two-layer optimization framework to build the day-ahead trading model and achieves optimal capacity allocation and price optimization by analyzing the operator's revenue. The energy storage day-ahead sharing module is used to construct an electricity carbon sharing model among producers and consumers based on a multi-energy storage operator sharing operation model. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints. The intraday trading processing module is used to take into account the uncertainties of power generation and consumption by producers and consumers in intraday trading, process net load deviations based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset carbon emissions generated in intraday trading.

[0044] Based on the spirit of this invention, those skilled in the art will readily conceive of a computer program product that can be obtained based on the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded to enable a processor to implement various aspects of this disclosure. That is, this application also includes a terminal comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps according to the aforementioned multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method.

[0045] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0046] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0047] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for co-trading carbon electricity and carbon from multiple producers and consumers both day-ahead and intraday, characterized in that, Includes the following steps: Step 1: Establish an electricity trading model between producers and consumers and an external carbon market trading model. Model and analyze the energy costs of producers and consumers and the revenue of energy storage operators. Use a two-layer optimization framework to build a day-ahead trading model. Analyze the revenue of operators to achieve optimal capacity allocation and price optimization. Step 2: Based on the multi-energy storage operator shared operation model, construct an electricity carbon sharing model among producers and consumers. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints. Step 3: In intraday trading, take into account the uncertainties in power generation and consumption by producers and consumers, process the net load deviation based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset the carbon emissions generated in intraday trading.

2. The method for processing multi-producer-consumer day-ahead and intraday electricity-carbon co-trading according to claim 1, characterized in that, The establishment of the electricity trading model between producers and consumers and the external carbon market trading model further includes: Electricity trading is prioritized among producers and consumers. When the supply of electricity within a producer or consumer exceeds demand, the producer or consumer obtains revenue from the electricity market by selling surplus electricity to other producers or consumers. When the supply of electricity within a producer or consumer falls short of demand, the producer or consumer purchases electricity from other producers or consumers or from the higher-level distribution network. Indirect carbon emissions are generated when prosumers purchase electricity from the grid. Prosumers also participate in external carbon markets to purchase additional carbon allowances.

3. The method for processing multi-producer-consumer day-ahead and intraday electricity-carbon co-trading according to claim 2, characterized in that, The construction of the electricity carbon sharing model among producers and consumers further includes calculating: （1） （2） in, For producers and consumers i The cost of electricity and carbon. , Producers and consumers respectively i exist t The amount of electricity purchased and sold from the upper-level power grid at all times; T is the total dispatch cycle; For producers and consumers i The cost of sharing electricity carbon with other producers and consumers, For the interaction of electricity between producers and consumers, Carbon allowances for mutual benefit between producers and consumers , These are the electricity price and carbon price, respectively, used to mutually support producers and consumers. This indicates the capacity-sharing price for energy storage operators; Indicates producer-consumer i Leasing dynamic capacity space for energy storage; This represents the price of electricity purchased from the grid / the price of electricity sold to the grid. This indicates the price of carbon allowances traded in the external carbon market; Indicates producer-consumer i Total carbon emissions; For producers and consumers i Free carbon emission allowances.

4. The multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method according to claim 3, characterized in that, The The calculation method is as follows: （3） in, This represents the average carbon emission coefficient of the upstream distribution network.

5. The multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method according to claim 4, characterized in that, The virtual capacity sharing constraints include: （4） （5） （6） （7） in, They represent producers and consumers, respectively. i The charging power of energy storage and the discharging power of receiving energy storage. This indicates its charging and discharging power limit; Indicates producer-consumer i The interaction status with energy storage; This indicates the charging and discharging efficiency of energy storage, and the dynamic capacity allocated to each producer and consumer meets the overall capacity state constraints of the shared energy storage. It requires that the capacity at the beginning and end of the total dispatch cycle of energy storage operators be equal, where This indicates the total number of producers and consumers.

6. The multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method according to claim 5, characterized in that, The carbon sharing balance constraint includes: （8） in, Indicates producer-consumer i The current day forecast value of internal renewable energy output. Showing off consumers i Daily forecast of internal electrical load; Producers and consumers i The carbon emission balance equation is （9） in For producers and consumers i Total carbon emissions, Provide them with free carbon emission allowances.

7. The method for processing multi-producer-consumer day-ahead and intraday electricity-carbon co-trading according to claim 6, characterized in that, The method of using remaining carbon allowances from the day-ahead phase to offset carbon emissions generated during intraday trading further includes: If the multi-prosumer alliance has no carbon allowance surplus after the day's trading, the carbon emission costs generated during the day's trading will be borne by each prosumer. If, after the day-ahead transaction, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day's transaction can be fully offset from the alliance's total allowance, then it is determined that there are no carbon emission costs. If, after the day-ahead trading, the multi-prosumer alliance has a carbon allowance surplus, and the carbon emission costs generated during the day-ahead trading cannot be fully offset from the alliance's total allowance, then the offset amount will be allocated based on the net load deviation.

8. A multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing system, characterized in that, include: The producer-consumer day-ahead trading module is used to establish electricity trading models between producers and consumers and external carbon market trading models. It models and analyzes the energy costs of producers and consumers and the revenue of energy storage operators. It uses a two-layer optimization framework to build the day-ahead trading model and achieves optimal capacity allocation and price optimization by analyzing the operator's revenue. The energy storage day-ahead sharing module is used to construct an electricity carbon sharing model among producers and consumers based on a multi-energy storage operator sharing operation model. The electricity carbon sharing model satisfies virtual capacity sharing constraints and electricity carbon sharing balance constraints. The intraday trading processing module is used to take into account the uncertainties of power generation and consumption by producers and consumers in intraday trading, process net load deviations based on ultra-short-term forecasts, and use the remaining carbon allowances in the day-ahead phase to offset carbon emissions generated in intraday trading.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the multi-producer-consumer day-ahead and intraday electricity-carbon co-trading processing method as described in any one of claims 1-7.

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